Ilya Prokin | Institut National de Recherche en Informatique et Automatique (INRIA) (original) (raw)

Papers by Ilya Prokin

Notre cerveau prend en charge differentes formes d’apprentissage dans ses diverses parties. C’est... more Notre cerveau prend en charge differentes formes d’apprentissage dans ses diverses parties. C’est par exemple le cas des ganglions de la base, un ensemble de noyaux sous-corticaux qui est implique dans la selection de l’action et une forme specifique de l’apprentissage / memoire, la memoire procedurale (memoire des competences ou d’expertise). A l’echelle du neurone unique, le support le plus plausible de l’apprentissage et de la memoire est la plasticite synaptique, le processus par lequel l’efficacite de la communication entre deux neurones change en reponse a un pattern specifique de conditions environnementales. Parmi les differentes formes de plasticite synaptique, la plasticite dependante du timing des spikes (STDP) represente le fait que le poids synaptique (l’efficacite de la connexion) change en fonction du temps ecoule entre l’emission des deux potentiels d’action (spikes) presynaptiques et postsynaptiques consecutifs. Si la STDP est une forme de plasticite qui a recemment...

Nature Communications, 2018

Dopamine modulates striatal synaptic plasticity, a key substrate for action selection and procedu... more Dopamine modulates striatal synaptic plasticity, a key substrate for action selection and procedural learning. Thus, characterizing the repertoire of activity-dependent plasticity in striatum and its dependence on dopamine is of crucial importance. We recently unraveled a striatal spike-timing-dependent long-term potentiation (tLTP) mediated by endocannabinoids (eCBs) and induced with few spikes (~5-15). Whether this eCB-tLTP interacts with the dopaminergic system remains to be investigated. Here, we report that eCB-tLTP is impaired in a rodent model of Parkinson's disease and rescued by L-DOPA. Dopamine controls eCB-tLTP via dopamine type-2 receptors (D 2 R) located presynaptically in cortical terminals. Dopamine-endocannabinoid interactions via D 2 R are required for the emergence of tLTP in response to few coincident pre-and post-synaptic spikes and control eCB-plasticity by modulating the long-term potentiation (LTP)/depression (LTD) thresholds. While usually considered as a depressing synaptic function, our results show that eCBs in the presence of dopamine constitute a versatile system underlying bidirectional plasticity implicated in basal ganglia pathophysiology.

Scientific reports, Jan 25, 2018

In Hebbian plasticity, neural circuits adjust their synaptic weights depending on patterned firin... more In Hebbian plasticity, neural circuits adjust their synaptic weights depending on patterned firing. Spike-timing-dependent plasticity (STDP), a synaptic Hebbian learning rule, relies on the order and timing of the paired activities in pre- and postsynaptic neurons. Classically, in ex vivo experiments, STDP is assessed with deterministic (constant) spike timings and time intervals between successive pairings, thus exhibiting a regularity that differs from biological variability. Hence, STDP emergence from noisy inputs as occurring in in vivo-like firing remains unresolved. Here, we used noisy STDP pairings where the spike timing and/or interval between pairings were jittered. We explored with electrophysiology and mathematical modeling, the impact of jitter on three forms of STDP at corticostriatal synapses: NMDAR-LTP, endocannabinoid-LTD and endocannabinoid-LTP. We found that NMDAR-LTP was highly fragile to jitter, whereas endocannabinoid-plasticity appeared more resistant. When the...

eLife, Jan 27, 2016

Synaptic plasticity is a cardinal cellular mechanism for learning and memory. The endocannabinoid... more Synaptic plasticity is a cardinal cellular mechanism for learning and memory. The endocannabinoid (eCB) system has emerged as a pivotal pathway for synaptic plasticity because of its widely characterized ability to depress synaptic transmission on short- and long-term scales. Recent reports indicate that eCBs also mediate potentiation of the synapse. However it is not known how eCB signaling may support bidirectionality. Here, we combined electrophysiology experiments with mathematical modeling to question the mechanisms of eCB bidirectionality in spike-timing dependent plasticity (STDP) at corticostriatal synapses. We demonstrate that STDP outcome is controlled by eCB levels and dynamics: prolonged and moderate levels of eCB lead to eCB-mediated long-term depression (eCB-tLTD) while short and large eCB transients produce eCB-mediated long-term potentiation (eCB-tLTP). Moreover, we show that eCB-tLTD requires active calcineurin whereas eCB-tLTP necessitates the activity of presynapt...

The problem of classification of neuronal network bursting activity monitored by multielectrode a... more The problem of classification of neuronal network bursting activity monitored by multielectrode arrays is one of important computational tasks in the studies of neuronal culture dynamics. We propose a novel method to ana-lyze spiking patterns capable to identify multiple statistically significant functional connections between cells/electrodes characterized by different spike transmission timings.

Radiophysics and Quantum Electronics, 2012

In this paper, we study the influence of the frequency-dependent connection on the signal transmi... more In this paper, we study the influence of the frequency-dependent connection on the signal transmission in a system of two interacting pulsed neural oscillators. The system is a model of two neurons with synaptic connection having the synaptic-plasticity feature, i.e., synaptic-parameter variation as a function of the frequency characteristics of the signal. It is shown that plastic connection can control the signal-transmission efficiency depending on the pulse-repetition rate and ensures stable synchronization modes of the pulse trains with different ratios between the frequencies of the output and input pulses. Analytical estimates for the parameter ranges corresponding to generation of the pulse response at the detector neuron depending on the pulse-repetition rate at the oscillator neuron were obtained.

Modern well-performing approaches to neural decoding are based on machine learning models such as... more Modern well-performing approaches to neural decoding are based on machine learning models such as decision tree ensembles and deep neural networks. The wide range of algorithms that can be utilized to learn from neural spike trains, which are essentially time-series data, results in the need for diverse and challenging benchmarks for neural decoding, similar to the ones in the fields of computer vision and natural language processing. In this work, we propose a spike train classification benchmark, based on open-access neural activity datasets and consisting of several learning tasks such as stimulus type classification, animal’s behavioral state prediction and neuron type identification. We demonstrate that an approach based on hand-crafted time-series feature engineering establishes a strong baseline performing on par with state-of-the-art deep learning based models for neural decoding. We release the code allowing to reproduce the reported results.Author summaryMachine learning-b...

International Journal of Bifurcation and Chaos, 2015

Radiophysics and Quantum Electronics, 2015

ННГУ им.Н.И.Лобачевского, Н.Новгород 2 Институт прикладной физики РАН, Н.Новгород Абстракт В рабо... more ННГУ им.Н.И.Лобачевского, Н.Новгород 2 Институт прикладной физики РАН, Н.Новгород Абстракт В работе исследуется влияние частотно-зависимой связи на синхронизацию в модельной системе синаптически связанных нейронных осцилляторов. Получены области параметров, при которых в модели происходит захват частоты ведомого (выходного) нейронного осциллятора.

The problem of classification of neuronal network bursting activity monitored by multielectrode a... more The problem of classification of neuronal network bursting activity monitored by multielectrode arrays is one of important computational tasks in the studies of neuronal culture dynamics. We propose a novel method to analyze spiking patterns capable to identify multiple statistically significant functional connections between cells/electrodes characterized by different spike transmission timings.

1 Нижегородский госуниверситет им. Н. И. Лобачевского, г. Нижний Новгород;

Nature Communications, 2018

Scientific reports, Jan 25, 2018

eLife, Jan 27, 2016

Radiophysics and Quantum Electronics, 2012

International Journal of Bifurcation and Chaos, 2015

Radiophysics and Quantum Electronics, 2015

The problem of classification of neuronal network bursting activity monitored by multielectrode a... more The problem of classification of neuronal network bursting activity monitored by multielectrode arrays is one of important computational tasks in the studies of neuronal culture dynamics. We propose a novel method to analyze spiking patterns capable to identify multiple statistically significant functional connections between cells/electrodes characterized by different spike transmission timings.

1 Нижегородский госуниверситет им. Н. И. Лобачевского, г. Нижний Новгород;