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

Engelken, Rainer; Ingrosso, Alessandro; Khajeh, Ramin; Goedeke, Sven; Abbott, L. F.

doi:10.48550/arxiv.2201.09916

Cited by 2 publications

(2 citation statements)

References 30 publications

(106 reference statements)

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“…Previous theoretical work found a noise-induced suppression of chaos in random neural networks driven by time-varying inputs both in discrete time [28] and continuous time [27, 29, 24, 22, 30, 24]. In previous cases, featuring a mean synaptic input centered in the middle of the high-gain region of the transfer function, suppression of chaos occurs because an increase in the variance drives the network away from the chaotic regime.…”

Section: Discussionmentioning

confidence: 99%

Baseline control of optimal performance in recurrent neural networks

Ogawa

Fumarola

Mazzucato

2022

Preprint

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Changes in behavioral state, such as arousal and movements, strongly affect neural activity in sensory areas. Recent evidence suggests that they may be mediated by top-down projections regulating the statistics of baseline input currents to sensory areas, inducing qualitatively different effects across sensory modalities. What are the computational benefits of these baseline modulations? We investigate this question within a brain-inspired framework for reservoir computing, where we vary the quenched baseline inputs to a random neural network. We found that baseline modulations control the dynamical phase of the reservoir network, unlocking a vast repertoire of network phases. We uncover a new zoo of bistable phases exhibiting the simultaneous coexistence of fixed points and chaos, of two fixed points, and of weak and strong chaos. Crucially, we discovered a host of novel phenomena, including noise-driven enhancement of chaos and ergodicity breaking; neural hysteresis, whereby transitions across phase boundary retain the memory of the initial phase. Strikingly, we found that baseline control can achieve optimal performance without any fine tuning of recurrent couplings. In summary, baseline control of network dynamics opens new directions for brain-inspired artificial intelligence and provides a new interpretation for the ubiquitously observed behavioral modulations of cortical activity.

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Section: Discussionmentioning

confidence: 99%

Baseline control of optimal performance in recurrent neural networks

Ogawa

Fumarola

Mazzucato

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Noise-induced enhancement of chaos. Previous theoretical work found a noise-induced suppression of chaos in random neural networks driven by time-varying inputs both in discrete time [28] and continuous time [27,29,24,22,30,24]. In previous cases, featuring a mean synaptic input centered in the middle of the high-gain region of the transfer function, suppression of chaos occurs because an increase in the variance drives the network away from the chaotic regime.…”

Section: Discussionmentioning

confidence: 99%