What is he best known for?
The fields of study he is best known for:
- Neuroscience
- Neuron
- Artificial intelligence
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.
His most cited work include:
- Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. (1308 citations)
- Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. (880 citations)
- Synaptic Mechanisms and Network Dynamics Underlying Spatial Working Memory in a Cortical Network Model (819 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Neuroscience (70.11%)
- Artificial intelligence (28.74%)
- Artificial neural network (23.56%)
What were the highlights of his more recent work (between 2017-2021)?
- Neuroscience (70.11%)
- Statistical physics (17.24%)
- Artificial neural network (23.56%)
In recent papers he was focusing on the following fields of study:
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.
Between 2017 and 2021, his most popular works were:
- Coupled ripple oscillations between the medial temporal lobe and neocortex retrieve human memory (73 citations)
- Correlations between synapses in pairs of neurons slow down dynamics in randomly connected neural networks. (39 citations)
- Correlations between synapses in pairs of neurons slow down dynamics in randomly connected neural networks. (39 citations)
In his most recent research, the most cited papers focused on:
- Neuroscience
- Neuron
- Statistics
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.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
