Control of noise-induced coherent oscillations in three-neuron motifs

Abstract

The phenomenon of self-induced stochastic resonance (SISR) requires a nontrivial scaling limit between the deterministic and the stochastic timescales of an excitable system, leading to the emergence of coherent oscillations which are absent without noise. In this paper, we numerically investigate SISR and its control in single neurons and three-neuron motifs made up of the Morris–Lecar model. In single neurons, we compare the effects of electrical and chemical autapses on the degree of coherence of the oscillations due to SISR. In the motifs, we compare the effects of altering the synaptic time-delayed couplings and the topologies on the degree of SISR. Finally, we provide two enhancement strategies for a particularly poor degree of SISR in motifs with chemical synapses: (1) we show that a poor SISR can be significantly enhanced by attaching an electrical or an excitatory chemical autapse on one of the neurons, and (2) we show that by multiplexing the motif with a poor SISR to another motif (with a high SISR in isolation), the degree of SISR in the former motif can be significantly enhanced. We show that the efficiency of these enhancement strategies depends on the topology of the motifs and the nature of synaptic time-delayed couplings mediating the multiplexing connections.

Dynamics and Information Import in Recurrent Neural Networks

“…Furthermore, we found a completely new resonance phenomenon which we call import resonance , showing that the correlation or mutual information between input and the subsequent network state depends on certain control parameters (such as coupling strength) in a peak-like way. Resonance phenomena are ubiquitous not only in simplified neural network models (Ikemoto et al, 2018 ; Krauss et al, 2019a ; Bönsel et al, 2021 ), but also in biologically more realistic systems (McDonnell and Abbott, 2009 ), where they show up in diverse variants such as coherence resonance (Lindner and Schimansky-Geier, 2000 ; Gu et al, 2002 ; Lindner et al, 2002 ), finite size resonance (Toral et al, 2003 ), bimodal resonance (Mejias and Torres, 2011 ; Torres et al, 2011 ), heterogeneity-induced resonance (Mejias and Longtin, 2012 , 2014 ), or inverted stochastic resonance (Buchin et al, 2016 ; Uzuntarla et al, 2017 ). They have been shown to play a crucial role for neural information processing (Moss et al, 2004 ; Krauss et al, 2018 ; Schilling et al, 2020 ).…”

Intrinsic Noise Improves Speech Recognition in a Computational Model of the Auditory Pathway

“…Our study provides evidence that an interplay of deep learning and neuroscience helps on the one hand to raise understanding of the function of biological neural networks and cognition in general (e.g., Schilling et al, 2018 , 2021b ; Krauss et al, 2019a , c , d , 2021 ; Gerum et al, 2020 ; Krauss and Maier, 2020 ; Bönsel et al, 2021 ; Metzner and Krauss, 2022 ), an emerging science strand referred to as cognitive computational neuroscience ( Kriegeskorte and Douglas, 2018 ). On the other hand, fundamental processing principles from nature—such as stochastic resonance—can be transferred to improve artificial neural systems, which is called neuroscience-inspired AI ( Hassabis et al, 2017 ; Gerum et al, 2020 ; Gerum and Schilling, 2021 ; Yang et al, 2021 ; Maier et al, 2022 ).…”

Predictive coding and stochastic resonance as fundamental principles of auditory perception

“…Our integrated model of auditory (phantom) perception demonstrates that the fusion of computational neuroscience, AI, and experimental neuroscience leads to innovative ideas and paves the way for further advances in neuroscience and AI research. For instance, novel evaluation techniques for neurophysiological data based on AI and Bayesian statistics were recently established [156][157][158][159], the role of noise in neural networks and other biological information processing systems was considered in [160][161][162][163], and the benefit and application of noise and randomness in Machine Learning approaches was further investigated in [43,164,165]. On the one hand, the fusion of these complementary fields may evince the neural mechanisms of tinnitus (CCN, [63]) and information processing principles that underwrite functional brain architectures.…”