Timescales of learning in prefrontal cortex

Nature reviews. Neuroscience, Journal Year: 2024, Volume and Issue: 25(9), P. 597 - 610

Published: June 27, 2024

Language: Английский

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Synaptic plasticity facilitates oscillations in a V1 cortical column model with multiple interneuron types

Frontiers in Computational Neuroscience, Journal Year: 2025, Volume and Issue: 19

Published: April 30, 2025

Neural rhythms are ubiquitous in cortical recordings, but it is unclear whether they emerge due to the basic structure of microcircuits or depend on function. Using detailed electrophysiological and anatomical data mouse V1, we explored this question by building a spiking network model column incorporating pyramidal cells, PV, SST, VIP inhibitory interneurons, dynamics for AMPA, GABA, NMDA receptors. The resulting matched vivo cell-type-specific firing rates spontaneous stimulus-evoked conditions mice, although rhythmic activity was absent. Upon introduction long-term synaptic plasticity form an STDP rule, broad-band (15-60 Hz) oscillations emerged, with feedforward/feedback input streams enhancing/suppressing oscillatory drive, respectively. These plasticity-triggered relied all cell types, specific experience-dependent connectivity patterns were required generate oscillations. Our results suggest that neural not necessarily intrinsic properties circuits, rather may arise from structural changes elicited learning-related mechanisms.

Language: Английский

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Cell type specific firing patterns in a V1 cortical column model depend on feedforward and feedback activity

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: April 2, 2024

Stimulation of specific cell groups under different network regimes (e.g., spontaneous activity or sensory-evoked activity) can provide insights into the neural dynamics cortical columns. While these protocols are challenging to perform experimentally, modelling serve as a powerful tool for such explorations. Using detailed electrophysiological and anatomical data from mouse V1, we modeled spiking model column microcircuit. This incorporates pyramidal cells three distinct interneuron types (PV, SST, VIP cells, specified per lamina), well dynamic voltage-dependent properties AMPA, GABA, NMDA receptors. We first demonstrate that thalamocortical feedforward (FF) feedback (FB) stimuli arriving in have opposite effects, leading net columnar excitation inhibition respectively revealing translaminar gain control via full-column by layer 6. then perturb one group (defined type layer) at time observe effects on other states: spontaneous, feedforward-driven, feedback-driven, combination feedback. Our findings reveal when same is perturbed, response may vary significantly based its state, with strong sensory input decreasing sensitivity all perturbations serving modulator intra interactions. Given changes within neuronal populations difficult predict priori considering practical challenges conducting experiments, our computational simulations critical outcomes assist design future experimental planning.

Language: Английский

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Cell-type-specific firing patterns in a V1 cortical column model depend on feedforward and feedback-driven states

PLoS Computational Biology, Journal Year: 2025, Volume and Issue: 21(4), P. e1012036 - e1012036

Published: April 23, 2025

Stimulation of specific cell groups under different network regimes (e.g., spontaneous activity or sensory-evoked activity) can provide insights into the neural dynamics cortical columns. While these protocols are challenging to perform experimentally, modelling serve as a powerful tool for such explorations. Using detailed electrophysiological and anatomical data from mouse V1, we built novel spiking model column, which incorporates pyramidal cells three distinct interneuron types (PV, SST, VIP cells, specified per lamina), well dynamic voltage-dependent properties AMPA, GABA, NMDA receptors. We first demonstrate that thalamocortical feedforward (FF) feedback (FB) stimuli arriving in column have opposite effects, leading net columnar excitation inhibition respectively revealing translaminar gain control via full-column by layer 6. then perturb one group (i.e., type layer) at time observe effects on other states: spontaneous, feedforward-driven, feedback-driven, combination feedback. Our findings reveal when given is perturbed, response varies significantly based its state, with strong sensory input decreasing sensitivity all perturbations serving modulator intra interactions. Given changes within neuronal populations difficult predict priori experiments, our may constitute useful outcomes assist experimental design.

Language: Английский

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Synaptic plasticity is required for oscillations in a V1 cortical column model with multiple interneuron types

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Aug. 28, 2023

Abstract Neural rhythms are ubiquitous in cortical recordings, but it is unclear whether they emerge due to the basic structure of microcircuits, or depend on function. Using detailed electrophysiological and anatomical data mouse V1, we explored this question by building a spiking network model column incorporating pyramidal cells, PV, SST VIP inhibitory interneurons, dynamics for AMPA, GABA NMDA receptors. The resulting matched vivo cell-type-specific firing rates spontaneous stimulus-evoked conditions mice, although rhythmic activity was absent. Upon introduction long-term synaptic plasticity, broad-band (15-60 Hz) oscillations emerged, with feedforward/feedback input streams enhancing/suppressing oscillatory drive, respectively. These plasticity-triggered relied all cell types, specific experience-dependent connectivity patterns were required generate oscillations. Our results suggest that neural not intrinsic properties circuits, rather arise from structural changes elicited learning-related mechanisms.

Language: Английский

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Biologically plausible models of cognitive flexibility: merging recurrent neural networks with full-brain dynamics

Current Opinion in Behavioral Sciences, Journal Year: 2024, Volume and Issue: 56, P. 101351 - 101351

Published: Feb. 6, 2024

Cognitive flexibility, a cornerstone of human cognition, enables us to adapt shifting environmental demands. This brain function has been widely explored using computational modeling, although oftentimes these models focus on the operational dimension cognitive flexibility and do not retain sufficient level neurobiological detail lead electrophysiological or neuroimaging insights. In this review, we explore recent advances future directions neurobiologically plausible flexibility. We first cover progress in recurrent neural network trained perform flexible tasks, followed by discussion how whole-brain large-scale have approached distributed nature functions. Ultimately, propose here hybrid framework which both modeling philosophies converge, advocating for balanced approach that merges power with realistic spatiotemporal dynamics activity, early examples direction.

Language: Английский

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Distributed evidence accumulation across macaque large-scale neocortical networks during perceptual decision making

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Dec. 27, 2023

Despite the traditional view of parietal cortex as an important region for perceptual decision-making, recent evidence suggests that sensory accumulation occurs simultaneously across many cortical regions. We explored this hypothesis by integrating connectivity, cellular and receptor density datasets building a large-scale macaque model able to integrate conflicting signals perform decision-making task. Our results reveal supported distributed network temporal, frontal regions, with flexible sequential bottom-up or top-down modulation pathways depending on task difficulty. The replicates experimental lesioning effects reveals causal irrelevance areas like LIP decision performance is explained compensatory mechanisms within integration process. also reproduces observed temporal gating distractor timing during after Overall, our work hints at broad phenomenon, providing multiple testable predictions.

Language: Английский

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Predictive processing in neuroscience, computational modeling and psychology

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 645 - 667

Published: Aug. 7, 2024

Language: Английский

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