Input correlations impede suppression of chaos and learning in balanced firing-rate networks.

• DOI: 10.48550/arxiv.2201.09916
• Publication Date: 2022-12-05
• Authors: Rainer Engelken; Alessandro Ingrosso; Ramin Khajeh; Sven Goedeke; L. F. Abbott
• Keywords: FOS: Computer and information sciences; 0301 basic medicine; Computer Science - Machine Learning; QH301-705.5; Models, Neurological; Action Potentials; FOS: Physical sciences; Machine Learning (cs.LG); 03 medical and health sciences; Learning; Biology (General); Neurons; Computational Neuroscience; 0303 health sciences; Disordered Systems and Neural Networks (cond-mat.dis-nn); Condensed Matter - Disordered Systems and Neural Networks; Nonlinear Sciences - Chaotic Dynamics; Networks, dynamical systems; Quantitative Biology - Neurons and Cognition; FOS: Biological sciences; Neurons and Cognition (q-bio.NC); Nerve Net; Chaotic Dynamics (nlin.CD); Research Article

Neural circuits exhibit complex activity patterns, both spontaneously and evoked by external stimuli. Information encoding and learning in neural circuits depend on how well time-varying stimuli can control spontaneous network activity. We show that in firing-rate networks in the balanced state, external control of recurrent dynamics, i.e., the suppression of internally-generated chaotic variability, strongly depends on correlations in the input. A distinctive feature of balanced networks is that, because common external input is dynamically canceled by recurrent feedback, it is far more difficult to suppress chaos with common input into each neuron than through independent input. To study this phenomenon, we develop a non-stationary dynamic mean-field theory for driven networks. The theory explains how the activity statistics and the largest Lyapunov exponent depend on the frequency and amplitude of the input, recurrent coupling strength, and network size, for both common and independent input. We further show that uncorrelated inputs facilitate learning in balanced networks.