Browsing by Author "Brunel, Nicolas"
Now showing items 1-20 of 46
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A cerebellar learning model of vestibulo-ocular reflex adaptation in wild-type and mutant mice.
(The Journal of neuroscience : the official journal of the Society for Neuroscience, 2014-05)Mechanisms of cerebellar motor learning are still poorly understood. The standard Marr-Albus-Ito theory posits that learning involves plasticity at the parallel fiber to Purkinje cell synapses under control of the climbing ... -
A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks.
(PLoS Comput Biol, 2015-08)Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor ... -
Acetylcholine Modulates Cerebellar Granule Cell Spiking by Regulating the Balance of Synaptic Excitation and Inhibition.
(The Journal of neuroscience : the official journal of the Society for Neuroscience, 2020-04)Sensorimotor integration in the cerebellum is essential for refining motor output, and the first stage of this processing occurs in the granule cell layer. Recent evidence suggests that granule cell layer synaptic integration ... -
Analytical approximations of the firing rate of an adaptive exponential integrate-and-fire neuron in the presence of synaptic noise.
(Front Comput Neurosci, 2014)Computational models offer a unique tool for understanding the network-dynamical mechanisms which mediate between physiological and biophysical properties, and behavioral function. A traditional challenge in computational ... -
Attractor Dynamics in Networks with Learning Rules Inferred from In Vivo Data.
(Neuron, 2018-07)The attractor neural network scenario is a popular scenario for memory storage in the association cortex, but there is still a large gap between models based on this scenario and experimental data. We study a recurrent network ... -
Bayesian reconstruction of memories stored in neural networks from their connectivity
The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such question is whether it is possible ... -
Bistability and up/down state alternations in inhibition-dominated randomly connected networks of LIF neurons.
(Scientific reports, 2017-09-20)Electrophysiological recordings in cortex in vivo have revealed a rich variety of dynamical regimes ranging from irregular asynchronous states to a diversity of synchronized states, depending on species, anesthesia, and ... -
Burst-Dependent Bidirectional Plasticity in the Cerebellum Is Driven by Presynaptic NMDA Receptors.
(Cell reports, 2016-04)Numerous studies have shown that cerebellar function is related to the plasticity at the synapses between parallel fibers and Purkinje cells. How specific input patterns determine plasticity outcomes, as well as the biophysics ... -
Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location.
(Proceedings of the National Academy of Sciences of the United States of America, 2012-03)Multiple stimulation protocols have been found to be effective in changing synaptic efficacy by inducing long-term potentiation or depression. In many of those protocols, increases in postsynaptic calcium concentration have ... -
Cerebellar learning using perturbations.
(eLife, 2018-11-12)The cerebellum aids the learning of fast, coordinated movements. According to current consensus, erroneously active parallel fibre synapses are depressed by complex spikes signalling movement errors. However, this theory ... -
Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning.
(Proceedings of the National Academy of Sciences of the United States of America, 2020-11-11)Sequential activity has been observed in multiple neuronal circuits across species, neural structures, and behaviors. It has been hypothesized that sequences could arise from learning processes. However, it is still unclear ... -
Correlations between synapses in pairs of neurons slow down dynamics in randomly connected neural networks
(2017-08-01)Networks of randomly connected neurons are among the most popular models in theoretical neuroscience. The connectivity between neurons in the cortex is however not fully random, the simplest and most prominent deviation ... -
Cortical dynamics during naturalistic sensory stimulations: experiments and models.
(Journal of physiology, Paris, 2011-01)We report the results of our experimental and theoretical investigations of the neural response dynamics in primary visual cortex (V1) during naturalistic visual stimulation. We recorded Local Field Potentials (LFPs) and ... -
Coupled ripple oscillations between the medial temporal lobe and neocortex retrieve human memory
(Science, 2019-03-01)<jats:p>Episodic memory retrieval relies on the recovery of neural representations of waking experience. This process is thought to involve a communication dynamic between the medial temporal lobe memory system and the neocortex. ... -
Dynamics of networks of excitatory and inhibitory neurons in response to time-dependent inputs.
(Front Comput Neurosci, 2011)We investigate the dynamics of recurrent networks of excitatory (E) and inhibitory (I) neurons in the presence of time-dependent inputs. The dynamics is characterized by the network dynamical transfer function, i.e., how ... -
Emergence of irregular activity in networks of strongly coupled conductance-based neurons
Cortical neurons are characterized by irregular firing and a broad distribution of rates. The balanced state model explains these observations with a cancellation of mean excitatory and inhibitory currents, which ... -
Estimating network parameters from combined dynamics of firing rate and irregularity of single neurons.
(Journal of neurophysiology, 2011-01)High firing irregularity is a hallmark of cortical neurons in vivo, and modeling studies suggest a balance of excitation and inhibition is necessary to explain this high irregularity. Such a balance must be generated, at ... -
Firing rate of the leaky integrate-and-fire neuron with stochastic conductance-based synaptic inputs with short decay times
We compute the firing rate of a leaky integrate-and-fire (LIF) neuron with stochastic conductance-based inputs in the limit when synaptic decay times are much shorter than the membrane time constant. A comparison of our ... -
Forgetting leads to chaos in attractor networks
Attractor networks are an influential theory for memory storage in brain systems. This theory has recently been challenged by the observation of strong temporal variability in neuronal recordings during memory tasks. In ... -
From spiking neuron models to linear-nonlinear models.
(PLoS Comput Biol, 2011-01-20)Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models ...
