Biology subjects

Beique, J.-C.

Publications and source records attributed to Beique, J.-C..

10 recordsLinked to original sources

bioRxiv2025

Electrospectrometry of the Mouse Brain

A powerful computational feature of the brain is its ability to compartmentalize different functions in a way that can be flexibly recombined. Experimental evidence for such modularity arose from cytoarchitecture, connectivity, and electroencephalograms, while magnetic resonance imaging could attest that this modularity is dynamic. More spatially precise and more widely recordable, the local field potential (LFP) has an unknown brain wide organization. Here, we developed deep electrospectroscopy, a method to assess nonlinear spectral similarity between the LFP of different areas. A brain-wide application of this technique to the mouse brain revealed an organization composed of groups of spectrally similar areas. Such communities were mostly found in the fore-brain and showed an organization that did not strictly follow the cytoarchitecture. For instance, visual parts of the cortex and the visual parts of the colliculus were found in the same community. These electrospectral communities reshaped with context, showing splitting and merging operations, growing in size around brain areas required for a task. In particular, upper and lower limb parts of the somatosensory cortex were primarily in separate communities but merged during a task that required turning a wheel. Similarly, oculomotor reflexes and associative parts of the thalamus merged during the visuo-motor task. These analyzes show that LFPs are organized in a modular fashion, offering a window onto subcortical and layer-specific contributions to the compositionality of cortical brain functions.

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bioRxiv2025

Extracting Value Coding Features from Individual Serotonin Neurons

Adaptive behaviour requires animals to continually reevaluate the appetitive or aversive quality of their surroundings. Dorsal raphe serotonin neurons, the main source of serotonergic input to the forebrain, have been implicated in both signaling the quality of an animals environment and regulating reward-seeking and punishmentavoiding behaviour, but the precise quantity signaled by these neurons has remained unclear, as well as how these neurons relate with behaviour. Using open-access recordings of serotonergic neurons of the dorsal raphe nucleus while animals perform a dynamic Pavlovian task, we compare firing rate and behavioral data with a model that considers reward history accumulated over a tunable timescale. Our Bayesian parameter estimation supports that serotonergic neurons are consistent with reward history being estimated over about a hundred trials on average, with a heterogeneity across individual neurons spanning 30 to 300 trials. Anticipatory licking also correlated with reward history at multiple timescale, but could not be dissociated from that of a time/thirst nuisance variable and otherwise mostly on a timescale faster than seen in serotonergic cells. These results provide a more precise picture of the dynamics of serotonergic cells under a dynamic Pavlovian task.

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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.

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bioRxiv2024

Functional separation of long-range inputs by intrinsic dynamics of dorsal raphe 5-HT neurons

Monoaminergic nuclei such as the serotonergic dorsal raphe nucleus (DRN) receive synaptic inputs containing functionally distinct streams of information, yet the dimensionality of the resulting output population code and its cellular underpinning are currently unknown. By combining electrophysiological and computational approaches, here we uncover separable neural encoding of two excitatory inputs conveying disjunct information to DRN 5-HT neurons - the lateral habenula (LHb) and medial prefrontal cortex (mPFC). Dual-color opsin strategies revealed that a population of 5-HT neurons receive inputs from both mPFC and LHb. Subthreshold excitatory postsynaptic potentials triggered by both inputs were largely indistinguishable, yet suprathreshold spiking behavior exhibited input-specific latencies and dispersion statistics. A support vector machine classifier demonstrated that input identity can be accurately decoded from spike timing, but not subthreshold events, of under ten 5-HT neurons. Upon examining the intrinsic cellular mechanisms in 5-HT neurons that couple EPSPs to spiking dynamics, we uncovered two likely candidate mechanisms: a low-threshold calcium conductance that selectively boosts slow excitatory inputs, and a subthreshold, voltage-dependent membrane noise that generates variation of spike latency and jitter. Stochastic simulations suggest that these two intrinsic properties of 5-HT neurons are sufficient to transform LHb and mPFC inputs into distinct output spiking patterns. These results reveal that hub-like networks like the DRN can segregate distinct informational streams by a cell-intrinsic mechanism. The resulting emergent population spike synchrony code provides a means for the DRN to widely broadcast these streams as a multiplexed signal. Significance statementPhylogenetically old neuromodulatory systems in the brain, such as the serotonergic dorsal raphe nucleus, are compact yet richly innervated structures. Here, we use the raphe as a testbed to ask how distinct informational sources to hub-like networks are processed or integrated into a coherent neural code. Using electrophysiological and computational methods, including biophysically grounded stochastic simulations, we find that intrinsic noise mechanisms in serotonergic neurons are critical to transform approximately matched subthreshold excitation into distinct spike timing profiles. Thus, cell-intrinsic noise mechanisms can effectively synthesize a spike synchrony code that, we hypothesize, multiplexes input information to hub-like networks at the population level even in the absence of strong local circuit interactions.

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bioRxiv2023

Inferring plasticity rules from single-neuron spiketrains using deep learning methods

Synaptic plasticity is a core basis for learning and adaptation. Determining how synapses are altered by local signals - the learning rules - is the hinge about which brain activity pivots. A large number of in vitro characterizations have focused on restricted sets of core properties, but it remains to be established which if any of the known learning rules is most consistent with changes in activity patterns in behaving animals. To address this question, we hypothesize that the correlation between features of the activity of a single post-synaptic neuron and subsequent changes of the representations could be used to detect the underlying learning rule. Because this correlation is expected to be diluted in the notoriously large variability of brain activity, we test here learning rule inference based on passive observations of single neurons using deep artificial neural networks. Using simulated data, we found that both transformers, temporal convolutional networks, and SVM could classify learning rules far above the chance level, with transformers achieving the best overall accuracy. This performance can be achieved despite the presence of noise and representational drift. We further investigated the features used by the algorithms to perform the classification and found the deep net used inner temporal differences of distinct learning rules to separate learning trajectories. We also find, however, that the classification accuracy is sensitive to alterations in network properties. Our work illustrates that distinct learning rules generate distinguishable trajectories of responses, but warns against using simulation-trained classifiers to infer learning rules from real data.

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bioRxiv2023

Mice can learn a cognitive map based on a stable start location and self-motion signals

Animals navigate by learning the spatial layout of their environment. We investigated spatial learning of mice in an open maze where food was hidden in one of a hundred holes. Mice leaving from a stable entrance learned to efficiently navigate to the food without the need for landmarks. We developed a quantitative framework to reveal how the mice estimate the food location based on analyses of trajectories and active hole checks. After learning, the computed "target estimation vector" (TEV) closely approximated the mices route and its hole check distribution. The TEV required learning both the direction and distance of the start to food vector, and our data suggests that different learning dynamics underlie these estimates. We propose that the TEV can be precisely connected to the properties of hippocampal place cells. Finally, we provide the first demonstration that, after learning the location of two food sites, the mice took a shortcut between the sites, demonstrating that they had generated a cognitive map.

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bioRxiv2022

A ternary neural code resolves error and sharpening signals

Theories of attention and learning have hypothesized a central role for high-frequency bursting in cognitive functions, but experimental reports of burst-mediated representations in vivo have been limited. Here we used a novel demultiplexing approach by considering a conjunctive burst code. We studied this code in vivo while animals learned to report direct electrical stimulation of the somatosensory cortex and found two acquired yet independent representations. One code, the event rate, showed a sparse and succint stiumulus representation and a small modulation upon detection errors. The other code, the burst fraction, correlated more globally with stimulation and more promptly responded to detection errors. Bursting modulation was potent and its time course evolved, even in cells that were considered unresponsive based on the firing rate. During the later stages of training, this modulation in bursting happened earlier, gradually aligning temporally with the representation in event rate. The alignment of bursting and event rate modulation sharpened the firing rate response, and was strongly associated behavioral accuracy. Thus a fine-grained separation of spike timing patterns reveals two signals that accompany stimulus representations: an error signal that can be essential to guide learning and a sharpening signal that could implement attention mechanisms.

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bioRxiv2022

A slow 5-HT1AR-mediated recurrent inhibitory network in raphe computes contextual value through synaptic facilitation

Serotonin (5-HT) neurons in the dorsal raphe nucleus (DRN) receive a constellation of long-range inputs, yet guiding principles of local circuit organization and underlying computations in this nucleus are largely unknown. Using inputs from the lateral habenula (LHb) to interrogate the processing features of the DRN, we uncovered 5-HT1A receptor-mediated recurrent connections between 5-HT neurons, refuting classical theories of autoinhibition. Cellular electrophysiology and imaging of a genetically encoded 5-HT sensor revealed that these recurrent inhibitory connections spanned the raphe, were slow, stochastic, strongly facilitating, and gated spike output. These features collectively conveyed highly non-linear dynamics to this network, generating excitation-driven inhibition and winner-take-all computations. In vivo optogenetic activation of LHb inputs to DRN, at frequencies where these computations are predicted to ignite, transiently disrupted expression of a reward-conditioned response in an auditory conditioning task. Together, these data identify a core computation supported by an unsuspected slow serotonergic recurrent inhibitory network.

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bioRxiv2022

Dentate gyrus mossy cells exhibit sparse coding via adaptive spike threshold dynamics

Hilar mossy cells (hMCs) are glutamatergic neurons in the dentate gyrus (DG) that receive inputs primarily from DG granule cells (GCs), CA3 pyramidal cells and local inhibitory interneurons. The hMCs then provide direct excitatory and disynaptic inhibitory feedback input to GCs. Behavioral and in vivo single unit recording experiments have implicated hMCs in pattern separation as well as is in spatial navigation and learning. It has, however, been difficult to mechanistically link the in vivo physiological behavior of hMCs with their intrinsic excitability properties that convert their synaptic inputs into spiking output. Here, we carried out electrophysiological recordings from the main cell types in the DG and found that hMCs displayed a highly adaptive threshold acting over a remarkably protracted time-scale. The hMC spike threshold increased linearly with increasing current stimulation and saturated at high current intensities. This threshold also increased in response to spiking and this effect also decayed over a long timescale, allowing for activity-dependent summation that limited hMC firing rates. This mechanism operates in parallel with a prominent medium after-hyperpolarizing potential (AHP) generated by the small conductance K+ channel. Based on experimentally derived parameters, we developed a phenomenological exponential integrate-and-fire model that closely mimics the hMC adaptive threshold. This lightweight model is amenable to its incorporation into large network models of the DG that will be conducive to deepen our understanding of the neural bases of pattern separation, spatial learning and navigation in the hippocampus. Statement of significanceRecent studies on hilar mossy cells have revealed that they are implicated in spatial navigation and mnemonic functions. Yet, the basic intrinsic characterization of these hMCs is still too superficial to explain their spiking behavior in vivo. Here, we describe novel biophysical properties of hMCs, including an independent relationship between spike latency and spike threshold as well as a slowly adapting spike threshold. These findings complement several other biophysical and connectivity similarities between hMCs and CA3 pyramidal cells, while emphasizing the contrast with hilar interneurons. Additionally, our results are well captured by a phenomenological model of the hMC which provides a useful framework to study the neural substrate of spatial navigation and learning in the dentate gyrus.

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bioRxiv2019

A synthetic likelihood solution to the silent synapse estimation problem

Functional features of populations of synapses are typically inferred from random electrophysiological sampling of small subsets of synapses. Are these samples unbiased? Here, we developed a biophysically constrained statistical framework for addressing this question and applied it to assess the performance of a widely used method based on a failure-rate analysis to quantify the occurrence of silent (AMPAR-lacking) synapses in neural networks. We simulated this method in silico and found that it is characterized by strong and systematic biases, poor reliability and weak statistical power. Key conclusions were validated by whole-cell recordings from hippocampal neurons. To address these shortcomings, we developed a simulator of the experimental protocol and used it to compute a synthetic likelihood. By maximizing the likelihood, we inferred silent synapse fraction with no bias, low variance and superior statistical power over alternatives. Together, this generalizable approach highlights how a simulator of experimental methodologies can substantially improve the estimation of physiological properties.

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