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Title: Using an Insect Mushroom Body Circuit to Encode Route Memory in Complex Natural Environments}
Article:
Abstract:
Ants, like many other animals, use visual memory to follow extended routes through complex environments, but it is unknown how their small brains implement this capability. The mushroom body neuropils have been identified as a crucial memory circuit in the insect brain, but their function has mostly been explored for simple olfactory association tasks. We show that a spiking neural model of this circuit originally developed to describe fruitfly( Drosophila melanogaster) olfactory association, can also account for the ability of desert ants( Cataglyphis velox) to rapidly learn visual routes through complex natural environments. We further demonstrate that abstracting the key computational principles of this circuit, which include one-shot learning of sparse codes, enables the theoretical storage capacity of the ant mushroom body to be estimated at hundreds of independent images.
Introduction:
The nature of the spatial memory that underlies navigational behaviour in insects remains a controversial issue, particularly as the neural mechanisms are largely unknown. Insects can perform path integration( PI), using a sky compass and odometer to accumulate velocity into a vector indicating the distance and direction of their start location, typically the nest or hive[1]. They are also known to be able to use landmark and/or panoramic visual memories of previously visited locations to guide their movements independently of PI[2, 3]. Under normal conditions, both systems are functioning. This raises the possibility that insects additionally store PI vector information with their visual memories[4]; or link their visual memories in sequences[5] or with relative heading vectors[6], forming a topological map; or could even use the PI information to integrate their visual memories into a metric map that represents the spatial relationship of known locations[7]. However another possibility is that PI information is used to determine which visual memories to store, for example, the views experienced when facing the nest[8]. Subsequently, such memories can be used directly for guidance without further reference to vector information. Rotating to match the current visual experience with a stored view will give the required heading direction[9], e. g., towards the nest. Surprisingly, this navigation mechanism can exploit multiple memories without necessarily requiring recovery of the ‘correct’ memory for the current location. Baddeley et al[10, 11] presented an algorithm by which an animal attempting to navigate home simultaneously compares the view experienced while it rotates to all memories ever stored while following a PI vector homewards. The direction in which the view looks ‘most familiar’, i. e., has the best match across all stored views, is generally the correct heading to take to retrace its previous path. In[11] this principle was implemented using the Infomax learning algorithm to train the weights in a two-layer network, where the input is successive images along simulated routes, and the summed activation of the output layer represents the novelty of each image. This implementation was able to replicate many features of ant route following in an agent simulation[11] and has also been shown( with some assumptions about the ant’s previous experience) to produce similar search strategies to ants in visual homing paradigms[12]. Strategies of this nature, where the animal does not need to know where it is to know where to go, have been invoked as a more parsimonious explanation for experimental results presented as evidence for a cognitive maps in insects[13]. But is this explanation of navigation plausible, given realistic environmental, perceptual and neural constraints? As pointed out in a recent review[14], parsimony based on what appears simple in computational terms may not map to simplicity with respect to the underlying neural architecture. Yet so far, “nothing is known about neural implementation of navigational space in insects”[14]. In particular, Baddeley et al[11] do not claim that the Infomax implementation of their familiarity algorithm represents the actual neural processing of the ant. Visual processing in the ant brain has not been extensively studied, but anatomically resembles that of other insects in terms of the initial sensory layers at least. Ants have typical apposition compound eyes, but the size and resolution varies substantially across species. For the desert ants Cataglyphis[15] and Melaphorus bagoti[16] each eye subtends a large visual field( estimated at 150–170 degrees horizontal extent, with a small frontal overlap) with low visual resolution( interommatidia angles between 3 and 5 degrees). Visual signals pass through the three layers of the optic lobe( lamina, medulla and lobula) maintaining a retinotopic projection but with increasing integration across the visual field[17]. Visual signals then pass directly or indirectly to several other brain regions, including the protocerebrum, the central complex, and the calyxes of the mushroom bodies[17]. The mushroom body( MB) neuropils are conspicuous central brain structures, made up from a large number of Kenyon cells( KCs), whose dendrites together form the calyx and whose axons run in parallel through the penduculus and then bifurcate to form the vertical( or α) and medial( or β) lobes[18]. The extent of visual input to the MB is significantly greater in ants and other hymenoptera than for many other insects for which the MB input is predominantly olfactory[19]. In fact there is increasing evidence that the MB may play a role in visual learning and navigation in insects. There is evidence of expansion or reorganisation of MB at the onset of foraging in ants[20, 21] and bees[22–24]. Upregulation of a learning related gene in the MB of honeybees has been linked to orientation flights in novel environments[25]. Cockroaches show impairment on a visual homing task after MB silencing[26], although the central complex rather than the MB appears essential for this task in Drosophila[27]. The MB may nevertheless play a role in some visual learning paradigms in Drosophila[28][29]. To date, the MB have been much more extensively studied in the context of olfactory associative learning, for which they appear crucial[18]. We have previously implemented a spiking neuron model of adult Drosophila MB olfactory learning[30] which used three stages of processing. Olfactory inputs produced a spatio-temporal pattern( an ‘image’ of the odour) in the antennal lobe( consistent with evidence in flies[31] but also observed in many other insects, including ants[32]). Divergent connectivity from the antennal lobe to the much larger number of KCs that make up the MB project this pattern onto a sparse encoding in a higher dimensional space[33–35]. Reward-dependent learning occurs in the synapses between the KC and a small number of output extrinsic neurons( ENs)[36–38], depending on the delivery at the synapse of an aminergic reward signal[39, 40], such that each pattern becomes associated with a positive or negative outcome. This is a simplification of the insect MB circuit, which in reality includes significant feedback connectivity, synaptic adaptation at other levels including in the calyx[41, 42] and has a substantial compartmentalisation of its inputs and outputs both between and within the lobes[40]. Nevertheless the basic feedforward architecture implemented in our model was shown to be sufficient to support learning of the association of non-elemental( configural) olfactory stimulus patterns to a reinforcement signal. Our proposal here is that the MB of the ant allows it to similarly associate visual stimulus patterns, as viewed along a route, with the ‘reinforcement’ of facing, moving towards or reaching home. In fact the learnt pattern could be multimodal( e. g., including olfactory cues, see Discussion) but we focus here on demonstrating that the ecologically realistic visual learning task posed by route following could be achieved by this circuit architecture. In other words, we suggest the neural architecture of the ant MB could plausibly form the substrate for the familiarity algorithm of Baddeley et al[11]. The complexity of navigation tasks make it difficult to directly measure or manipulate neural circuits in ants under naturalistic conditions to evaluate their contribution to navigation behaviour. Instead, we take a modelling approach, and explore whether our previously developed spiking neural simulation of olfactory learning in the MB of Drosophila[30] could be directly applied to the complex ecological task of route memory in ants, using realistic stimuli derived directly from our field experiments.
Discussion:
We have shown that the neural architecture of the insect mushroom body can implement the ‘familiarity’ algorithm[11] for ant route following. In our simulation, the MB learns over 80 on-route images, reconstructed from real ant viewpoints in their natural habitat, and reliably distinguishes them from the highly similar images obtained when looking off-route. This is the first model of visual navigation in insects to draw such a close connection to known neural circuits, rather than appealing to abstract computational capacities; as a result we found that a simpler associative learning network( computationally equivalent to a Willshaw net[50]) than Infomax suffices for the task. Strikingly, the network which was originally developed for modelling simple olfactory associative learning in flies required almost no modification other than increased size to perform the apparently much more complex function of supporting navigation under realistic conditions. Ants( and other navigating insects such as bees) are known to have significantly enlarged MB compared to flies[57]. Visual memory has also been localised to the central complex of the insect brain[27, 58], and it remains possible that navigation, including visual memory for homing routes, is carried out entirely in the central complex and does not involve the MB. We would agree that the central complex is likely to be a key area for navigational capabilities in many insects[27, 59], in particular processing polarised light[60], and potentially for path integration. However the extreme memory demands of extended route following in hymenoptera such as ants and bees may have co-opted the unique circuit properties of the MB: a divergent projection of patterns into a sparse code across a very large array of neurons, enabling separation and storage of a large number of arbitrary, similar patterns. Such an architecture appears to be unique to the MB neuropils in the insect brain, and has interesting parallels to the cerebellum in vertebrates[61]. Although we have used whole images in our simulation, we believe the approach is neutral to the issue of whether insects actually use the whole panorama, the skyline, the ground pattern, optic flow, or salient landmarks. Indeed there is no reason why the patterns stored should represent only one modality at a time. The multimodal inputs to the MB in ants[62] suggest that the KC activation pattern might combine olfactory, visual and other sensory( and possibly proprioceptive or motivational) inputs into a gestalt experience of the current location, and alteration of any of these cues might reduce the familiarity. Manipulation of olfactory[63], wind[64] and tactile[65] cues have been shown to affect navigational memory in ants. The network we have described simply stores patterns, rather than trying to learn an underlying generative model to classify ‘on-route’ vs ‘off-route’ memories. Nevertheless, in common with many learning problems, the ideal visual memory for route following has an over-fitting/generalization tradeoff. If the memory for individual views is too specific, then any small displacement from the route will result in no direction looking familiar and the animal being lost; and if the similar views resulting from small displacements could be stored as ‘one class’, the environmental range of a limited capacity memory would be increased. However, if parameters( or the learning rule) are adjusted to allow greater generalization to similar views, there will be increased aliasing and hence risk of mistaking a new view for a familiar one, which would result in moving in the wrong direction, with potentially fatal consequences. One way to deal with this problem might be to introduce more sophisticated visual processing. In particular, a more effective vPN-KC mapping than the random connectivity assumed here might be obtained by allowing self-organisation( unsupervised learning) in response to visual experience in the natural environment[66] so that only ‘useful’ correlations( for example features that vary strongly with rotation but not displacement[67]) are preserved. Evidence for adaptive synapses at this level of the circuit exists for bees[41] and flies[42]. More complex pre-processing might also be needed to deal with some aspects of the real performance of ants, who continue to follow a route under changing light conditions and movement of vegetation by wind, with some robustness to changing landmark information, and probably also pitch and roll of the head[68, 69]. Similarly, we recognise there is likely to be more complexity to the ant’s use of PI and visual memory than the algorithm presented here suggests. For example, it would be necessary to postulate a separate memory to explain the outwards routes of ants, with acquisition guided by successful progress along an outbound vector that is expected to lead to a food source. Insects also appear to acquire visual memory during learning walks and flights, by turning back to view the nest[8, 70, 71]. In this case we would need to assume that the reinforcement signal is under more complex control of a motor programme that maintains or generates fixations in the nest direction. Another possibility is that memory storage is triggered by significant change in the view. Interestingly, this is already inherently determined by the use of a three-factor learning rule in our MB model. If a view is already sufficiently ‘familiar’, the EN will not fire in response to the KC activation, so the synapses will not be tagged and learning will not occur. The model we present provides a neurophysiological underpinning for the claim that ants can perform route navigation without requiring a map and for assessing the range over which views can provide guidance. We should be cautious to extend results from ants to other insects, particularly bees, whose sensorimotor habitat is significantly different and might thus have developed different strategies for navigation. However, although it might seem difficult to account for novel routes( or short cuts) ever being taken using the familiarity algorithm, great care is needed to distinguish a truly novel route from the behaviour that may emerge from an animal continuously orienting to make the best visual memory match possible given its current location and stored memories. This can include visual homing within a catchment area larger than that actually explored by the animal[12, 72, 73]. We also note that though PI is not used in route guidance in our simulations, it will normally still be active in the animal, with potential to influence the direction taken. In particular, recent results have shown that conflicting visual and PI cues often result in a compromise direction being taken by ants[73–77]. Under some circumstances, the result could appear to be a novel shortcut.
We propose a model based directly on insect neuroanatomy that is able to account for the route following capabilities of ants . We show this mushroom body circuit has the potential to store a large number of images , generated in a realistic simulation of an ant traversing a route , and to distinguish previously stored images from highly similar images generated when looking in the wrong direction . It can thus control successful recapitulation of routes under ecologically valid test conditions .