Self-Organized Structuring of Recurrent Neuronal Networks for Reliable Information Transmission

SIMPLE SUMMARY: Information processing in the brain takes places at multiple stages, each of which is a local network of neurons. The long-range connections between these network stages are sparse and do not change over time. Thus, within each stage information arrives at a sparse subset of input neurons and must be routed to a sparse subset of output neurons. In this theoretical work, we investigate how networks achieve this routing in a self-organized manner without losing information. We show that biologically inspired self-organization entails that input information is distributed to all neurons in the network by strengthening many synapses in the local networks. Thus, after successful self-organization, input information can be read out and decoded from a small number of outputs. We also show that this way of self-organization can still be more energy efficient than creating more long-range in- and output connections. ABSTRACT: Our brains process information using a layered hierarchical network architecture, with abundant connections within each layer and sparse long-range connections between layers. As these long-range connections are mostly unchanged after development, each layer has to locally self-organize in response to new inputs to enable information routing between the sparse in- and output connections. Here we demonstrate that this can be achieved by a well-established model of cortical self-organization based on a well-orchestrated interplay between several plasticity processes. After this self-organization, stimuli conveyed by sparse inputs can be rapidly read out from a layer using only very few long-range connections. To achieve this information routing, the neurons that are stimulated form feed-forward projections into the unstimulated parts of the same layer and get more neurons to represent the stimulus. Hereby, the plasticity processes ensure that each neuron only receives projections from and responds to only one stimulus such that the network is partitioned into parts with different preferred stimuli. Along this line, we show that the relation between the network activity and connectivity self-organizes into a biologically plausible regime. Finally, we argue how the emerging connectivity may minimize the metabolic cost for maintaining a network structure that rapidly transmits stimulus information despite sparse input and output connectivity.