Optimal cue combination and landmark‐stability learning in the head direction system. (5th October 2016) - Jeffery, Kate J.; Page, Hector J. I.; Stringer, Simon M.
Abstract :  Optimal cue combination using a ring attractor. (A) Hypothetical head direction cell ring attractor being activated by two cues, one highly reliable (cue 1, red) and one less reliable (cue 2, pink). According to classic attractor winner‐take‐all dynamics, cue 1 should capture activity in the ring. According to optimal cue combination theory, the resultant activation should be between the two activations, and closer to the stronger one. (B) The two scenarios could be reconciled if Gaussian inputs onto a ring attractor experience Hebbian plasticity in the overlap region, causing re‐weighting of the inputs, and a shift towards the other cue. (C) The final outcome of this re‐weighting process is that the strong cue captures activity in a winner‐take‐all fashion, but does so at a part of the ring attractor that is close to the other cue, as predicted by optimal cue combination theory. Abstract: Maintaining a sense of direction requires combining information from static environmental landmarks with dynamic information about self‐motion. This is accomplished by the head direction system, whose neurons – head direction cells – encode specific head directions. When the brain integrates information in sensory domains, this process is almost always 'optimal' – that is, inputs are weighted according to their reliability. Evidence suggests cue combination by head direction cells may also be optimal. The simplicity of the head direction signal, together with the detailed knowledge we have about the anatomy and physiology of the underlying circuit, therefore makes this system a tractable model with which to discover how optimal cue combination occurs at a neural level. In the head direction system, cue interactions are thought to occur on an attractor network of interacting head direction neurons, but attractor dynamics predict a winner‐take‐all decision between cues, rather than optimal combination. However, optimal cue combination in an attractor could be achieved via plasticity in the feedforward connections from external sensory cues (i.e. the landmarks) onto the ring attractor. Short‐term plasticity would allow rapid re‐weighting that adjusts the final state of the network in accordance with cue reliability (reflected in the connection strengths), while longer term plasticity would allow long‐term learning about this reliability. Although these principles were derived to model the head direction system, they could potentially serve to explain optimal cue combination in other sensory systems more generally.