Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity

TLDR

Neurons in behaving animals display temporally irregular spiking, and the origin and functional implications of this irregularity remain unknown. The study tests whether temporal variability in neuronal firing arises from an approximate balance of excitatory and inhibitory inputs. Large, sparsely connected networks of excitatory and inhibitory neurons with strong synapses naturally generate this balance. The balanced network exhibits strongly chaotic dynamics even with constant external input, yet responds linearly and adapts to changing stimuli on timescales shorter than a neuron's integration time.

Abstract

Neurons in the cortex of behaving animals show temporally irregular spiking patterns. The origin of this irregularity and its implications for neural processing are unknown. The hypothesis that the temporal variability in the firing of a neuron results from an approximate balance between its excitatory and inhibitory inputs was investigated theoretically. Such a balance emerges naturally in large networks of excitatory and inhibitory neuronal populations that are sparsely connected by relatively strong synapses. The resulting state is characterized by strongly chaotic dynamics, even when the external inputs to the network are constant in time. Such a network exhibits a linear response, despite the highly nonlinear dynamics of single neurons, and reacts to changing external stimuli on time scales much smaller than the integration time constant of a single neuron.