Non-random connections in our brain

Researchers on the project NONRANDOM CIRCUITS (Origin and function of nonrandom cortical connectivities) used simplified assumptions to understand the dynamics of neurons and the way they connect with each other. Their idea was to identify how the brain processes information via local circuits, which are nothing but densely interconnected networks of several thousand neurons.

Researchers refined the concept of random neuron connections to study non-trivial statistical patterns where there is overrepresentation of bidirectional connections. In other words, pre- and post-synaptic neurons are more likely to connect to each other if they have previously connected to each other. For measurement, they correlated pair-connecting strengths, which refers to clustering between strongly connecting neurons in a network.

Project experiments provided important insight into the effect of correlated weights on the dynamics of cortical circuits. The importance of eigenvector structure in addition to data on the eigen spectrum was highlighted for better insight into neuronal network dynamics. Several key findings were made that will shortly be disseminated via a manuscript.

The NONRANDOM CIRCUITS study has made significant inroads into understanding non-random connectivity patterns in the cortical circuits of the brain. Results have opened up more avenues for exploration and should foster further research collaborations in the area of computational and theoretical neuroscience.