Abstract
A central goal in systems neuroscience is to understand the functions performed by neural circuits. Previous top-down models addressed this question by comparing the behaviour of an ideal model circuit, optimised to perform a given function, with neural recordings. However, this requires guessing in advance what function is being performed, which may not be possible for many neural systems. Here, we propose an alternative approach that uses recorded neural responses to directly infer the function performed by a neural network. We assume that the goal of the network can be expressed via a reward function, which describes how desirable each state of the network is for carrying out a given objective. This allows us to frame the problem of optimising each neuron’s responses by viewing neurons as agents in a reinforcement learning (RL) paradigm; likewise the problem of inferring the reward function from the observed dynamics can be treated using inverse RL. Our framework encompasses previous influential theories of neural coding, such as efficient coding and attractor network models, as special cases, given specific choices of reward function. Finally, we can use the reward function inferred from recorded neural responses to make testable predictions about how the network dynamics will adapt depending on contextual changes, such as cell death and/or varying input statistics, so as to carry out the same underlying function with different constraints.
