Neural activity classification with machine learning models trained on interspike interval series data

The flow of information through the brain is reflected by the activity patterns of neural cells. Indeed, these firing patterns are widely used as input data to predictive models that relate stimuli and animal behavior to the activity of a population of neurons. However, relatively little attention was paid to single neuron spike trains as predictors of cell or network properties in the brain. In this work, we introduce an approach to neuronal spike train data mining which enables effective classification and clustering of neuron types and network activity states based on single-cell spiking patterns. This approach is centered around applying state-of-the-art time series classification/clustering methods to sequences of interspike intervals recorded from single neurons. We demonstrate good performance of these methods in tasks involving classification of neuron type (e.g. excitatory vs. inhibitory cells) and/or neural circuit activity state (e.g. awake vs. REM sleep vs. nonREM sleep states) on an open-access cortical spiking activity dataset

Synaptic plasticity emerging from chemical reactions: modeling spike-timing dependent plasticity of basal ganglia neurons

Notre cerveau prend en charge différentes 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 impliqué dans la sélection de l’action et une forme spécifique de l’apprentissage / mémoire, la mémoire procédurale (mémoire des compétences ou d’expertise). A l’échelle du neurone unique, le support le plus plausible de l’apprentissage et de la mémoire est la plasticité synaptique, le processus par lequel l’efficacité de la communication entre deux neurones change en réponse à un pattern spécifique de conditions environnementales. Parmi les différentes formes de plasticité synaptique, la plasticité dépendante du timing des spikes (STDP) représente le fait que le poids synaptique (l’efficacité de la connexion) change en fonction du temps écoulé entre l’émission des deux potentiels d’action (spikes) présynaptiques et postsynaptiques consécutifs. Si la STDP est une forme de plasticité qui a récemment attiré beaucoup d’intérêt, on ne comprends pas encore comment elle emerge des voies de signalisation / biochimiques qui la sous-tendent. Pour répondre à cette question, nous combinons les approches expérimentales de nos collaborateurs (pharmacologie et électrophysiologie) avec la modélisation de la dynamique des réseaux de signalisation impliquées (décrite par des équations différentielles ordinaires). Après estimation des paramètres, le modèle reproduit la quasi-totalité des données expérimentales, y compris la dépendance de la STDP envers le nombre stimulations pré- et post-synaptiques appariées et son exploration pharmacologique intensive (perturbation des voies de signalisation par des produits chimiques). En outre, contrairement à ce qui était largement admis dans la communauté des neurosciences, notre modèle indique directement que le système endocannabinoïde contrôle les changements du poids synaptique de façon bi-directionnelle (augmentation et diminution). De plus, nous étudions comment une série de facteurs comme la recapture du glutamate régule la STDP. Notre modèle représente une première étape pour l’élucidation de la régulation de l’apprentissage et de la mémoire au niveau du neurone unique dans les ganglions de la base

Spikebench: An open benchmark for spike train time-series classification.

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

Robustness of STDP to spike timing jitter

International audienceIn 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 frequency or number of pairings was increased, NMDAR-LTP became more robust and could be expressed despite strong jittering. Our results identify endocannabinoid-plasticity as a robust form of STDP, whereas the sensitivity to jitter of NMDAR-LTP varies with activity frequency. This provides new insights into the mechanisms at play during the different phases of learning and memory and the emergence of Hebbian plasticity in in vivo-like activity

BDNF controls bidirectional endocannabinoid-plasticity at corticostriatal synapses

International audienceThe dorsal striatum exhibits bidirectional corticostriatal synaptic plasticity, NMDAR- and endocannabinoids-(eCB) mediated, necessary for the encoding of procedural learning. Therefore, characterizing factors controlling corticostriatal plasticity is of crucial importance. Brain-derived neurotrophic factor (BDNF) and its receptor, the tropomyosine receptor kinase- B (TrkB), shape striatal functions and their dysfunction deeply affects basal ganglia. BDNF/TrkB signaling controls NMDAR-plasticity in various brain structures including the striatum. However, despite cross-talk between BDNF and eCBs, the role of BDNF in eCBplasticity remains unknown. Here, we show that BDNF/TrkB signaling promotes eCBplasticity (LTD and LTP) induced by rate-based (low-frequency stimulation) or spike-timingbased (spike-timing-dependent plasticity, STDP) paradigm in striatum. We show that TrkB activation is required for the expression and the scaling of both eCB-LTD and eCB-LTP. Using two-photon imaging of dendritic spines combined with patch-clamp recordings, we show that TrkB activation prolongs intracellular calcium transients, thus increasing eCB synthesis and release. We provide a mathematical model for the dynamics of the signaling pathways involved in corticostriatal plasticity. Finally, we show that TrkB activation enlarges the domain of expression of eCB-STDP. Our results reveal a novel role for BDNF/TrkB signaling in governing eCB-plasticity expression in striatum, and thus the engram of procedural learning

Dopamine–endocannabinoid interactions mediate spike-timing-dependent potentiation in the striatum

Dopamine tightly regulates plasticity at corticostriatal synapses. Here, the authors report that endocannabinoid dependent LTP induced with few spikes in the striatum is impaired in a rodent model of Parkinson’s disease, requires dopamine through presynaptic D2 receptors located on corticostriatal inputs