The scientist’s investigation covers issues in Neuroscience, Artificial intelligence, Excitatory postsynaptic potential, Stimulus and Biological system. As part of his studies on Neuroscience, Nicolas Brunel often connects relevant subjects like Attractor network. The study incorporates disciplines such as Interaction information, Sensory system and Pattern recognition in addition to Artificial intelligence.
His Excitatory postsynaptic potential research integrates issues from Cerebral cortex, Attractor and Delay periods. His Stimulus study integrates concerns from other disciplines, such as Neural Inhibition, Neural ensemble and Stimulation. The various areas that Nicolas Brunel examines in his Biological system study include Linear filter, Parameter space, Modulation, Noise and Carpet plot.
Nicolas Brunel spends much of his time researching Neuroscience, Artificial intelligence, Artificial neural network, Excitatory postsynaptic potential and Inhibitory postsynaptic potential. Many of his studies on Neuroscience involve topics that are commonly interrelated, such as Network model. Nicolas Brunel interconnects Biological system, Local field potential, Sensory system and Pattern recognition in the investigation of issues within Artificial intelligence.
His Artificial neural network study combines topics from a wide range of disciplines, such as Fixed point, Computational neuroscience, Attractor, Statistical physics and Algorithm. His biological study spans a wide range of topics, including Stimulation, Time constant, Neuron and Synchronization. The concepts of his Stimulus study are interwoven with issues in Electrophysiology and Communication.
Nicolas Brunel mostly deals with Neuroscience, Statistical physics, Artificial neural network, Inhibitory postsynaptic potential and Hebbian theory. Nicolas Brunel has researched Neuroscience in several fields, including Spike-timing-dependent plasticity and Intracellular. His Statistical physics research includes themes of Limit, Randomness and Neuron.
His work deals with themes such as Computational neuroscience, Fixed point and Chaotic, which intersect with Artificial neural network. His Inhibitory postsynaptic potential study which covers Optogenetics that intersects with Cerebral cortex, Parvalbumin, Sensory system and Sensory stimulation therapy. His work carried out in the field of Hebbian theory brings together such families of science as Pattern recognition, Network model and Learning rule.
His primary areas of study are Neuroscience, Statistical physics, Artificial neural network, Cortex and Optogenetics. His Statistical physics study combines topics from a wide range of disciplines, such as Computational neuroscience, Randomness, Chaotic and Autocorrelation. The concepts of his Artificial neural network study are interwoven with issues in Transfer function, Fixed point and Limit.
His study in Cortex is interdisciplinary in nature, drawing from both Neocortex, Human brain and Brain mapping. Nicolas Brunel combines subjects such as Somatosensory system, Cerebral cortex, Inhibitory postsynaptic potential and Motor cortex, Stimulation with his study of Optogenetics. His Somatosensory system study incorporates themes from Sensory stimulation therapy, Sensory system and Parvalbumin.
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Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons.
Journal of Computational Neuroscience (2000)
Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex.
D J Amit;N Brunel.
Cerebral Cortex (1997)
Synaptic Mechanisms and Network Dynamics Underlying Spatial Working Memory in a Cortical Network Model
Albert Compte;Nicolas Brunel;Nicolas Brunel;Patricia S. Goldman-Rakic;Xiao Jing Wang.
Cerebral Cortex (2000)
Fast global oscillations in networks of integrate-and-fire neurons with low firing rates
Nicolas Brunel;Vincent Hakim.
Neural Computation (1999)
What Determines the Frequency of Fast Network Oscillations With Irregular Neural Discharges? I. Synaptic Dynamics and Excitation-Inhibition Balance
Nicolas Brunel;Xiao Jing Wang.
Journal of Neurophysiology (2003)
Erratum to: Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition
Nicolas Brunel;Xiao-Jing Wang.
Journal of Computational Neuroscience (2014)
How spike generation mechanisms determine the neuronal response to fluctuating inputs
Nicolas Fourcaud-Trocmé;David Hansel;Carl van Vreeswijk;Nicolas Brunel.
The Journal of Neuroscience (2003)
Sensory neural codes using multiplexed temporal scales
Stefano Panzeri;Nicolas Brunel;Nicolas Brunel;Nikos K. Logothetis;Nikos K. Logothetis;Christoph Kayser.
Trends in Neurosciences (2010)
Mutual information, Fisher information, and population coding
Nicolas Brunel;Jean-Pierre Nadal.
Neural Computation (1998)
Dynamics of the firing probability of noisy integrate-and-fire neurons
Nicolas Fourcaud;Nicolas Brunel.
Neural Computation (2002)
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