Generating Coherent Patterns of Activity from Chaotic Neural Networks

Sussillo, David; Abbott, L. F.

doi:10.1016/j.neuron.2009.07.018

Cited by 991 publications

(1,314 citation statements)

References 29 publications

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“…1f, g). We also tested randomly connected recurrent networks trained to produce ramping triggered by a transient input [22][23][24] ("trained RRNs"). These models failed to recover from perturbations (Extended Data Fig.…”

Section: Robust Model Networkmentioning

confidence: 99%

“…1.5 is a gain factor which is sufficiently strong to produce chaotic activity 49 . Training was performed for 30 iterations where the weights were adjusted at each time step as described in 22 . Each solid line represents the activity of the network's output in response to transiently clamping the activity of a randomly-selected population of 200 (i.e.…”

Section: Extended Datamentioning

confidence: 99%

“…Gating neurons (labeled "G") cancel the inter-module coupling during normal operation. d. Modular recurrent network trained with FORCE learning 22 to recapitulate single hemisphere perturbation.…”

Section: Extended Datamentioning

confidence: 99%

See 2 more Smart Citations

Robust neuronal dynamics in premotor cortex during motor planning

Li¹,

Daie²,

Svoboda³

et al. 2016

Nature

461

485

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Neural activity maintains representations that bridge past and future events, often over many seconds. Network models can produce persistent and ramping activity, but the positive feedback that is critical for these slow dynamics can cause sensitivity to perturbations. Here we use electrophysiology and optogenetic perturbations in mouse premotor cortex to probe robustness of persistent neural representations during motor planning. Preparatory activity is remarkably robust to large-scale unilateral silencing: detailed neural dynamics that drive specific future movements were quickly and selectively restored by the network. Selectivity did not recover after bilateral silencing of premotor cortex. Perturbations to one hemisphere are thus corrected by information from the other hemisphere. Corpus callosum bisections demonstrated that premotor cortex hemispheres can maintain preparatory activity independently. Redundancy across selectively coupled modules, as we observed in premotor cortex, is a hallmark of robust control systems. Network models incorporating these principles show robustness that is consistent with data.

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Section: Robust Model Networkmentioning

confidence: 99%

Section: Extended Datamentioning

confidence: 99%

See 1 more Smart Citation

Robust neuronal dynamics in premotor cortex during motor planning

Li¹,

Daie²,

Svoboda³

et al. 2016

Nature

461

485

View full text Add to dashboard Cite

show abstract

“…Task-related reduction in variability has been observed throughout the cortex at the intracellular and inter-cellular level affecting membrane potential as well as individual and correlated neuronal firing (Churchland et al, 2010). Theoretical models have suggested similar properties for large scale neural networks (Sussillo & Abbott, 2009;Rajan et al 2012). Our results provide direct empirical support for these models with regards to WM and further suggest that task difficulty is a significant additional moderator of short-term plasticity.…”

Section: Discussionmentioning

confidence: 94%

Dynamic causal modeling of load-dependent modulation of effective connectivity within the verbal working memory network

2013

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractNeuroimaging studies have consistently shown that working memory (WM) tasks engage a distributed neural network that primarily includes the dorsolateral prefrontal (DLPFC), the parietal (PAR) and the anterior cingulate cortices (ACC). The current challenge is to provide a mechanistic account of the changes observed in regional activity. To achieve this we characterised neuroplastic responses in effective connectivity between these regions at increasing WM loads using Dynamic Causal Modeling of functional magnetic resonance imaging data obtained from healthy individuals during a verbal n-back task. Our data demonstrate that increasing memory load was associated with (a) right-hemisphere dominance, (b) increasing forward (i.e. posterior to anterior) effective connectivity within the WM network, and (c) reduction in individual variability in WM network architecture resulting in the right-hemisphere forward model reaching an exceedance probability of 99% in the most demanding condition. Our results provide direct empirical support that task difficulty, in our case WM load, is a significant moderator of short-term plasticity, complementing existing theories of task-related reduction in variability in neural networks.

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“…Although aging correlates with a natural decline in such cognitive abilities [1], notable deficits are usually associated with degenerative diseases or traumatic brain injuries (TBI). At present, limitations in biophysical measurements and neural recordings make it extremely difficult to extract the underlying mechanisms responsible for dysfunctions in neural networks, especially when circuits display intrinsically complex behavior and functional activity.…”

Section: Introductionmentioning

confidence: 99%

Cognitive and behavioral deficits arising from neurodegeneration and traumatic brain injury: a model for the underlying role of focal axonal swellings in neuronal networks with plasticity

Rudy¹,

Maia²,

Kutz³

2016

J Integr Syst Neurosci

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Neurodegenerative diseases and traumatic brain injury, whose hallmark features are the presence of focal axonal swellings (FAS), are leading causes of cognitive dysfunction. By leveraging biophysical observations of FAS statistics, we develop a theoretical model of functional neural network activity driven by adaptive changes from plasticity. Based upon the FORCE model of Sussillo and Abbott [1], our innovations highlight the role of plasticity in overcoming injuries and degeneration of neurons in a network architecture. We provide a quantitative measure, on a network level, of cognitive deficits arising from injury. We demonstrate that plasticity is capable of overcoming mild injuries while failing to compensate for more severe injuries. Such injuries are characterized by their underlying effect on spike trains propagating through the neurons in a network architecture. Specifically, spike trains can be filtered in firing rate, or blocked under more severe FAS. The level of injury dictates the FORCE model's ability to produce a desired output functionality (and associated behavior) and allows for quantitative metrics for accessing cognitive and behavioral deficits. Thus a direct link between FAS in neural networks and compromised functional response can be established. The theoretical framework developed is a promising computational framework for providing a deeper understanding of the cognitive deficits arising in, for instance, Alzheimer's, Parkinson's, Multiple-Sclerosis, and TBI.

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Generating Coherent Patterns of Activity from Chaotic Neural Networks

Cited by 991 publications

References 29 publications

Robust neuronal dynamics in premotor cortex during motor planning

Robust neuronal dynamics in premotor cortex during motor planning

Dynamic causal modeling of load-dependent modulation of effective connectivity within the verbal working memory network

Cognitive and behavioral deficits arising from neurodegeneration and traumatic brain injury: a model for the underlying role of focal axonal swellings in neuronal networks with plasticity

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