Sequence memory in recurrent neuronal network can develop without structured input

Loidolt, Matthias; Rudelt, Lucas; Priesemann, Viola

doi:10.1101/2020.09.15.297580

Cited by 7 publications

(7 citation statements)

References 74 publications

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“…First, assemblies might be formed in early development, and be later used as a ‘backbone’ to learn new sequences or associate new features (Holtmaat & Caroni, 2016). Cortical areas generate stereotypical structured ‘spontaneous activity’ during early development that is important for connectivity refinement (Richter & Gjorgjieva, 2017) and can drive assembly formation without structured external input (Loidolt et al., 2020; Montangie et al., 2020; Ravid Tannenbaum & Burak, 2016). Second, new assemblies might also form in the adult brain, where they should be integrated into an existing network structure without ‘forgetting’ previously learned representations and potentially allowing overlap between assemblies.…”

Section: Introductionmentioning

confidence: 99%

Formation and computational implications of assemblies in neural circuits

Miehl

Onasch

Festa

et al. 2022

The Journal of Physiology

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In the brain, patterns of neural activity represent sensory information and store it in non‐random synaptic connectivity. A prominent theoretical hypothesis states that assemblies, groups of neurons that are strongly connected to each other, are the key computational units underlying perception and memory formation. Compatible with these hypothesised assemblies, experiments have revealed groups of neurons that display synchronous activity, either spontaneously or upon stimulus presentation, and exhibit behavioural relevance. While it remains unclear how assemblies form in the brain, theoretical work has vastly contributed to the understanding of various interacting mechanisms in this process. Here, we review the recent theoretical literature on assembly formation by categorising the involved mechanisms into four components: synaptic plasticity, symmetry breaking, competition and stability. We highlight different approaches and assumptions behind assembly formation and discuss recent ideas of assemblies as the key computational unit in the brain.

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Section: Introductionmentioning

confidence: 99%

Formation and computational implications of assemblies in neural circuits

Miehl

Onasch

Festa

et al. 2022

The Journal of Physiology

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“…For cognitive processes (for example during task-solving), the intrinsic timescale was further linked to working memory. In particular, working memory might be implemented through neurons with long timescales [ 3 , 4 , 50 ].…”

Section: Discussionmentioning

confidence: 99%

MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity

et al. 2021

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Here we present our Python toolbox “MR. Estimator” to reliably estimate the intrinsic timescale from electrophysiologal recordings of heavily subsampled systems. Originally intended for the analysis of time series from neuronal spiking activity, our toolbox is applicable to a wide range of systems where subsampling—the difficulty to observe the whole system in full detail—limits our capability to record. Applications range from epidemic spreading to any system that can be represented by an autoregressive process. In the context of neuroscience, the intrinsic timescale can be thought of as the duration over which any perturbation reverberates within the network; it has been used as a key observable to investigate a functional hierarchy across the primate cortex and serves as a measure of working memory. It is also a proxy for the distance to criticality and quantifies a system’s dynamic working point.

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Section: Discussionmentioning

confidence: 99%

MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity

Spitzner,

Dehning,

Wilting

et al. 2020

Preprint

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Here we present our Python toolbox "MR. Estimator" to reliably estimate the intrinsic timescale from electrophysiologal recordings of heavily subsampled systems. Originally intended for the analysis of time series from neuronal spiking activity, our toolbox is applicable to a wide range of systems where subsampling -the difficulty to observe the whole system in full detail -limits our capability to record.Applications range from epidemic spreading to any system that can be represented by an autoregressive process.In the context of neuroscience, the intrinsic timescale can be thought of as the duration over which any perturbation reverberates within the network; it has been used as a key observable to investigate a functional hierarchy across the primate cortex and serves as a measure of working memory. It is also a proxy for the distance to criticality and quantifies a system's dynamic working point.

show abstract

Sequence memory in recurrent neuronal network can develop without structured input

Cited by 7 publications

References 74 publications

Formation and computational implications of assemblies in neural circuits

Formation and computational implications of assemblies in neural circuits

MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity

MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity

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