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text:
TBT: The Princess Bride (1987)
By Sarah Lorraine on January 12, 2017 in Medieval
Normally we don't cover fantasy films here on Frock Flicks, largely because the "fantasy" element in costuming goes so far in the opposite direction from history that it is basically impossible to compare the two (i.e. Why we haven't talked about Game of Thrones in any depth, so stop pestering us about it). However, every so often there is a film that is definitely a fantasy but is grounded in a particular time and place that either is historical or is closely based off of a historical period. One such film is The Princess Bride (1987).
Adapted from William Goldman's "abridged" novel, The Princess Bride tells the epic love story between the most beautiful woman in the world and her "poor, but perfect" farm boy. The film does a pretty good job of getting the main plot points, but the book… Oh, just read it. If nothing else, it will make the entire dueling scene atop the Cliffs of Insanity between Inigo and Westley make sense. That, alone, is a pretty good reason to read it.
And yes. It is a kissing book.
True story: 10-year old me actually asked my mom if it was a "kissing movie" when it first hit the theaters.
For those three of you who haven't seen the film…
It takes place in the country of Florin, which bears more than a passing resemblance to a prototypical 15th-century European country. There are parallels to be drawn between Florin and its rival country across the channel, Guilder, with that of England and France during the period of inter-kingdom conflict known as the Hundred Years' War, but one shouldn't get hung up on the details. This is a fairy tale, after all. That said, there are no fairy godmothers (though there is a crotchety Miracle Man that sort of fits that role) and the only magic is True Love, which does, indeed, conquer all — even death. Even the terrifying Fire Swamp and its R.O.U.S. (Rodents of Unusual Size) inhabitants are improbable, but not necessarily unrealistic. The evil in The Princess Bride is not cast in black magic, but in all-too-real human desires for power and domination.
We are going to skip the peasant outfits and focus on the flashy stuff, because that's how we roll.
Costume designer Phyllis Dalton, whose screen credits include Lawrence of Arabia (1962) , Much Ado About Nothing (1993), and Henry V (1990) among a host of other extremely well-known and beloved historical films, took her cue from this slightly skewed realism and remained fairly faithful to the silhouette of the mid-15th-century fashion popular in France and England.
The first glimpse we have of the heroine Buttercup (Robin Wright) after she has been plucked from peasant obscurity to become the fiancée of the scheming Prince Humperdinck (a nice, sardonic twist on the rags-to-riches trope), she is in a flowing red houppelande. Her hair is down, but she is wearing a pearled cap (at least until she's kidnapped and manages to lose it in the fray). We can debate until the cows come home as to how historically accurate having her hair down would be for a woman of her age/station — IIRC, she's 18 when she goes to Florin City to marry Humperdinck, but allegedly a virgin, so the standard rule of young girls and unmarried young women of upper classes wearing one's hair down can apply.
Buttercup's iconic red gown, before it goes through hell and back for True Love.
Left: Portrait of Lysbeth van Duvenvoorde, anonymous, c. 1430; Right: Detail from "April", Très Riches Heures, Limbourg brothers, c. 1412-1416.
Not to digress, but I'm 99.9% sure that Buttercup's red houppe was worn by Amy Poehler in the 2013 Parks & Recreation episode "Recall Vote."
Same cartridge pleating on the cuffs.
Detail showing the back lacing and the visible side back seam seam placement on both gowns.
Buttercup wears a few different high-waisted gowns that evoke a more Italianate feeling, such as her wedding gown, but then switches it up in the middle of the film with a fitted kirtle and tippets. The kirtle was a hold-over from the previous century that continued in various permutations right up until the first decades of the 16th century, so it's plausibly placed within the loosely defined 15th-century-ish period of the film.
Buttercup's wedding gown, flanked by the outfits for Inigo Montoya (left) and Westley's Dread Pirate Roberts getup (right). Via Camknows.
This gown has a similar cut as her wedding gown, including the hanging split sleeves. She wears this dress twice in two different dream sequences.
One of the "fitted" kirtles. Obviously, this relies on the modern shaping of princess seams from the shoulder over the bust — so far as we know, this is not seen in any gowns before the 19th century. But you get the gist.
So freaking hard to find a clear image of this gown online, and it's my favorite one of them all… But it's the same pattern as the pink one above, with contrasting "tippets" sewn in at the bicep.
Another detail from "April" showing a fitted kirtle with tippets. Très Riches Heures, Limbourg brothers, c. 1412-1416.
So what of the menswear? Setting aside the roguish characters of Inigo Montoya, Fezzick, and Vizzini who all wear pretty generic tunics and trousers with knee high boots, and the all-black pirate ensemble Westley spends most of the film wearing, that leaves us with the two bad guys, Prince Humperdinck (Chris Sarandon) and Count Rugen (played by a disturbingly attractive Christopher Guest).
That crown just screams "DOUCHE BAG."
I am a little ashamed that it took me well over a decade to realize this was Christopher Guest.
Like Buttercup's costumes, both Humperdinck and Rugen's outfits mostly adhere to the 1430s-1440s. Both wear knee-length houppelandes and knee-high boots. Historically, of course, the boots would only be worn for outdoor activities, but given that they are "men of action," I'll let it go this time. Also, both never wear appropriate headgear of any sort (unless it's Humperdinck's massive crown), but again, so very few films ever get this detail remotely correct that I've stopped giving a damn about it.
Portrait of Charles the Bold, Duke of Burgundy. After Rogier van der Weyden, c. 1440s.
Detail of Presentation of Hainaut Chronicles to Philip the Good, attr. Rogier van der Weyden, 1448.
Humperdinck wears a blue damask houppe that coordinates wtih Buttercup's wedding gown. It's a good color on him, honestly.
All told, the historical reference points are solid and fairly straight-forward, adhering to a definitive location (Western Europe) and time (mid-15th century). There are fantasy embellishments, but they are relatively innocuous and mainly serve to remind us that this is a fairy tale, after all. And honestly, The Princess Bride gets far more right than it gets wrong in terms of costuming, which makes it stand apart from other historical films.
So, kick back with a mutton, lettuce, and tomato sandwich and…
What do you think about the costumes in The Princess Bride?
Tagsadapted from booksphyllis daltonplaying fast & loose with historypretty boyspretty pretty princessesrecycled movie costumesThe Princess Bride (1987)
The Crown: "Smoke and Mirrors", The Coronation of Queen Elizabeth II
Who's the Best Queen Victoria on Screen?
Sarah Lorraine
Sarah has an undergraduate degree in Clothing & Textile Design and a Master's in Art History and Visual Culture, with an emphasis on fashion history. When she's not caught in paralyzing existential dread, she's drinking craft cocktails and writing about historical costume in film and television. She's been pissing people off on the internet since 1995.
wildemoose January 12th, 2017
As both a Parks and Rec and Princess Bride fan, it gives me so much joy to know that Leslie Knope was wearing the actual dress from the movie.
MoHub January 12th, 2017
When Princes Bride first came out, I was working—in custom picture framing—with a costume designer/nerd who had a designer/nerd fiancée. All three of us were in awe of the magnificent costumes in the film.
In fact, they had both worked on costumes for an opera company in which I sang, and we had many discussions about the construction and accuracy of what went on the stage. Unfortunately, most of what we saw/wore was woefully wrong, so our discussions of the film's costuming—as you might expect—involved a lot of envy and gushing.
Sharon Rose, LAc, MSAOM January 12th, 2017
I'm with Wildemoose!!!
Kathleen Norvell January 12th, 2017
I just rewatched this and was blown away by the costumes. For a fantasy film, the costumers did a great job. I was especially impressed by the houppelandes. I mean, who does that? Even the costumes for The Hollow Crown weren't as good.
On the other hand, I believe this portrait of Agnes Sorel, mistress of Charles VII of France, as the Virgin Mary shows a princess seam on the front of her gown. It's faint so you really have to look carefully.
https://upload.wikimedia.org/wikipedia/commons/e/e7/Fouquet_Madonna.jpg
https://upload.wikimedia.org/wikipedia/commons/4/42/Melun-diptychon-detail.gif
Sarah Lorraine January 13th, 2017
Ugh. The Melun madonna is such a problem with the whole princess seam debate. It is *literally* the only true "princess seam" that shows up pre-19th century anywhere. (Let's be clear: I do not consider the Greenland gowns to be princess seams and I will fight anyone who says differently to the death pain.) I don't like using a single outlying instance as "proof" of a construction technique, especially when that one example is in a religious portrait.
It's also an ugly-ass painting. Fouquet was tripping on some bad shit.
Alys Mackyntoich January 14th, 2017
I don't know what's creepier about the painting: the dead-eyed sociopath of a baby Jesus or the fact that Fouquet apparently had never seen an actual human breast.
themodernmantuamaker January 16th, 2017
I often wonder if any of the painters of the past (great and not so great) ever paid attention to what a real breast looked like. I'm constantly bewildered by incredibly beautiful and masterfully executed works with weird-shaped or otherwise unrealistic portrayals of breasts, it can be (sadly) quite jarring and off-putting.
Anyway, this seam is a tricky one, I've mused over it myself. As you say, Sarah, it seems to be the *only* one during this time period. But I wonder if it can be dismissed entirely just because it is, technically, a proper princess seam. The line and location are correct and it's doing what a princess seam does – fit close to the body. This would seem to suggest that the technique was known and understood. Yet, with it being so singular, especially within the overall oeuvre of Northern Ren painting with it's intense level of just such details, it seems obvious that it was not part of the repertoire for actual garment construction in that time/place. I have not answer for this, so maybe my comment is pointless, lol.
If you cut a gown to the "Greenland" gown pattern (not the packaged one, which is good, but copy the pieces of the real one), you get a line very much like a princess seam. I was surprised when I saw a reproduction someone had made (before the commercial pattern came out) and it was very princess seam-like. I think this is where the conventional SCA wisdom comes from about princess seams.
I am an SCAer, and I've got a bit of an ax to grind on this issue. The Greenland gowns look nothing like princess seams. Yes, they are seamed and flared like a princess seam gown is, but put all the pattern pieces flat and it's a totally different shape. We modern reenactors tend to make the Greenland gowns way tighter than they were in reality as well, which gives the same effect as a princess seamed gown, but it's historically inaccurate for that particular extant example.
I lean more toward the "fitted kirtle" method put forth by Robin Netherton and perfected by Tasha Kelly and Charlotte Johnson of a rectangularly constructed garment that's been fitted to the torso as closely as possible to achieve the look of a body-hugging flared dress.
I had an Art professor who used to tell us if we wanted to reproduce a Michelangelo female nude, all we had to do was paint a male nude and add a half grapefruit to form each breast.
That's funny, but so true. I never saw such a bunch of muscular female nudes as the ones Michelangelo painted. They really do look like males with breasts.
I think we may have had the same Art professor ;o)
Liutgard January 12th, 2017
I don't like the back of the red houppelande- there's no basis for that that I know of, and frankly, once you remove the belt, you can easily slip it on and off. No need for a laced back opening.
Or am I being too picky?
With the laced back, I think they missed the whole concept of the construction…
NO, you're not being picky. I forgot about that. There is no practical reason for lacings in a houppelande.
For a costume the lacings are actually pretty practical.
Historically accurate? Nope. But it makes sense if you use modern construction techniques, which was what was done on all of the costumes in this film (princess seams, I'm looking at you).
Charity January 12th, 2017
The blue gown is also my favorite. I literally gasped when I first saw it. As a child. And then I thought, "If I ever get married (doubt it) I want a wedding dress that looks something like one of these dresses!"
Still waiting on the getting married thing, mostly because I don't care; and I've cheated emotionally on Buttercup's gown in my future wedding dreams, but… so pretty.
Susan Pola January 12th, 2017
The Princess Bride is a favourite of mine. Besides the story, I did note that Humperdinck and Rugen were in houpplandes. So right for the best dress man in 1440-1470. Buttercup's gowns were gorgeous.
And who can forget:
1) My name is Inigo Montoya.You killed my father. Prepare to die.
2)…As you …wish…
3) Mostly dead
4) Have fun storming the castle
5) You'd make a great Dread Pirate Roberts
And my favorite, especially since I'm a retired copyeditor: "You keep using that word. I do not think it means what you think it means."
I even have the sweatshirt, which I wear as often as possible.
If we are talking favorite quotes, mine will always be "It's possible, pig. Anything is conceivable, you miserable vomitous mass."
No one writes insults like William Goldman.
Mawwaige. Mawwaige is whut bwings us twogevvah twoday. Mawwaige, that bwessed awaingement….that dweam whivvin a dweam.
Lynn253 January 12th, 2017
Don't feel bad about not recognizing Christopher Guest, I didn't either. I think it was the 6th finger that threw me off.
I think there's was such a strong association that Christopher Guest = Nigel Tufnel. It never occurred to me until after "Waiting For Guffman" that he's a character actor. Of course he's not stuck in any one "type"!
Also, Rugen is HOT. Christopher Guest is just a nice looking guy. LOL
Lucky Jamie Lee Curtis!
Jen January 12th, 2017
I've always loved the houppes in this movie. But then, I do love 15th century fashion.
Ditto! I skew to the last two decades of the 15th c. but I appreciate a good houppelande. Especially on guys. OH BABY.
One of these days, I will make Francis a full-length Henry V-style houppe. Whenever I actually get my sewing give-a-fuck back from wherever it fucked off to…
Did someone borrow it? And what colour will Francis' houpplande be?
LoGirLoo January 13th, 2017
I adore Buttercup's blue dress too. I wish that it showed up more in the movie; I was obsessed with it when I was a teenager.
GAHHHHH!!!!! One of my most favouritest movies of all time!! I've always loved the costumes in this and watching it as an adult (I was 9/10 when it came out) and historical costume person I've always appreciated the pretty high level of historicity of it. The blue dress may be my favourite of Buttercup's but I my favourite piece of hers is actually the least historical (lol) – the blue silk sleeveless robe she wears over her nightgown. I just love the visuals of her sweeping through the castle corridors in it and would love to do the same, haha!
Count Rugen and Prince Humperdink's houppes may be my overall favourite costumes of the movie, they are so well done! And love when Humperdink does his little twirls – whee!
Sonya Heaney January 16th, 2017
I tried the book, got to the part where he *actually hit her* (not just a threat like in the movie), and stopped.
I love the book, but Goldman actually improved on the novel when he wrote the screenplay. I will always take screaming eels over sharks.
Maggie January 27th, 2017
I was so so excited to see Amy Poehler in the red riding costume. It's actually a repro, not the original from the movie, as far as I know. When I posted a pic of this on FB, someone identified the person that made the gown for the show. The commenter said "Lauren Matesic Bregman of Castle Corsetry" made the costume. https://www.facebook.com/costumersguide/photos/a.184352868295517.49920.176197025777768/628804533850346/?type=3&theater
I was really impressed with their version because they did get the detail of the cartridge pleats and all that.
Enjoyed the post! Thanks!
Karen K. February 14th, 2017
My FAVORITE MOVIE OF ALL TIME. So nice to know that the costumes are mostly historically accurate.
Anne-Helen Wickizer November 25th, 2018
Thank you so much for this…I am co-hosting a screening of the Princess Bride at my Community College as a board member of the Fashion Club, (the other club is the Martial Arts club) and I get to talk about the costumes and the accuracy of them. I have so much historical crap in my brain, most of it unfiled that I was lost as to where to even start looking. My mind palace is a mess, but, as always, FFs helped me out. You guys rock so hard!!
Cindy January 12th, 2019
Everything you said…Love love love this movie!
For more shots of Buttercup's gorgeous blue gown, I found them here:
https://movie-screencaps.com/the-princess-bride-1987/43

redpajama_set_name: RedPajamaCommonCrawl


text:
Wales Sees Record Price Growth and Market Activity as Housebuilding Recovers
A - Home Page B - Original Content Built Environment
Built Environment , 7th December 2021
New housing delivery reached its highest point since before 2009 between July and September, exceeding Q3 2019 delivery by 30.7 per cent. EPC data shows that 2,899 new homes were completed during the quarter
However NHBC data suggests that while completions are back at average pre-pandemic levels on a quarterly basis, starts have recovered more slowly and sit below completions. This suggests a shrinking pipeline of housing under construction.
This is despite planning consents staging a rapid recovery since falling sharply during the pandemic, exceeding average 2019 levels in Q2 2021 before falling slightly below in Q3.
Delivery in the first half of 2021 exceeded Welsh Government housing need by 32.9 per cent with all four regions exceeding the required level. However the more ambitious targets set within Local Development Plans were missed in every region except North Wales.
South West Wales fell short by 45.7 per cent of the total number of new homes required as set out in its Local Development Plans, while Mid Wales was 35.3 per cent and South East Wales was 30.6 per cent under the required level.
These figures are set against a backdrop of record demand for residential property in Wales, comments Scott Caldwell, head of Savills development team in Cardiff;
'Pent up demand and the extended Land Transaction Tax holiday has supported a strong housing market this year, with transactions 19 per cent higher than the 2017-19 average and price growth of 15.3 per cent to September. While we expect less urgency in 2022, there remains a large gap between demand and supply – a theme which extends into the development land market where we expect consented sites to be the subject of intense competition as a result of the buoyant market and severely constrained land availability across South Wales.'
Commenting on Local Development Plan progress, Rhys Govier, an associate director in the Savills Cardiff planning team, said; "A number of authorities continue to progress with their second Local Development Plans, with Delivery Agreements agreed with Welsh Government showing staggered progress, particularly amongst the South East Wales authorities, which is also targeting a Strategic Development Plan by 2028. Numerous opportunities remain, therefore, for the promotion of sites through the plan making process.'
The most up-to-date EPC figures show an increase in the use of Help to Buy in Wales in the first half of 2021, with the scheme supporting 20.3 per cent of new home purchases to June. This uptick follows a period of declining use since 2018.
Claire Turner, head of residential development sales for Savills Cardiff, notes;
'The surge in Help to Buy use earlier this year reflects the rush to take advantage of the scheme before a lower eligibility cap of £250,000 – from £300,000 – was introduced in May. The new £250,000 cap would have excluded 32 per cent of Help to Buy sales in the year to March 2021. Falling use in annual terms will therefore likely emerge over the next 12 months, as the impact of the price cap is felt.'
Affordable delivery increased by 14% in the year to March 2020 but was 16% below need. As this was almost entirely before the pandemic disruption, it is likely that 2021 delivery will be lower, which would create an even greater gap between delivery and need.
Caroline Jones is an expert in affordable housing in Savills development team in Cardiff. She said:
'Affordable housing delivery is being affected by a whole host of challenges in the current market, with build cost increases, materials and skilled labour shortages, alongside increasing statutory requirements all putting pressure on viability and deliverability.
'Smaller developers may be nervous about starting new projects because uncertainty over price increases over a build period makes it is difficult to tie down a price at the outset. The rises we have seen have impacted heavily on margins and we have unfortunately seen some longstanding names disappear this year as a result. Welsh Government, Housing Associations and council house building authorities need to work together to support the SME construction sector in Wales to restart momentum.'
Commenting on the robust house price growth in Wales over the last 12-18 months and the 18 per cent growth forecasted by Savills researchers over the next five years, Caroline said;
'Unfortunately, while this strong growth is and will continue to help private developers to offset some of the build cost inflation, it has very little impact when delivering a 100% affordable housing scheme. As a consequence, more and more HAs are now incorporating private units into schemes so the profit can be invested back and subsidise the more costly social rented element. I expect we will see a continuation of this trend over the next year.'
US Market to Open Doors to UK Lamb from 2022
SME Manufacturers Urge Government to Create Funding Package


As clients start to approach the age for retirement, many worry they won't survive financially, and they will need long-term care because their health has started to slope downhill. They may not even worry about it for themselves as much as they worry about it for a family member. In the annual Genworth Financial's study, researchers discovered how long-term care provides almost no comfort on that score, and the costs will continue to elevate at a substantial rate as some of the priciest states in the United States only continue to raise their costs. It becomes enough to make anyone hesitate to retire especially if they haven't built a significant nest egg. One year in the nursing home can devour every cent they saved.
The caregivers who provide financial assistance for their loved ones will often estimate the expenses as high as $10,000 in out-of-pocket expenses. This may include personal items, household expenses, transportation and even LTC facilities. Many times, the money gets siphoned out of the caregiver's personal retirement savings or their own daily budget. What are some states that will cost you the most in the United States?
As far as cost goes, you can expect an average annual cost to reach as high as $66,418, and if you want to send your loved one to an adult day care, it will cost you as much as $22,100. For licensed home care, it will run as high as $50,336. Meanwhile, you can expect to pay around $59,400 and a private room at the nursing home will cost you an average $133,835 in annual costs. If you live in New Jersey, perhaps you could retire in the, "Land Down Under." Here the community shares the costs of their aged care services among the commonwealth and the States.
On the whole, the average cost of Hawaii doesn't differ too much from New Jersey. The average annual cost here will be around $66,533, which is only $100 more than New Jersey. Adult day care only costs $22,100, and for a licensed home care professional, you can expect to pay $57,772. Nursing home with a private room, however, will cost you an average $141,310.
Sometimes referred to as the, "Land of Steady Habits," you can expect an average annual cost of around $72,836. For an adult day care, it costs less than the other two states and only costs $21,008 annually. Meanwhile, licensed home care will run you an average of $50,336, and assisted living costs $59,400. A nursing home with a private room will cost around $160,600.
In general, Alaska normally experiences highest costs of all the states anyway because of how everything has to be shipped in for it. However, the high cost extended to long-term aged care services is no different from the high cost of healthcare here, and you can expect to pay an average annual cost of around $114,767. Meanwhile, adult day care costs $26,624, and licensed home care will run $59,442. The cost of assisted living can run as high as $81,000, and for a nursing home with a private room, you can expect to pay as much as $292,000.
Luckily, some of these costs will be covered through insurance, but when they're not covered, that's when it hurts. Looking at growth on a five-year scale for adult day care, they have had an annual growth rate of 2.53 percent. The only higher cost growth has been nursing home costs over the same period at 3.51 percent. Even when a family member takes the role of the primary caregiver, the cost can still skyrocket, which is why you have to understand the actual medical care costs.


text:
All Female Productions of the classics - It's not historically accurate. So what?
Aliki Chapple, 27 January 2013
The session was attended by: Alison Goldie, Grace Gummer, Ellan Parry, Kitty Martin, David Bellwood, Rebecca Manson Jones, Rose Biggin, Mark Courtice - Apologies if I've misspelled your name or omitted it in error.
I started out by saying that I was one of a group of women in NW England who wanted to stage all-female productions of the classics, beginning with but not limited to Shakespeare, and that we were interested in people's reactions to the concept. I've tried to organize the report by topics we discussed rather than in chronological order. If anyone present feels I've missed anything out or misrepresented anything, please do say.
Why? Why not?
Advocacy - All female productions as redressing the balance
Someone was amazed that more women don't do this, given the huge number of women actors and the limited number of available roles.
Rebecca said she finds herself veering between great approval of the idea and a desire to poke holes in it (I am paraphrasing)
Why wouldn't cross-gendered casting do the same thing?
It's good for the actors, is it good for the audience?
There is a question of restoring a historical injustice as well as getting to play those roles.
The world is changing, gender roles are changing, but students are still encouraged to think very traditionally about gender in canonical plays.
The backlash against changing gender roles is pushing young people in the direction of hyper-feminine and hyper-masculine self-presentation. All female productions can help illustrate the performativity of gender
"For every all male production there should be an all female production"
The possibility of throwing new light on the text through this approach, whether to illuminate gender roles in a historical text, or, more broadly, to illuminate a particular character or relationships between characters by making the familiar strange.
David told us how much he admired the Portia in the Donmar Warehouse production of Julius Caesar, how the tenderness between the characters was deeper and stronger than he's ever seen.
Can it reveal a core of meaning in the text that was obscured by gender?
Again a great deal of love for the Donmar Warehouse JC, from everyone present who had seen it, several people citing the enthusiastic audience response.
There are audiences that will be drawn to a production because it's all female, there are audiences that will shun it for the same reason.
What if you never mentioned it was all female, and just allowed the audience to notice in their own time?
Because of the sexism of most texts, it's common to have only one or two (usually young) women in a company of men. Very often, women don't have older women in a production to learn from, but in an all-female production, there might be an abundance of elders.
Because we want to
Women wanting to work with other women, to play those parts... This is a gopod enough reason in itself.
The Fallacy of All-Male Historical Theatre
(I really wanted to write "phallusy")
There's a sense in the general public that all-male theatre is traditional, just the way things were. This is not historically robust : It's something that happened in a very particular time and place and for quite a short period, not the way things were done since time immemorial.
Does an all female production tacitly accept this premise? Does it reinforce the "Bardology", accept all male productions as more historically valid to seek to overturn the model?
Do you change the text, make the character a woman?
Do you you "bloke up", drag king the character and go to lengths to perform masculinity?
Or do you just "not get your knickers in a twist" about it and just play the character who happens to be male?
Do you set the play in a time and place that justifies everyone being female?
Does the period in which you set it facilitate or disturb the suspension of disbelief?
Ellan, who is a designer, is very interested in how design works with gender presentation in such a production, and is keen to work with us.
Or do you just do it, without highlighting or justification?
There are parallels with race blind casting, with modern dress or different historical settings for texts.
How do you market such productions so as to put off the fewest possible potential audience members?
Conclusion: We're doing it. We're The Rose Company, we're based in Lancaster, and we will have an all female Shakespeare production in summer 2014, come hell or high
NW, Theatre, gender, crossdressing, women, Shakespeare, theatre, canon, cross casting
Jen McGregor, 28 March 2013
Interesting report. I run a theatre company in Edinburgh and we're gearing up for a production of Macbeth. It's not all female, but the casting was gender-blind and there's only one male in the cast. We simply don't have enough men in the ensemble to cast according to gender. Instead, I have a lot of committed, talented and versatile women. It makes sense to use them, and if I want to tackle the classics that means being willing to cast cross-gender. So I prefer to cast according to qualities, not chromosomes.
A couple of years ago I directed a production of Romeo and Juliet with a female playing Tybalt. That was the only role where I cast cross-gender. She played the character as a man. I wondered whether anyone would ask about this choice at the Q&A sessions we held, but... no-one did. She had the right qualities for the character and was believable, so the audience just went with it. I think we need to trust them to do that.
It was a period (or fantasy period, since it wasn't accurate to any particular point in history) production and I think that helped, because the costumes gave clear indications as to who was male and who was female. I believe it's important not to confuse your audience there, because if the story depends on a female character being subject to gender-related behavioural constraints - Juliet, for example, seems unable to leave the house unaccompanied unless she has permission - then you can't have another female character of the same background who isn't subject to those constraints. If Tybalt were being depicted as a woman, we would wonder why Tybalt can run around Verona at will while Juliet can't. It makes Juliet look like a bit of a wimp for getting the nurse to do all her dirty work for her, because we assume that she could and just chooses not to. It strips her of strength and sympathy. Likewise, if you set your production in a time period/society where women have the freedom to work, hold property remain unmarried, have sex and children out of wedlock and have access to birth control, many of Shakespeare's stories lose their power.
Personally, I plan to market Macbeth in exactly the same way that I would if it weren't gender-blind. I might well have Macbeth himself (played by a woman, and she's really not a masculine-looking woman) on the poster. If that puts a few people off, well... it probably wasn't going to be their cup of tea anyway.
Good luck with your production! Do you have a blog where we can follow its progress? Ours is www.tightlacedtheatre.com.


Intimate and intelligent, Drake Doremus's (Like Crazy) drama unfolds in a sleepy community in upper New York State. Sophie arrives to stay with a family, taking piano lessons from Keith and hanging out with his daughter Lauren. However, Sophie's longing for excitement, Keith's growing disenchantment with his life and his wife Megan's reluctance to move back to the city threaten their comfortable existence.


Error screen after credit card registration.
I was completing my registration, I put the data of my credit card.
The system requested my email and password. Then the error screen appeared.
Hmm that looks odd -- but I do see a related error in our logs that appear like an extension or something might be interfering with the URL.
Any chance you can try going directly to the new credit card page in an incognito browser (e.g. no cookies or extensions)?


Catharina Elizabeth PUTTER was born estimated 1800. She married Johannes Christoffel GREYLING, son of Arnoldus Jacobus GREYLING and Johanna Elisabeth de KLERK, on December 4, 1820 in Cradock, Ooskaap, SUID AFRIKA,. He was born on August 7, 1796.


text:
SOLVING THE DISTAL REWARD PROBLEM THROUGH LINKAGE OF STDP AND DOPAMINE SIGNALING
United States Patent Application 20080162391
Kind Code:
In Pavlovian and instrumental conditioning, rewards typically come seconds after reward-triggering actions, creating an explanatory conundrum known as the distal reward problem or the credit assignment problem. How does the brain know what firing patterns of what neurons are responsible for the reward if (1) the firing patterns are no longer there when the reward arrives and (2) most neurons and synapses are active during the waiting period to the reward? A model network and computer simulation of cortical spiking neurons with spike-timing-dependent plasticity (STDP) modulated by dopamine (DA) is disclosed to answer this question. STDP is triggered by nearly-coincident firing patterns of a presynaptic neuron and a postsynaptic neuron on a millisecond time scale, with slow kinetics of subsequent synaptic plasticity being sensitive to changes in the extracellular dopamine DA concentration during the critical period of a few seconds after the nearly-coincident firing patterns. Random neuronal firings during the waiting period leading to the reward do not affect STDP, and hence make the neural network insensitive to this ongoing random firing activity. The importance of precise firing patterns in brain dynamics and the use of a global diffusive reinforcement signal in the form of extracellular dopamine DA can selectively influence the right synapses at the right time.
Izhikevich, Eugene M. (San Diego, CA, US)
NEUROSCIENCES RESEARCH FOUNDATION, INC. (San Diego, CA, US)
G06N3/08
Download PDF 20080162391 PDF help
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GONZALES, VINCENT
FLIESLER MEYER LLP (650 CALIFORNIA STREET, 14TH FLOOR, SAN FRANCISCO, CA, 94108, US)
1. In a simulated nervous system network having a first pre-neuron and a second post-neuron, and a synaptic pathway between the first pre-neuron and the second post-neuron having synaptic strength (s) and an eligibility trace (c), a method for determining a firing pattern of the first pre-neuron and the second post-neuron, comprising: (a) firing the first pre-neuron and the second post-neuron to induce changes to the synaptic strength (s) according to a spike-timing-dependent plasticity (STDP) rule; and (b) providing extracellular dopamine to the synaptic pathway during a window of time after the firing and the eligibility trace (c) decays to zero.
2. A method according to claim 1, wherein the window is in the range of a few seconds.
3. A method according to claim 1, further comprising delivering to the synaptic pathway an increase of dopamine each time a post-synaptic firing of the post-neuron occurs within a certain time after a pre-synaptic firing of the pre-neuron to increase the synaptic strength (s).
4. A method according to claim 3, wherein the increase in dopamine is provided at a random delay of between 1-3 seconds after the firing of the pre-neuron and the post-neuron and each time the post-neuron firing occurs within about 10 ms after the pre-neuron firing to reinforce the firing of the first pre-neuron and the second post-neuron.
5. A method according to claim 4, wherein the increase in dopamine is provided until the synaptic pathway is potentiated up to a maximum allowable value.
6. A method according to claim 4, wherein the maximum allowable value is 4 mV.
7. In a simulated nervous system network having a first pre-neuron and a second post-neuron, and a synaptic pathway between the first pre-neuron and the second post-neuron having synaptic strength (s) and an eligibility trace (c), a method for implementing reinforcement learning in a spiking network based on spike timing dependent plasticity (STDP), comprising: (a) firing the first pre-neuron and the second post-neuron within a substantially coincident time of one another to induce changes to the synaptic strength (s) according to a spike-timing-dependent-plasticity (STDP) rule; (b) detecting an eligibility trace (c) over a time window commencing with the substantially coincident firings of the first pre-neuron and the second post-neuron, the eligibility trace (c) decaying towards zero over the time window; and (c) providing an extracellular global diffusive reinforcement signal to the synaptic pathway during the time window and providing an increase in the reinforcement signal at a time in the window occurring at about 1-3 seconds after the coincident firing and commencement of the time window.
8. In a simulated nervous system network having four random groups of neurons, representing, respectively, an unconditional stimulus (US), a first conditional stimulus (CS1), a second conditional stimulus (CS2), and cortical projections (VTAp) that project to a ventral tegmental area (VTA) of a brain responsible for releasing dopamine, and in which there are synaptic connections from the unconditional stimulus (US) groups of neurons to the cortical projections (VTAp) group of neurons, and from the first conditional stimulus (CS1) group of neurons, and the second conditional stimulus (CS2) group of neurons, a method of shifting the release of dopamine in response to the unconditional stimulus (US) to an earlier reward-predicting conditional stimulus (CS1) and (CS2), comprising: (a) initially setting the synaptic connections from the unconditional stimulus (US) groups of neurons to maximum values; (b) firing the neurons of the unconditional stimulus (US) groups of neurons to induce changes to the synaptic strength (S) according to a spike-timing-dependent plasticity (STDP) rule, and to cause a response in the neurons of the cortical projections (VTAp); (c) firing the neurons of the first conditional stimulus (CS1) prior to firing the unconditional stimulus (US) by about 1±0.25 seconds to induce changes to the synaptic strength (S) in accordance with the STDP rule and to shift to and evoke a response by the cortical projections (VTAp) to the first conditional stimulus (CS1) of neurons; and (d) firing the neurons of the second conditional stimulus (CS2) prior to firing the neurons of the first conditional stimulus (CS1) by about 1±0.25 seconds to induce changes to the synaptic strength (S) in accordance with the STDP rule and to shift to and evoke a response by the cortical projections (VTAp) to the second conditional stimulus (CS2) of neurons.
CLAIM OF PRIORITY
This application claims priority to U.S. Provisional Application No. 60/877,841 entitled "SOLVING THE DISTAL REWARD PROBLEM THROUGH LINKAGE OF STDP AND DOPAMINE SIGNALING" by Eugene M. Izhikevich, filed Dec. 29, 2006 and incorporated herein by reference.
The present invention relates generally to brain dynamics and, more particularly, to methods for solving the "distal reward problem" or "credit assignment problem."
Learning the associations between cues and rewards (classical or Pavlovian conditioning) or between cues, actions, and rewards (instrumental or operant conditioning) involves reinforcement of neuronal activity by rewards or punishments. Typically, the reward comes seconds after reward-predicting cues or reward-triggering actions, creating an explanatory conundrum known in the behavioral literature as the distal reward problem and in the reinforcement learning literature as the credit assignment problem. Indeed, how does an animal know which of the many cues and actions preceding the reward should be credited for the reward? In neural terms, in which sensory cues and motor actions correspond to neuronal firings, how does the brain know what firing patterns, out of an unlimited repertoire of all possible firing patterns, are responsible for the reward if the firing patterns are no longer there when the reward arrives? How does the brain know which of the spikes of many neurons result in the reward if many of these neurons fire during the waiting period to the reward? Finally, how does a reinforcement signal in the form of the neuromodulator dopamine (DA) influence the right synapses at the right time, if DA is released globally to many synapses?
This problem, mentioned above as the distal reward problem in the behavioral literature or the credit assignment problem in the machine learning literature, is notoriously difficult to solve in autonomous robotics. Such robotic devices have to execute multiple steps before they achieve the goal and obtain a reward. There is a whole subfield of the machine learning field known as "reinforcement learning theory" that attempts to solve this problem using artificial intelligence and dynamic programming methods.
A similar problem exists when the behavior of the robot is controlled by a simulated neural network, as in what are known in the art as brain-base devices (BBDs). Indeed, how does the simulated neural network of a BBD know what firing patterns of what neurons are responsible for the reward if (a) the firing patterns are no longer there when the reward arrives and (b) most neurons and synapses are active during the waiting period to the reward? Traditionally, this problem is solved using one of the two assumptions: (1) the neural network is designed to be quiet during the waiting period to the reward; then the last firing neurons are the ones that are responsible for the reward, or (2) the firing patterns that are responsible for the reward are somehow preserved until the reward arrives; then whatever neurons are firing at the moment of reward are the ones that are responsible for the reward. Both assumptions are not suitable for BBDs because BBDs are embedded into and operate in real-world environments and thereby receive inputs and produce behavior all the time, even during the waiting period to the reward.
With respect to DA modulation of synaptic plasticity, an important aspect is its enhancement of what is known as long-term potentiation (LTP) and long-term depression (LTD). For example, in the hippocampus of the brain, dopamine D1 receptor agonists enhance tetanus-induced LTP, but the enhancement effect disappears if the agonist arrives at the synapses 15-25 seconds after the tetanus. LTP in the hippocampal→prefrontal cortex pathway is enhanced by direct application of DA in vivo or by burst stimulation of the ventral tegmental area (VTA), which releases DA. Correspondingly, D1 receptor antagonists prevent the maintenance of LTP, whereas agonists promote it via blocking depotentiation even when they are applied after the synapse plasticity-triggering stimuli. DA is also shown to enhance tetanus-induced LTD in layer 5 pyramidal neurons of the prefrontal cortex, and it gates corticostriatal LTP and LTD in striatal projection neurons.
Synaptic connections between neurons may be modified in accordance with what is known as the spike-timing dependent plasticity (STDP) rule. STDP involves both LTP and LTD of synapses: firing of a presynaptic neuron immediately before firing of a postsynaptic neuron results in LTP of synaptic transmission, and the reverse order of pre, post synaptic neuron firing results in LTD. It is reasonable to assume that the LTP and LTD components of STDP are modulated by DA the same way as they are in the classical LTP and LTD protocols. That is, a particular order of firing induces a synaptic change (positive or negative), which is enhanced if extracellular DA is present during the critical window of a few seconds.
A method is disclosed of solving the distal reward problem or the credit assignment problem using spiking neurons with spike-timing-dependent plasticity (STDP) modulated by a rewarding substance—e.g., dopamine (DA). Although STDP is triggered by nearly-coincident firing patterns on a millisecond time scale, slow kinetics of subsequent synaptic plasticity is sensitive to changes in the reward (DA) concentration during the critical period of a few seconds. Random firings during the waiting period to the reward do not affect STDP, and hence make the network insensitive to the ongoing activity. The network can selectively reinforce reward-triggering precise firing patterns, even if the firing patterns are embedded into a sea of noise and even if the rewards are delayed by seconds. Thus, if a behavior of a BBD is governed by precise firing patterns in a simulated nervous system and some patterns (some actions) consistently bring rewards, the synaptic connections between the neurons generating these patterns strengthen so that the BBD is more likely to learn and exhibit the same behavior in the same environmental context in the future.
In accordance with one embodiment of the present invention, the distal reward or credit assignment problem is solved using a simulated network of spiking neurons with DA modulated plasticity. DA modulation of STDP is shown to have a built-in property of instrumental conditioning: it can reinforce firing patterns occurring on a millisecond time scale even when they are followed by rewards that are delayed by seconds. This property relies on the existence of slow synaptic processes that act as "synaptic eligibility traces" or "synaptic tags."
These synaptic processes i.e., the eligibility traces or tags, are triggered by nearly-coincident spiking patterns of two neurons, but due to a short temporal window of STDP, the processes are not affected by random firings during the waiting period to the reward. For example, as described and illustrated more fully below, consider two neurons, each firing 1 spike per second, which is comparable to the spontaneous firing rate of neocortical pyramidal neurons. A nearly coincident firing of the two neurons will trigger STDP and change the synaptic tag. However, the probability that subsequent random spikes with the same firing frequency will fall within 50 ms of each other to trigger more STDP and alter the synaptic tag is quite small—on average once per 20 seconds. This "insensitivity" of the synaptic tags to the random ongoing neuronal spiking activity during the waiting period is a feature that distinguishes the present invention from previous studies, which require that the network be quiet during the waiting period or that the patterns be preserved as a sustained response. As further described below, DA-modulated STDP can selectively reinforce precise spike-timing patterns that consistently precede the reward, and ignore the other firings that do not cause the reward. This mechanism works when precise firing patterns are embedded into the sea of noise and would fail in the mean firing rate models.
Also, in accordance with the present invention, a spiking network implementation is described of the most important aspect of the temporal difference (TD) reinforcement learning rule—the shift of reward-triggered release of DA from unconditional stimuli to reward-predicting conditional stimuli.
Thus, the simulations described in the present application demonstrate how DA modulation of STDP may play an important role in the reward circuitry and solve the distal reward or credit assignment problem.
FIG. 1 shows generally the instrumental conditioning of a synapse in accordance with the present invention;
FIG. 1(a) is an illustration of two coupled pre- and post-synaptic neurons used to explain the dynamics of a synapse governed by two variables, synaptic strength s and eligibility trace c;
FIG. 1(b) is a chart showing changes in the variable c over a time interval according to the known spike-timing-dependent plasticity (STDP) rule;
FIG. 1 (c) is a magnification of the region shown in FIG. 1 (d) by an "asterisk";
FIG. 1 (d) is a graph illustrating the consistent rewarding of each event of post-synaptic firing of the post-neuron occurring within 10 ms after a pre-synaptic firing of the pre-neuron shown in FIG. 1(a);
FIG. 2, generally, are diagrams used to describe classical (Pavlovian) conditioning;
FIG. 2(a) illustrates a continuous input stream of stimuli received by a network of groups of randomly chosen neurons, S1, S2, . . . ;
FIG. 2(b) is a histogram illustrating the response of the network to the stimuli of FIG. 2(a) at the beginning of an experiment to be described in the present application;
FIG. 2(c) is a histogram illustrating the response of the network to the stimuli of FIG. 2(a) after one hour of the experiment to be described;
FIG. 2(d) is a graph illustrating the mean excitation strength of synapses outgoing from the neurons in a group S1 and the mean excitation strength of synapses in groups S2, S3 . . . , in the remainder of the network;
FIG. 3 generally shows instrumental conditioning;
FIG. 3(a) illustrates three groups S, A and B of randomly chosen neurons that correspond to the representation of an input stimulus and two (non-antagonistic) motor responses, respectively;
FIGS. 3(b) and (c) are graphs showing the responses of groups A and B of neurons of FIG. 3(a) over a number of trials;
FIG. 4 illustrates generally the spiking neuron implementation of a shift of the DA response amongst random groups of neurons;
FIG. 4(a) shows four random groups of neurons representing unconditional stimulus (US), two conditional stimuli CS1 and CS2, and a group VTAp responsible for the release of extracellular dopamine;
FIG. 4(b) is a histogram used to explain trials 1-100;
FIG. 4(c) is a histogram used to explain trials 101-200;
FIG. 4(d) is a histogram used to explain trials 201-300; and
FIG. 4(e) is an illustration used to explain the mechanism of the shift from group US of neurons to group CS1 of neurons.
I. Materials and Methods
Details of the kinetics of the intracellular processes of the brain triggered by STDP and DA are unknown; therefore, in the present application the simplest phenomenological model that captures the essence of DA modulation of STDP is described. This is illustrated and to be described in relation to FIGS. 1 (a)-FIG. 1 (d).
With respect to FIG. 1(a), the dynamics of a synapse between the pre-neuron and a post-neuron are described by two phenomenological variables governed by Eqs. (1) and (2) set forth below: synapse strength s and eligibility trace c. The latter is gated by extracellular dopamine (DA) d as shown graphically in FIG. 1(c). Firings of the pre- and post-synaptic neurons shown in FIG. 1(a) induce changes to the variable c according to the spike-timing-dependent plasticity (STDP) rule, as shown in FIG. 1(b). These changes in variable c result in modification of the synaptic strength, s, only when extracellular dopamine d is present (d>0) during a critical window of a few seconds while the eligibility trace c decays to zero; see in FIG. 1(c) respective curves "extracellular dopamine d(t)" and "eligibility trace c(t)." (FIG. 1(c) is a magnification of the region in FIG. 1(d) marked by "*".)
To reinforce coincident firings of the two coupled pre, post neurons shown in FIG. 1(a), a reward is delivered each time a firing of the postsynaptic neuron occurs within 10 ms after a firing of the presynaptic neuron. This coincident firing of the pre, post neurons is shown by the blue rectangle in FIG. 1(c). This rare event increases variable c greater than any random firings (shown in FIG. 1(c)) of the same pre, post neurons during the delayed period of coincident pre, post neuron firings. The reward that is delivered is, as shown in FIG. 1(c), a step increase in the extracellular dopamine d provided at a delay of about 1-3 seconds from the coincident firings.
As shown in FIG. 1(d) and as will be further described, consistent rewarding of each such event of coincident pre, post neuronal firings results in the gradual increase of synaptic strength, s. This in turn increases the probability of coincident pre, post neuron firings and brings even more rewards of extracellular dopamine d. On the other hand, the time course of a typical un-reinforced synapse would look like a random walk near 0. The reinforced synapse, as shown in FIG. 1(d), is potentiated to the maximal allowable value 4 mV whereas the other synapses are not. The pre- and post-inset in FIG. 1(b) shows the distribution of all synaptic weights in a network before (dotted red) and after (green) the experiment.
More particularly, the state of each synapse using the two phenomenological variables (s,c), i.e., synaptic strength/weight, s, and activation of an enzyme important for plasticity, c, e.g., autophosphorylation of CaMK-II, oxidation of PKC or PKA, or some other relatively slow process acting as a "synaptic tag" is defined by:
ċ=−c/τc+STDP(τ)δ(t−tpre/post) (1)
s=cd. (2)
As already mentioned, d describes the extracellular concentration of dopamine DA, and δ(t) is the Dirac delta function that step-increases the variable c. Firings of pre- and postsynaptic neurons shown in FIG. 1(a), occurring at times tpre/post, respectively, change c by the amount STDP(τ) depicted in FIG. 1(b), where τ=tpost−tpre is the interspike interval. This variable c decays exponentially to c=0 with the time constant τc=1 second, as shown in FIG. 1(c). The decay rate controls the sensitivity of plasticity to delayed rewards. Variable c acts as the eligibility trace for synaptic modification, since it allows for change of the variable synaptic strength s via Eq. (2) gated by extracellular dopamine d. (Other, detailed biophysical/kinetic models that may be used as a description of the decay of variable "synaptic tag" c are possible.) As shown in FIG. 1(c), the decay of the eligibility trace c(t) is relatively fast, so that the effect of DA is negligible 5 seconds after the STDP-triggered coincident firing event, which is consistent with prior known experimental results in which no effect was observed when DA was delivered 15-25 seconds after the induction of plasticity.
The model described in the present application integrates, in a biophysically plausible way, the millisecond time scale of spike interactions in synapse-specific STDP with the slow eligibility trace c modulated by the global reward signal d corresponding to the behavioral time scale. There is no direct experimental evidence for or against this model; thus, the model makes a testable prediction, rather than a postdiction, on the action of DA on STDP based on purely theoretical considerations.
The variable d describes the concentration (μM) of extracellular DA, and it is the same for all synapses in the present described model (whereas variables c and s are different for different synapses). It is assumed that τd is the time constant of DA uptake and DA(t) models the source of DA due to the activity of dopaminergic neurons in the midbrain structures VTA (ventral tegmental area) and SNc (substantia nigra pars compacta) (described more fully with reference to FIG. 4). Other descriptions of DA kinetics are known in the art.
In the present simulations τd=0.2 second, which is greater than the experimentally measured time constant of DA uptake in striatum (around 0.1 second, as is known in the art) but smaller than that in the prefrontal cortex. The tonic source of DA is taken to be DA(t)=0.01 μM/s so that the baseline (tonic) concentration of DA is 2 nM as measured by microdialysis in the striatum and prefrontal cortex. The delivery of the reward of extracellular dopamine d in FIG. 1(c) is simulated as a burst of activity of dopaminergic neurons which step-increases the concentration of DA by 0.5 μM (i.e., DA(t)=0.5δ (t−trew) of μM/s at the moment of reward trew), which is in a range already measured in the art. Because the tonic level of DA is much lower than the phasic level during the reward, no significant modification of synaptic strength occurs (d≈0) unless the reward is delivered (d is large). In accordance with a further description of the invention below with reference to FIG. 4, DA(t)=0.004δ(t) μM/s is used for each spike fired by the neurons in a neuronal group VTAp. A possible extension of Eqs. (1), (2) is to consider a vector of synaptic tags corresponding to a cascade of processes. In this case, the STDP-triggered increase of the synaptic eligibility trace c would not be instantaneous, as shown in FIG. 1(c) at the time of the coincident pre, post neuron firings. Instead, it would slowly increase and then decrease, like the synaptic alpha function but on a longer time scale. The slow increase would create a "refractory period" corresponding to the insensitivity to rewards that come too early.
II. Results
There will now be described, as one example, a spiking network of 1000 cortical neurons with DA-modulated STDP to illustrate various aspects of reinforcement of precise firing patterns embedded into a sea of noise. Following this description, in Section IIE below, there will be a discussion of the computer simulation including software to carry out the method of the present invention.
A. Reinforcing a Synapse
As shown in FIG. 1, contingent firing of two (pre and post) neurons (FIG. 1(a)) is reinforced by a delayed reward d to illustrate how DA-modulated STDP addresses the distal reward or credit assignment problem on the synaptic level. This example is motivated by an in vivo monkey experiment described in the art. The example may appear to be artificial in the context of animal learning, but it explains the mechanism responsible for reinforcement of more complicated spiking patterns, as is described further below.
In a network of 1000 neurons and 100,000 synaptic interconnections, a synapse is randomly chosen that connects two excitatory (pre, post) neurons, as shown in FIG. 1(a), and its synaptic strength is set manually to zero (s=0). The firing rate in the network is around 1 Hz, so every few minutes the post-synaptic neuron of FIG. 1(a) fires by chance within 10 ms after the presynaptic neuron of FIG. 1(a) fires. Every time such an event occurs, marked by the blue rectangle in FIG. 1(c), a reward d is delivered to the network in the form of a spike of extracellular DA with a random delay between 1 and 3 seconds. Since the delivery of the reward depends on what the network is doing, the example of FIG. 1 may be interpreted as the simplest form of instrumental conditioning.
In FIG. 1(d) the strength of the synapse (black curve) and the moments the rewards are delivered (blue bars) are plotted. At the beginning, the network receives unexpected rewards every few minutes, but it does not know what causes the rewards or when. Because of the delay to the reward, all neurons fire and all synapses are activated during the waiting period to the reward, and all synapses receive the same amount of reward (variable d). As in "instrumental conditioning," the system network determines on its own what patterns of spiking bring the reward and how to reinforce the synapses.
Each delivery of the reward d potentiates the chosen synapse of the pre, post neurons and brings the synapse closer to the maximal allowable value of 4 mV, as shown in FIG. 1(d). On average, the probability (frequency) of rewards triples, and the chosen synapse quickly reaches the maximal allowable value of 4 mV. Other synapses change as well, but none reach 4 mV. The distribution of all synaptic weights, depicted in the inset in FIG. 1(d), remains relatively unchanged. To test the robustness of this phenomenon, 50 simulated experiments were undertaken, each with a randomly chosen synapse and schedule of reward delays. In 42 out of 50 experiments, the chosen synapse reached the maximal allowable value within a 1-hour period, requiring only 40±8 rewards.
Why is the chosen synapse consistently potentiated, but the other 79,999 excitatory synapses are not? (Only excitatory synapses are plastic in the model.) Nearly coincident pre-then-post firing of the two respective neurons shown in FIG. 1(c) within the blue rectangle increases the value of the variable c, which acts as the eligibility trace (synaptic tag) for the modification of the synapse. The subsequent non-coincident firings of the two (pre, post) neurons also shown in FIG. 1(c) perturb variable c slightly because the function STDP(τ) in FIG. 1(b) is small for large interspike intervals τ. As a result, eligibility trace variable c has a residual positive value shown in FIG. 1(c) when the delayed reward arrives at the end of the 1-3 second interval shown, so the synaptic strength s increases in proportion to cd; Eq. (2). A nearly coincident firing of the two (pre, post) neurons with the reverse order (post- then-pre) during the waiting period could make c negative, resulting in the decrease of s when the reward arrives, but the probability of such an adverse event during the waiting period is quite small (because the firing rate is small). There are many other pairs of neurons that fire nearly coincident spikes by chance just before the reward, so the corresponding synapses are also modified. However, the order of firing of these neurons is random, so after many firings, the positive and negative modifications cancel each other out, resulting in a net decrease of the synaptic weight (because the LTD area of the STDP curve shown in FIG. 1(b) is larger than the LTP area). As a result, across many trials, each reward d consistently potentiates only the chosen synapse and increases the cross-correlation between the pre- and postsynaptic neurons, thereby bringing more rewards.
B. Classical (Pavlovian) Conditioning
With reference to FIG. 2(a), there is illustrated a classical (Pavlovian) conditioning experiment: rewarding a conditional stimulus (S1) embedded into a continuous stream of a large number of irrelevant but equally salient stimuli. To simulate the experiment, 100 random sets, S1, S2, . . . , S100, of 50 neurons each are selected to represent 100 random stimuli. To deliver a stimulus, e.g. S1, all 50 neurons in the set S1 are stimulated with a 1-ms pulse of superthreshold current. The nearly coincident firing of the pre, post neurons in stimulus S1 reveals itself as a vertical strip in FIG. 2(b). The precise firing pattern is clearly seen only when activities of all neurons are plotted, but it cannot be seen in the activity of any individual neuron, since the spike evoked by stimulus S1 is not different from any other spike of the neuron. Next, a continuous input stream is formed comprising stimuli Sk(1≦k≦100) in a random order with random inter-stimulus intervals between 100 ms and 300 ms, i.e., on average 5 stimuli per second. Stimulus S1 is treated as the conditional stimulus (CS) and the other stimuli as distracters. For every occurrence of S1, a reward is delivered in the form of the increase of extracellular dopamine DA with a random delay of up to 1 second, as shown in FIG. 2(a) by the upward arrows titled "reward." The delay is large enough to allow many neurons in the network to fire a spike and to allow a few irrelevant stimuli during the waiting period, as shown in FIG. 2(b). Thus, the network receives rewards on average every 20 seconds caused by an unknown (to the network) firing pattern embedded into the sea of random spikes and distracters.
At the beginning of the experiment depicted in FIG. 2(b), all stimuli S1, S2, . . . have equal salience in the sense that they evoke coincident firings of 50 stimulated neurons. However, after a hundred of the conditional stimulus CS-reward parings, i.e., within the first hour, the response of the network to the particular conditional stimulus S1 becomes reinforced, as indicated by the thick vertical strip in FIG. 2(c). As shown in FIG. 2(d), the averaged strength of excitatory synaptic connections from neurons in conditional stimulus S1 becomes much stronger than the mean excitatory synaptic connections in the rest of the network. That is, the neurons in S1 can strongly influence their postsynaptic targets, or, in other words, the other neurons in the network listen more closely to the neurons in S1. The other neurons may contain motor neurons that trigger a conditional response. In this case, S1 would trigger the response more often and stronger than any other stimulus Sk. The conditional response could be a simple movement in the anticipation of receiving the reward or a learned motor response, as in the instrumental (operant) conditioning discussed in Section IIC below. The other neurons may also contain neurons projecting to the midbrain dopaminergic neurons, as discussed in Section IID below. In this case, presentation of the conditional stimulus S1 would trigger more DA release than presentation of any other stimulus Sk, i.e., S1 would acquire a rewarding value.
How can the network select and reinforce a single firing pattern in the presence of noise and irrelevant patterns, especially since the rewards come with a delay? Presentation of every stimulus Sk fires 50 neurons, which send spikes to other neurons in the network, possibly firing a few of them by chance. Because of the order pre-then-post, the synaptic connections from the 50 neurons to the fired neurons become eligible for potentiation, i.e., the corresponding tags cij increase. If no DA reward is delivered within a critical period after this event, the synaptic tags cij decay to zero (see FIG. 1(c)), resulting in small overall potentiation (due to the tonic level of DA) which is counterbalanced by depression (due to random spikes and the fact that the LTD window of STDP is greater than the LTP window as shown in FIG. 1(b)). However, if the DA reward comes within the critical period after the stimulation, the synapses are potentiated according to the mechanism depicted in FIG. 1(c). The stronger the synapses, the more excitation follows S1, the more postsynaptic targets fire, leading to even greater potentiation of synapses from neurons representing the conditional stimulus S1.
C. Stimulus-Response Instrumental Conditioning
FIG. 3 illustrates the simulation of a typical instrumental conditioning experiment: a network of 1,000 cortical spiking neurons is reinforced to produce an appropriate motor response to a stimulus. First, a random group of 50 neurons is selected, called group S, that represents the input stimulus to the network. Two random non-overlapping groups of 50 excitatory neurons each, called group A and group B, are also selected that give rise to two motor responses of the network. To deliver the stimulus (shown by the arrow labeled "stimulus" in FIG. 3(a)), a strong 1-ms pulse of current is injected into the neurons in group S to make them fire, as shown in FIG. 3(a) (the 2-3 ms delay is due to the spike upstroke). Their coincident firing typically evokes a few spikes in the other neurons in the network. During a 20 ms time window after the stimulation, the number of spikes fired by neurons in group A and group B, denoted as |A| and |B|, respectively, are counted. The network is said to exhibit response A when |A|>|B|, response B when |B|>|A|, and no response otherwise (e.g., when |B|=|A|=0 or 1). (A stronger requirement, e.g., |A|>2|B| for response A, would still be effective, but it takes a longer time to reinforce.) The neurons in groups A and B may be thought of as projecting to two motor areas that innervate two antagonistic muscles; to produce a noticeable movement, one group A, B has to fire more spikes than the other group A, B.
The simulated experiment consists of trials separated by 10 sec. In each trial, illustrated in FIG. 3(a), stimulation to neurons in group S is delivered (see the arrow "stimulus") and the response of the network is monitored. If the response is A (more spikes in group A than in group B), a reward is delivered in the form of the increase of extracellular DA with a delay of up to 1 second (the delay is inversely proportional to the ratio |A|/|B|, so that greater ratios result in faster movements and earlier rewards). During the first few trials, the probability of response of group A is the same as that of group B; see FIG. 3(b), but then it quickly increases to nearly 80% in less than 100 trials. As a control, after the first 400 trials (occurring at the dotted vertical line of FIG. 3(b)) the reward to response B is started. The probability of response group A decreases while that of group B increases, and the network switches its behavior after less than 50 trials after the first 400 trials. This experiment was repeated 20 times, selecting random sets S, A, and B. The network learned the correct response all 20 times. The only variability was the number of trials needed to reach the 80% correct probability of responses. Increasing the learning rate can decrease the number of required trials to just a few—consistent with animal experiments. However, the small size of the network would make the network responses less reliable (noisier) in this case.
The number of spikes fired by the neurons in group A and group B depends on the strength of the synaptic connections from S to A and B. Rewarding the response of group A reinforces connections to A, as can be seen in FIG. 3(c), according to the same mechanism as described in FIG. 1(a) for the pair of pre and post neurons. It also reinforces connections from group S to group B (because there is no winner-take-all competition between neurons in A and B), as well as connections from group S to any other neuron in the network (as shown in FIG. 2), though to a lesser degree. Indeed, if a neuron in group B starts to fire in response to the stimulation, but there are still more spikes in group A, the reward still comes and the connections from group S to that neuron in group B are potentiated. This may continue as long as |A|>|B|. A possible behavioral interpretation of this effect is that the network generalizes that rewards are delivered in response to stimulation S. Conversely, rewarding group B after 400 trials makes connections S→B stronger than connections S→A. The contrast between the synaptic connections to A and B can be further enhanced (and the percentage of correct choices improved) via anatomical constraints, such as stronger winner-take-all lateral inhibition. In this description of the present invention, the anatomy is kept simple (all-to-all with 10% connectivity) to emphasize the role of DA modulation of STDP over any other mechanism.
It is to be noted that a simple combinatorial consideration shows that there are more than 10164 different choices of two groups of 50 neurons out of 800 excitatory neurons. The network does not know the identity of the neurons in group A and group B, nor does it know the rules of the game or the schedule of the rewards. It receives seemingly arbitrary rewards and it determines on its own what brings the rewards and what it must do to increase the frequency of the rewards.
D. Shift of DA Response from US to Reward-Predicting CS in Classical Conditioning
With reference to FIG. 4, the basic phenomenology of shifting the release of DA in response to an unconditional stimulus (US) to an earlier reward-predicting conditional stimulus (CS) is discussed. The shift of the release of DA occurs automatically when VTA (Ventral Tegmental Area) projecting neurons are part of the whole network and the synapses onto these neurons are subject to the same DA-modulated STDP. Demonstrating the shift is the first step toward a spiking network implementation of what is known as the temporal difference (TD) error signal. The full spiking implementation of TD would require modeling the looping anatomy of striatum and basal ganglia, which is not described in the present specification.
First, a random group of 100 excitatory neurons is selected and it is assumed that this group, called VTAp, represents cortical projections to the ventral tegmental area (VTA) of the midbrain. VTA refers to the area in midbrain and VTAp refers to the group of neurons projecting to VTA (subscript "p" stands for "projecting"). Thus, it is assumed that the midbrain activity, and hence the amount of DA released into the network, is proportional to the firing rate of the neurons in this group. Next, a random group of excitatory neurons that represents the unconditional stimulus, called US, and two groups, CS1 and CS2 that represent two conditional stimuli, are chosen; see FIG. 4(a).
To simulate the prior association between the group US and the release of DA, the weights of synaptic connections from the group US to the group VTAp (projecting to VTA) are reset to the maximal allowable values. (This can be achieved by repeating the classical conditioning experiment described with reference to FIG. 2 with group S1 being the group US.) Thus, stimulating neurons in the group US results in a strong response in the VTA-projecting neurons VTAp, and hence releases DA, whereas stimulating any other random group of neurons does not result in significant response of the group VTAp. This is the only difference between the group US and the other neurons in the network. (Apparently, there are multiple pathways from US-triggered activity in the brain to the VTA; here only one, the cortical pathway, is considered.)
During the first 100 trials, where each trial is separated by 10-30 seconds, the neurons in the group US (but not the groups CS1 and CS2), are injected with a superthreshold current. Because of the strong initial projections from group US to group VTAp, this stimulation evokes a reliable response in the group VTAp resulting in the elevation of extracellular dopamine DA, and maintaining (reinforcing) the projections (indeed, due to the spontaneous release of DA, synapses are plastic all the time and may depress because STDP is dominated by LTD). The histogram in FIG. 4(b) shows the response of the entire group VTAp on the last trial (No. 100), and the spike raster shows a typical response of a single neuron in the group in 100 consecutive trials, which is similar to the in vivo recorded responses of midbrain neurons to unexpected rewards, novel, and salient stimuli.
During trials 101 to 200, neurons in the group CS1 are stimulated, and then neurons in the group US are stimulated with a random delay 1±0.25 seconds. As shown in FIG. 4(c), the group VTAp neurons start to fire in response to the reward-predicting conditional stimulus CS1, just after a few trials, as was observed in the art in vivo in monkeys and rats. The response of the neurons to the group US slowly decreases, so the response of the entire group VTAp to the last trial (histogram in FIG. 4(c)) is diminished. During trials 201 to 300, group CS2 is presented 1±0.25 seconds prior to group CS1, which is 1±0.25 seconds prior to group US. As can be seen in FIG. 4(d), the response of the neuron switches to the earliest reward-predicting stimulus, group CS2, though there is still some response to group CS1 and the group US, again, consistent with prior known in vivo work.
The mechanism of switching of the response from the group US to the earlier group CS relies on the sensitivity of STDP to the order of firings occurring within tens of milliseconds (despite the fact that each group CS and group US is separated by one second). Due to the random connections in the network, stimulation of group CS1 neurons causes some neurons in the group US to fire, which in turn causes some neurons in the group VTAp to fire; see FIG. 4(e) in the early trial. In essence, presentation of the group CS triggers the reactivation of the activity chain leading to the reward, CS1-then-US-then-VTA, but on a compressed time scale. This property emerged in the spiking network spontaneously. Due to the same mechanism as described in connection with FIG. 1, the order of firing CS1-then- VTA, and the subsequent release of DA due to the presentation of the group US, potentiates the direct synaptic projections CS1→VTAp, resulting in the increased response to the group CS1, as seen in FIG. 4(c), at left. After many trials, neurons in group VTAp can fire in response to firings of CS1 neurons alone, simultaneously or often before they receive spikes from the group US neurons, as indicated in FIG. 4(e) for the late trial. As a result of a jittered and often inverse order of firing, VTAp-then-US, and the fact that the LTD part of STDP as shown in FIG. 1 (b) is dominant over the LTP part of STDP, the synaptic projections US→VTAp depress, resulting in the decreased (unlearned) response to the group US seen in FIG. 4(c), to the right. The same mechanism is responsible for the switching of the response from group CS1 to group CS2 shown in FIG. 4(d). Again, this property appears spontaneously in a randomly connected network of spiking neurons with STDP.
E. Computer Simulations
All of the simulations described above, particularly those described in Section I, Materials and Methods, may be carried out using a network of 1,000 spiking neurons described in detail in a published article by the inventor of the present invention, entitled "Polychronization: Computation with Spikes," Neural Computation 18:245-282, by Eugene M. Izhikevich, 2006, pgs. 245-282. This article in its appendix entitled "Appendix: The Model," at pages 274-278 describes the MATLAB and C code, which appendix and its computer code are incorporated by reference herein in their entirety. The computer code also is described in the published article, available on the author's webpage, www.izhikevich.com, since prior to Dec. 29, 2006, the priority date of the present application.
The network has 80% excitatory neurons of the regular spiking (RS) type and 20% inhibitory neurons of the fast spiking (FS) type, representing the layer ⅔ part of a cortical minicolumn. Neurons are randomly connected with 10% probability so that there are 100 synapses per averaged neuron. The connections, combined with the random input simulating noisy miniature PSPs, make neurons fire Poisson-like spike trains with an average frequency around 1 Hz. This low frequency of firing is important for the low probability of sequential spikes to fall within the STDP time window by chance (the firing rate in neocortical layer ⅔ is much less than 1 Hz). The maximal axonal conduction delay is taken to be 1 ms. Each excitatory synapse is modified according to Eqs. (1) and (2) given above, with STDP depicted in FIG. 1(b), but the weights are limited to the range 0 to 4 mV (i.e., clipped at 0 and 4 mV). Both excitatory-to-excitatory and excitatory-to-inhibitory synaptic connections are subject to the same STDP rule. A different, more physiological STDP rule could be used for the latter connections, or even keep them fixed (non-plastic). The choice described in the present specification was done for the sake of simplicity and to be consistent with previous implementations of the spiking model as described in the above-mentioned published article by the present inventor. Inhibitory synapses are not plastic in the model. The LTD area shown in FIG. 1(b) is 50% greater than the LTP area shown in FIG. 1(b) so that uncorrelated firing of any two neurons results in the decrease of synaptic strength between them. As a result of spontaneous activity, the strengths of excitatory synapses in the network converge to the exponential distribution depicted in the inset in FIG. 1(d). As illustrated, all synapses are much weaker than the maximal allowable value of 4 mV, and the majority is less than 0.1 mV.
III. Further Discussion of the Present Invention
A. Generally
Described above is a biologically realistic implementation of what is known as Pavlovian and instrumental conditioning, and some aspects of temporal difference (TD) reinforcement learning using a spiking network with DA-modulated STDP. Based on prior experimental evidence that DA modulates classical LTP and LTD, it has been assumed that DA has a permissive, enabling effect allowing STDP to take place—a testable assumption that is believed not to have been suggested before. Although STDP acts on a millisecond time scale, the slow biochemical kinetics of synaptic plasticity could make it sensitive to DA rewards delayed by seconds. In the discussion above the spiking network is interpreted as representing a small part of the prefrontal cortex receiving numerous dopaminergic projections from the midbrain and projecting to the midbrain, though this theory can be applied to neostriatum and basal ganglia as well. The described simulations provide a neurally plausible mechanism of how associations between cues, actions, and delayed rewards are learned (FIGS. 1-3), as well as how DA response shifts from unconditional stimuli US to reward-predicting conditional stimuli CS (FIG. 4).
B. Spiking Implementation of Reinforcement Learning
Spiking implementation of reinforcement learning has been suggested in the art, and there are many models based on synaptic eligibility traces c (shown in FIG. 1(c)). All of these models are believed to have a common drawback: they require the network to be quiet during the waiting period to the reward; see arrow labeled "reward" in FIG. 1(c), with the illustrated waiting period occurring from the coincident firings of the pre, post neurons indicated by the blue rectangle to the time a step increase in the amount of extracellular dopamine d is delivered. Indeed, random neuronal activity during the waiting period triggers synaptic transmission in all synapses, alters the eligibility traces, and impedes learning. In contrast, STDP is insensitive to random firings during the waiting period but sensitive only to precise firing patterns. Since the set of precise patterns is sparse in the space of all possible firing patterns, DA-modulated STDP takes advantage of this fact and renders a superior mechanism of reinforcement learning.
Prior discussions consider explicitly the relationship between STDP and TD, but ask the opposite question: how to get STDP from TD acting on a millisecond time scale and how the resulting STDP depends on the dendritic location? In this sense, the results of the present invention are complementary to those of these prior discussions.
C. Synaptic Eligibility Traces
The slow kinetics of synaptic plasticity, modeled by the variable c (see Eq. (1)), results in the existence of synaptic eligibility traces c. This idea is known in the art of classical machine learning algorithms, where eligibility traces are assigned to cues and actions, as in the TD(λ) learning rule. To make the machine learning algorithms work, the network needs to know in advance the set of all possible cues and actions that may lead to the reward. In contrast, there is a combinatorially large number of possible spike-timing patterns that could trigger STDP and which could represent unspecified cues and actions of the spiking network; see the above-mentioned published article by the present inventor. Any one of them can be tied to the reward by the environment or by the experimenter, and the network can figure out which one on its own is tied to the reward, using a more biologically plausible way than TD(λ) or other machine learning algorithms do.
D. Spiking Implementation of TD
The model described in the present specification shows a possible spiking network implementation of some aspects of temporal difference (TD) reinforcement learning: the shift of DA response from group US to reward-predicting group CS1, and group CS2 as shown in FIG. 4. This property is not built in into the model, but appears spontaneously when synapses are allowed onto VTA-projecting neurons to be affected by DA the same way as any other synapses in the network. Thus, the shift is a general property of DA-modulated STDP applied to synaptic circuits projecting to VTA. The mechanism of the shift is quite unexpected: it takes advantage of the sensitivity of STDP to the fine temporal structure of the firing of group US, CS, and VTA-projecting neurons during the presentation of CS, as was explained above in connection with FIG. 4(e).
It may be noted that the DA response described in relation to FIG. 4 is not a true error prediction signal required by TD algorithms because the model fails to exhibit depression of firing rate (dip) in the activity of the VTAp group when group US is omitted. On the one hand, the depression would not be expected because the intervals between groups CS1, CS2 and US are random. However, the depression would not occur even if the intervals were fixed, because there is no internal clock or anticipatory signal that tells the network when US is expected. To get the depression of firing rate, the group US anticipatory signal generated by the caudate nucleus and globus pallidus of the brain could be simulated, and inhibitory neurons stimulated at the moment the US is expected to arrive. Also, it should be noted that the DA response described in relation to FIG. 4 does not exhibit a gradual shift in latency, as predicted by known TD models, but jumps from group US to reward-predicting groups CS1, CS2, which is more consistent with the effects observed in in vivo experiments. Consistent with these recordings, the DA response to US in FIG. 4 does not diminish completely, but remains above a baseline level. Finally, an unexpected presentation of the US after training would result in a diminished DA response in the model because the synaptic connections US→VTAp are depressed, i.e., the association is unlearned, in contrast to in vivo recordings showing a strong response. Thus, DA-modulated STDP is sufficient to reproduce some aspects of TD reinforcement learning in biologically relevant terms of spiking activity and synaptic plasticity, but not all aspects. To address all aspects, the network architecture would need to be refined and anatomical loops introduced similar to those of basal ganglia of the brain.
E. Spiking vs. Mean-Firing Rate Models
The results described in the present specification emphasize the importance of precise firing patterns in brain dynamics: the mechanism presented in this specification works only when reward-predicting stimuli correspond to precise firing patterns. Only synchronous patterns embedded into the sea of noise are considered, but the same mechanism would work equally well for polychronous firing patterns, i.e., time-locked but not synchronous. Interestingly, rate-based learning mechanisms would fail to reinforce the patterns. Indeed, presentation of a cue, such as group S1 shown in FIG. 2, does not increase the firing rate of any neuron; it just adds, removes, or changes the time of a single spike of each of the 50 neurons in S1. In particular, the neurons continue to fire Poissonian-looking spike trains with 1-2 spikes per second. The information about the stimulus is contained only in the relative timings of spikes, which are seen as vertical stripes in FIG. 2, and which are effective to trigger STDP. A mean firing rate description of the same network would result in neuronal activities having constant values, corresponding to constant firing rates, with no possibility to know when stimulation occurs.
Interestingly, DA-modulated STDP, it is believed, would fail to reinforce firing rate patterns. Indeed, large firing rate fluctuations produce multiple coincident spikes with random pre-post neuron order, so STDP dominated by LTD would result in the average depression of synaptic strength. Thus, even when the firing coincidences of pre, post neurons are not rare, STDP can still decouple chance coincidences due to rate-based dynamics from causal pre-post neuron relations due to spike-timing dynamics. This is how DA-modulated STDP differs from rate-based learning rules and this is why it is so effective to selectively reinforce precise firing patterns, but insensitive to firing rate patterns.
F. Rewards vs. Punishments
The present invention may be used to model not only rewards but also punishments. Indeed, the variable d may be treated as a concentration of extracellular DA above a certain baseline level. In this case, negative values of d, interpreted as concentrations below the baseline, result in active unlearning of firing patterns, that is, in punishments. Another way to implement punishment is to assume that DA controls only the LTP part of STDP. In this case, the absence of a DA signal results in overall depression of synaptic connections (punishment), certain intermediate values of DA result in an equilibrium between LTD and LTP parts of STDP (baseline), and strong DA signals result in potentiation of eligible synaptic connections (reward). There is anecdotal evidence in the art that the STDP curve has a very small LTP part in the prefrontal and motor cortices of the brain which affect personal communication. The model described in the present specification makes a testable prediction that the STDP curve will look quite different if DA is present during or immediately after the induction of synaptic plasticity.
IV. Conclusion
DA modulation of STDP provides a solution to the distal reward/credit assignment problem: Only nearly-coincident spiking patterns are reinforced by rewards, while uncorrelated spikes during the delay period to the reward do not affect the eligibility traces (variables c), and hence are ignored by the network. In contrast to previous theoretical studies, (1) the network does not have to be quiet during the waiting period to the reward, and (2) reward-triggering patterns do not have to be retained by recurrent activity of neurons. If a spiking pattern out of a potentially unlimited repertoire of all possible patterns consistently precedes or triggers rewards (even seconds later), the synapses responsible for the generation of the pattern are eligible for modification when the rewards arrive and the pattern is consistently reinforced (credited). Even though the network does not know what pattern was credited, it is more likely to generate the same pattern in the same behavioral context in the future.
Consequently, a computer simulated neural network based on the algorithms described above of the present invention can selectively reinforce reward-triggering precise firing patterns even if these firing patterns are embedded into a sea of noise and even if the rewards are delayed by seconds. Thus, known brain-based devices (BBDs) including robotic BBDs which are controlled by simulated nervous systems and operate in a real-world environment, can have their simulated nervous systems modified in accordance with the algorithms and code of the present invention. As so modified, with the behavior of a BBD being governed by precise firing patterns in the simulated nervous system and some patterns (i.e. some real-world actions) bringing rewards, the synaptic connections between the neurons of the computer simulated nervous system generating these firing patterns are strengthened, so that the BBD is more likely to exhibit the same behavior in the same context in the future. Thus, the methods of the present invention are biologically plausible and simple to implement in simulations and, if desired, in special-purpose hardware. The methods of the present invention can then be implemented to be a part of every spiking neural network designed to control a robot capable of operating in and learning its real-world environment through rewards and punishments.
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