FIG. 1.

Cartoon of theoretical framework. (a) We first specify some steady-state responses of a recurrent threshold-linear neural network receiving feedforward input. (b) We then find all synaptic weight matrices that have fixed points at the specified responses. Red (blue) matrix elements are positive (negative) synaptic weights. (c) When a weight is consistently positive (or consistently negative) across all possibilities, then the model needs a nonzero synaptic connection to generate the responses. We therefore make the experimental prediction that this synapse must exist. We also predict whether the synapse is excitatory or inhibitory.