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The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model

Cerebral cortex (New York, N.Y. 1991), 2014-03, Vol.24 (3), p.785-806

2014

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Second type of criticality in the brain uncovers rich multiple-neuron dynamics

Proceedings of the National Academy of Sciences - PNAS, 2019-06, Vol.116 (26), p.13051-13060

2019

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Coherent noise enables probabilistic sequence replay in spiking neuronal networks

PLoS computational biology, 2023-05, Vol.19 (5), p.e1010989-e1010989

2023

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The correlation structure of local neuronal networks intrinsically results from recurrent dynamics

PLoS computational biology, 2014-01, Vol.10 (1), p.e1003428-e1003428

2014

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A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas

PLoS computational biology, 2018-10, Vol.14 (10), p.e1006359-e1006359

2018

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Identifying Anatomical Origins of Coexisting Oscillations in the Cortical Microcircuit

PLoS computational biology, 2016-10, Vol.12 (10), p.e1005132-e1005132

2016

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Spike-Timing-Dependent Plasticity in Balanced Random Networks

Neural computation, 2007-06, Vol.19 (6), p.1437-1467

2007

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Decorrelation of neural-network activity by inhibitory feedback

PLoS computational biology, 2012-08, Vol.8 (8), p.e1002596-e1002596

2012

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Connectivity concepts in neuronal network modeling

PLoS computational biology, 2022-09, Vol.18 (9), p.e1010086-e1010086

2022

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Sequence learning, prediction, and replay in networks of spiking neurons

PLoS computational biology, 2022-06, Vol.18 (6), p.e1010233-e1010233

2022

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Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations

PLoS computational biology, 2015-09, Vol.11 (9), p.e1004490-e1004490

2015

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Global organization of neuronal activity only requires unstructured local connectivity

eLife, 2022-01, Vol.11

2022

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Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model

Frontiers in neuroscience, 2018-05, Vol.12, p.291-291

2018

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Reconstructing neuronal circuitry from parallel spike trains

Nature communications, 2019-10, Vol.10 (1), p.4468-13, Article 4468

2019

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Dynamical Characteristics of Recurrent Neuronal Networks Are Robust Against Low Synaptic Weight Resolution

Frontiers in neuroscience, 2021-12, Vol.15, p.757790-757790

2021

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Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers

Frontiers in neuroinformatics, 2018-02, Vol.12, p.2-2

2018

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CoCoMac 2.0 and the future of tract-tracing databases

Frontiers in neuroinformatics, 2012-01, Vol.6, p.30-30

2012

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Spike-timing dependence of structural plasticity explains cooperative synapse formation in the neocortex

PLoS computational biology, 2012-09, Vol.8 (9), p.e1002689-e1002689

2012

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The Local Field Potential Reflects Surplus Spike Synchrony

Cerebral cortex (New York, N.Y. 1991), 2011-12, Vol.21 (12), p.2681-2695

2011

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Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions

Frontiers in neuroinformatics, 2020-05, Vol.14, p.12-12

2020

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A Spiking Neural Network Model of an Actor-Critic Learning Agent

Neural computation, 2009-02, Vol.21 (2), p.301-339

2009

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NMDA-receptor inhibition increases spine stability of denervated mouse dentate granule cells and accelerates spine density recovery following entorhinal denervation in vitro

Neurobiology of disease, 2013-11, Vol.59, p.267-276

2013

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Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome

PLoS computational biology, 2017-02, Vol.13 (2), p.e1005179-e1005179

2017

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Sub-realtime simulation of a neuronal network of natural density

Neuromorphic computing and engineering, 2022-06, Vol.2 (2), p.21001

2022

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An imperfect dopaminergic error signal can drive temporal-difference learning

PLoS computational biology, 2011-05, Vol.7 (5), p.e1001133-e1001133

2011

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The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model

Second type of criticality in the brain uncovers rich multiple-neuron dynamics

Coherent noise enables probabilistic sequence replay in spiking neuronal networks

The correlation structure of local neuronal networks intrinsically results from recurrent dynamics

A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas

Identifying Anatomical Origins of Coexisting Oscillations in the Cortical Microcircuit

Spike-Timing-Dependent Plasticity in Balanced Random Networks

Decorrelation of neural-network activity by inhibitory feedback

Connectivity concepts in neuronal network modeling

Sequence learning, prediction, and replay in networks of spiking neurons

Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations

Global organization of neuronal activity only requires unstructured local connectivity

Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model

Reconstructing neuronal circuitry from parallel spike trains

Dynamical Characteristics of Recurrent Neuronal Networks Are Robust Against Low Synaptic Weight Resolution

Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers

CoCoMac 2.0 and the future of tract-tracing databases

Spike-timing dependence of structural plasticity explains cooperative synapse formation in the neocortex

The Local Field Potential Reflects Surplus Spike Synchrony

Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions

A Spiking Neural Network Model of an Actor-Critic Learning Agent

NMDA-receptor inhibition increases spine stability of denervated mouse dentate granule cells and accelerates spine density recovery following entorhinal denervation in vitro

Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome

Sub-realtime simulation of a neuronal network of natural density

An imperfect dopaminergic error signal can drive temporal-difference learning

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