Rich dynamics caused by known biological brain network features resulting in stateful networks

TL;DR

This study investigates how known biological features of brain networks, including connectivity patterns and neuron properties, induce rich, state-dependent dynamics in recurrent neural networks, highlighting differences between sparse and dense connectivity.

Contribution

The paper demonstrates how intrinsic neuronal parameters and network connectivity influence dynamic state changes in biologically inspired recurrent networks.

Findings

Sparse networks exhibit more profound dynamic effects from parameter variations.

Both dense and sparse networks can generate multiple distinct states from the same input.

Neuron properties like inhibition and delays significantly affect network dynamics.

Abstract

The mammalian brain could contain dense and sparse network connectivity structures, including both excitatory and inhibitory neurons, but is without any clearly defined output layer. The neurons have time constants, which mean that the integrated network structure has state memory. The network structure contains complex mutual interactions between the neurons under different conditions, which depend on the internal state of the network. The internal state can be defined as the distribution of activity across all individual neurons across the network. Therefore, the state of a neuron/network becomes a defining factor for how information is represented within the network. Towards this study, we constructed a fully connected (with dense/sparse coding strategies) recurrent network comprising of both excitatory and inhibitory neurons, driven by pseudo-random inputs of varying frequencies. In this study we assessed the impact of varying specific intrinsic parameters of the neurons that enriched network state dynamics, such as initial neuron activity, amount of inhibition in combination with thresholded neurons and conduction delays. The impact was assessed by quantifying the changes in mutual interactions between the neurons within the network for each given input. We found such effects were more profound in sparsely connected networks than in densely connected networks. However, also densely connected networks could make use of such dynamic changes in the mutual interactions between neurons, as a given input could induce multiple different network states.