< Back to Article

Cooperative coding of continuous variables in networks with sparsity constraint

Fig 2

Schematics of feedforward and cooperatively coding networks.

(A) Top: In the feedforward network, the response (gray solid curve) to an isolated input is fully generated by the neurons’ feedforward inputs (blue lines and dots, line thickness represents input strength). For the displayed RF width d = 2, five neurons receive feedforward input, so that the network response (gray solid curve) represents of the summed target response (gray dashed curve). Bottom: Feature and input neuron activities as in Fig 1. Outgoing feedforward synapses from the active input neuron j = 2 and incoming feedforward synapses to feature neuron i = 6 are shown in blue. (B) Top: In the cooperatively coding network model, the network response (gray solid curve) is the sum of feedforward input (blue line and dot) and recurrent input (brown-purple dashed curve). For the displayed case of an isolated input, only one neuron receives feedforward input, which induces a part of the stationary response of the most active feature neuron. The rest of the response and all other responses are induced by recurrent input from neighboring neurons. The total recurrent input that each feature neuron receives is the sum of recurrent input from the right (brown solid curve) and left neighbor (purple solid curve). Bottom: Each feature neuron receives one feedforward synapse (blue lines) and two recurrent synapses (black lines, all recurrent connections are bidirectional). (C) The RF of feature neuron i ( for varying j, gray solid curve) is the weighted sum (brown-purple dashed curve) of the RFs of its left (, purple) and right neighbors (, brown) plus a contribution from feedforward input (, blue line and dot). All shown RFs have width d = 2.

doi: https://doi.org/10.1371/journal.pcbi.1012156.g002