Figure 1: Categorization task and the neural circuit model.
From: Choice-correlated activity fluctuations underlie learning of neuronal category representation
(a) A set of 12 motion direction stimuli is divided into two categories, C1 and C2 (red and blue arrows), separated by a category boundary (black dashed line). On each trial, one randomly chosen motion stimulus is presented, and the model learned through trial and error to indicate its category membership. (b) Schematic of the circuit model. The network comprises a sensory (MT), an association (LIP) and a decision neural circuits. Neurons in the sensory circuit are tuned to motion directions (indicated by arrows). They receive directional bottom-up inputs and provide inputs to the association neurons through feedforward synapses (cS→A). The decision circuit (C1 and C2 populations) pools activity of association neurons through feedforward synapses (cA→D) and generates a category decision through competitive attractor dynamics. The model has feedback connections from the decision to association neurons (cD→A). All synaptic connections between the local circuits undergo Hebbian plasticity modulated by a reward prediction error signal. (c) An example network activity before categorization training. A motion direction stimulus (195°) is presented for 1 s (grey bar). The sensory and association neurons show direction-tuned responses in their spatiotemporal activity patterns (lower and middle panels, respectively). x axis, time; y axis, neurons labelled by the preferred direction; firing rate is colour-coded. The decision circuit generates categorical choice through a winner-take-all competition between the C1 and C2 populations (upper panel). Firing rates of the C1 and C2 populations are shown for two trials, where C1 (red line) and C2 (blue line) choice was made for the same stimulus.
