Latest Research: Single Neuron Activity Classifiction

The activity of the populations of neurons carries information about the states of the brain. What about individual neurons? As participants to the population signals, they should carry some information about the global states of the brain as well. We tested this hypothesis by applying modern Machine Learning methods to individual neuron spike-train data. We evaluated our approach in a series of progressively more challenging classification problems: i) inhibitory vs excitatory neuron classification, ii) wake vs sleep brain state classification, iii) REM vs non-REM sleep classification. In all cases, it is possible to use single neuron data to infer the state of the brain / functional role of the neuron. This result opens the range of questions about the role of single neurons in neural codes of the brain and allows to quantify how much of the global information is contained in the spike-trains of individual neurons.

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Synaptic Plasticity of Basal Ganglia Neurons

Human brain supports various forms of memory and learning in its various subparts. This is for instance the case of the basal ganglia, a set of subcortical nuclei that is involved in action selection, and a specific form of learning / memory, procedural memory (memory of skills or expertise). At the scale of single neurons, the most plausible support of learning and memory is synaptic plasticity, i.e. the activity-dependent modification of the weight between two connected neurons. In particular, spike-timing dependent plasticity (STDP), whereby weight change is dictated by the time delay between presynaptic and postynaptic action potentials, has attracted much attention from theoretical and experimental communities. However, at the molecular scale, this phenomenon and its dependence on underlying signaling pathways are not yet understood. To address this issue, we combine experimental methods (pharmacology and electrophysiology) with modeling (ODE model of the implicated signaling network).

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