Maximum entropy inference on weight distributions under context-dependent task constraints produces neuron populations with contextual gain modulation whose connectivity matches gradient-descent trained networks, with transitions to random structure as context count or weight scale increases.
Flexible Multitask Computation in Recurrent Networks Utilizes Shared Dynamical Motifs
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Balancing structure and randomness: maximum entropy networks for context-dependent computations
Maximum entropy inference on weight distributions under context-dependent task constraints produces neuron populations with contextual gain modulation whose connectivity matches gradient-descent trained networks, with transitions to random structure as context count or weight scale increases.