There was a time when the neuroscience world was consumed by the question of how individual neurons were coding information about the world. Was it in the average firing rate? Or did every precise spike matter, down to the millisecond? Was it, potentially, more complicated?
Like everything else in neuroscience, the answer was resolved in a kind of “it depends, it’s complicated” kind of way. The most important argument against the role of precise spike timing is noise. There is the potential for noise in sensory input, noise between every synapse, noise at every neuron. Why not make the system robust to this noise by taking some time average? On the other hand, if you want to respond quickly you can’t take too much time to average – you need to respond!
Much of the neural coding literature comes from sensory processing where it is easy to control the input. Once you get deeper into the brain, it becomes less clear how much of what the neuron is receiving is sensory and not some shifting mass.
The field has shifted a bit with the rise of calcium indicators which allow imaging the activity of large population of neurons at the expense of timing information. Not only does it sacrifice precise timing information but it can be hard to get connectivity information. Plus, once you start thinking about networks the nonlinear mess makes it hard to think about timing in general.
The straightforward way to decide whether a neuron is using the specific timing of each spike to mean something is to ask whether that timing contains any information. If you jitter the precise position of any given spike my 5 milliseconds, 1 millisecond, half a millisecond – does the neural code become more redundant? Does this make the response of the neuron any more random at that moment in time than it was before?
Just show an animal some movie and record from a neuron that responds to vision. Now show that movie again and again and get a sense of how that neuron responds to each frame or each new visual scene. Then the information is just how stereotyped the response is at any given moment compared to normal, how much more certain you are at that moment than any other moment. Now pick up a spike and move it over a millisecond or so. Is this within the stereotyped range? Then it probably isn’t conveying information over a millisecond. Does the response become more random? Then you’ve lost information.
But these cold statistical arguments can be unpersuasive to a lot of people. It is nice if you can see a picture and just understand. So here is the experiment: songbirds have neurons which directly control the muscles for breathing (respiration). This provides us with a very simple input/output system, where the input is the time of a spike and the output is the air pressure exerted by the muscle. What happens when we provide just a few spikes and move the precise timing of one of these spikes?
The beautiful figure above is one of those that is going directly into my bag’o’examples. What it shows is a sequence of three induced spikes (upper right) where the time of the middle spike changes. The main curves are the how the pressure changes with the different timing in spikes. You can’t get much clearer than that.
Not only does it show, quite clearly, that the precise time of a single spike matters but that it matters in a continuous fashion – almost certainly on a sub-millisecond level.
The twitter thread on this post ended up being useful, so let me clarify a few things. First, the interesting thing about this paper is not that the motor neurons can precisely control the muscle; it is that when they record the natural incoming activity, it appears to provide information on the order of ~1ms; and the over-represented patterns of spikes include the patterns in the figure above. So the point is that these motor neurons are receiving information on the scale of one millisecond and that the information in these patterns has behaviorally-relevant effects.
Some other interesting bits of discussion came up. What doesn’t use spike-timing information? Plenty of sensory systems do; I thought at first that maybe olfaction doesn’t but of course I was wrong. Here’s a hypothesis: all sensory and motor systems do (eg, everything facing the outside world). (Although, read these papers). When would you expect spike-timing to not matter? When the number of active input neurons are large and uncorrelated. Does spike timing make sense for Deep Networks where the neurons are implicitly representing firing rates? Here is a paper that breaks it down into rate and phase.
Srivastava KH, Holmes CM, Vellema M, Pack AR, Elemans CP, Nemenman I, & Sober SJ (2017). Motor control by precisely timed spike patterns. Proceedings of the National Academy of Sciences of the United States of America, 114 (5), 1171-1176 PMID: 28100491
Nemenman I, Lewen GD, Bialek W, & de Ruyter van Steveninck RR (2008). Neural coding of natural stimuli: information at sub-millisecond resolution. PLoS computational biology, 4 (3) PMID: 18369423