Biology subjects

Thivierge, J.-P.

Publications and source records attributed to Thivierge, J.-P..

5 recordsLinked to original sources

bioRxiv2026

CLEAR-FORCE: A reservoir computing framework for closed-loop brain stimulation

Electrical stimulation of brain activity has emerged as a key therapeutic intervention for neurological diseases. The success of these approaches relies on precisely tuned stimulation parameters. A major challenge is that these parameters should ideally be adjusted in real-time based on changes in brain activity. Closed-loop controllers address this challenge by monitoring brain activity and adapting stimulation according to ongoing changes in neural dynamics. While machine learning has been helpful in this context, closed-loop controllers typically do not adapt in real-time to fluctuations in brain activity. Here, we propose a novel framework based on reservoir computing to train a closed-loop, real-time neural controller. This approach, termed CLEAR-FORCE, trains the output weights of a recurrent network based on a combination of target signals and feedback from brain activity. Two series of simulations were performed, where the controller was successful at (1) inducing neural oscillations in a population of spiking neurons and (2) suppressing pathological oscillations. This framework provides a promising avenue for the implementation of neural controllers in low-powered, real-time, and adaptive hardware that harnesses machine learning for the personalized treatment of brain disorders.

neuroscience↗

bioRxiv2025

SpikeMAP: An unsupervised spike sorting pipeline for cortical excitatory and inhibitory neurons in high-density multielectrode arrays with ground-truth validation

Large-scale extracellular recording techniques represent a major advance in interrogating the structure and dynamics of neuronal circuits. However, methods that can resolve cell-type identity in a principled way, while simultaneously scaling to thousands of neurons, are currently lacking. Here, we introduce spikeMAP, a pipeline for the analysis of large-scale recordings of in vitro cortical activity that not only allows for the detection of spikes produced by single neurons (spike sorting), but also allows for the reliable distinction between genetically determined cell types by utilizing viral and optogenetic strategies as ground-truth validation. This approach tightly integrates the data analysis pipeline to an optogenetic, viral, and pharmacological protocol allowing for the dynamical probing of distinct cell-types while simultaneously recording from large populations. The novelty of spikeMAP is to combine a stream of well-established analysis techniques in an end-to-end fashion, creating a unified framework as follows. First, individual spike waveforms are fitted by spline interpolation to estimate their half-amplitude and peak-to-peak durations. These values are then entered in a principal component analysis with k-means clustering to identify uncorrelated signals from single channels on the array. Optimal separability of clusters is assessed by linear discriminant analysis. Finally, each channels source location is identified using spatiotemporal characteristics of spike waveforms across the array. We show that spikeMAP can resolve cell type identity in high-density arrays by analyzing activity monitored from mouse prefrontal cortex in vitro slices with an array of 4,096 closely-spaced channels. Using an optotagging functional strategy, we show an effective distinction of regular-spiking excitatory neurons from fast-spiking inhibitory interneurons using measures of action potential waveform, Fano factor, and spatially-dependent cross-correlations. In sum, the approach introduces a toolbox, validated by an experimental pipeline, that allows for a comprehensive characterization of neuronal activity obtained from different cell-types in high-density multielectrode recordings. This provides a scalable approach to investigate the interplay between distinct cell types in microcircuits of the brain. SummaryWe introduce spikeMAP, an automated cell-type specific spike sorting pipeline tailored to high-density, large-scale multielectrode array recordings. We integrate this analysis pipeline to an experimental protocol allowing for the dynamical probing of distinct cell-types while simultaneously recording from large populations of neurons. Using high-density multielectrode arrays containing 4,096 closely spaced electrodes, we monitor the spiking activity of PFC neurons in acute slice preparations. Our spike sorting technique combines spline interpolation and principal component analysis to distinguish regular-spiking excitatory neurons from fast-spiking inhibitory interneurons. We provide an experimental validation of our sorting algorithm using a targeted combination of pharmacological, viral and optogenetic strategies.

neuroscience↗

bioRxiv2021

Estimating null and potent modes of feedforward communication in a computational model of cortical activity

Communication across anatomical areas of the brain is key to both sensory and motor processes. Dimensionality reduction approaches have shown that the covariation of activity across cortical areas follows well-delimited patterns. Some of these patterns fall within the "potent space" of neural interactions and generate downstream responses; other patterns fall within the "null space" and prevent the feedforward propagation of synaptic inputs. Despite growing evidence for the role of null space activity in visual processing as well as preparatory motor control, a mechanistic understanding of its neural origins is lacking. Here, we developed a mean-rate model that allowed for the systematic control of feedforward propagation by potent and null modes of interaction. In this model, altering the number of null modes led to no systematic changes in firing rates, correlations, or mean synaptic strengths across areas, making it difficult to characterize feedforward communication with common measures of functional connectivity. A novel measure termed the null ratio captured the proportion of null modes relayed from one area to another. Applied to simultaneous recordings of primate cortical areas V1 and V2 during image viewing, the null ratio revealed that feedforward interactions have a broad null space that may reflect properties of visual stimuli.

neuroscience↗

bioRxiv2021

tACS competes with ongoing oscillations for control of spike-timing in the primate brain.

Transcranial alternating current stimulation (tACS) is commonly used to enhance brain rhythms, in the hopes of improving behavioral performance. Unfortunately, these interventions often yield highly variable results. Here, we identify a key source of this variability by recording from single neurons in alert non-human primates. We find that, rather than enhancing rhythmic activity, tACS appears to compete with the brains endogenous oscillations for control of spike timing. Specifically, when the strength of stimulation is weak relative to endogenous oscillations, tACS actually decreases the rhythmicity of spiking. However, when stimulation is comparatively stronger, tACS imposes its own rhythm on spiking activity. Thus the effect of tACS depends categorically on the strength of neural entrainment to endogenous oscillations, which varies greatly across behavioral states and brain regions. Without carefully considering these factors, attempts to impose external rhythms on specific brain regions may often yield precisely the opposite of the intended effect.

neuroscience↗

bioRxiv2019

Learning long temporal sequences in spiking networks by multiplexing neural oscillations

Many cognitive and behavioral tasks - such as interval timing, spatial navigation, motor control and speech - require the execution of precisely-timed sequences of neural activation that cannot be fully explained by a succession of external stimuli. We show how repeatable and reliable patterns of spatiotemporal activity can be generated in chaotic and noisy spiking recurrent neural networks. We propose a general solution for networks to autonomously produce rich patterns of activity by providing a multi-periodic oscillatory signal as input. We show that the model accurately learns a variety of tasks, including speech generation, motor control and spatial navigation. Further, the model performs temporal rescaling of natural spoken words and exhibits sequential neural activity commonly found in experimental data involving temporal processing. In the context of spatial navigation, the model learns and replays compressed sequences of place cells and captures features of neural activity such as the emergence of ripples and theta phase precession. Together, our findings suggest that combining oscillatory neuronal inputs with different frequencies provides a key mechanism to generate precisely timed sequences of activity in recurrent circuits of the brain.

neuroscience↗