Biology subjects

Beau, M.

Publications and source records attributed to Beau, M..

5 recordsLinked to original sources

bioRxiv2024

A deep-learning strategy to identify cell types across species from high-density extracellular recordings

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but dont reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals, revealing the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetic activation and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep-learning classifier that predicts cell types with greater than 95% accuracy based on waveform, discharge statistics, and layer of the recorded neuron. The classifiers predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across animal species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously-recorded cell types during behavior.

neuroscience↗

bioRxiv2022

Domain Adaptive Neural Inference for Neurons, Microcircuits and Networks

Accurately inferring neural circuit connectivity from in vivo recordings is essential for understanding the computations that support behavior and cognition. However, current deep learning approaches are limited by incomplete observability and the lack of ground-truth labels in real experiments. Consequently, models are often trained on synthetic data, which leads to the well-known "model mismatch" problem when simulated dynamics diverge from true neural activity. To overcome these challenges, we present Deep Domain-Adaptive Matching (DeepDAM), a training framework that adaptively matches synthetic and in vivo data domains for neural connectivity inference. Specifically, DeepDAM fine-tunes deep neural networks on a combined dataset of synthetic simulations and unlabeled in vivo recordings, aligning the models feature representations with real neural dynamics to mitigate model mismatch. We demonstrate this approach in rodent hippocampal CA1 circuits as a proof-of-concept, achieving near-perfect connectivity inference performance (Matthews correlation coefficient [~]0.97-1.0) and substantially surpassing classical methods ([~]0.6-0.7). We further demonstrate robustness across multiple recording conditions within this hippocampal dataset. Additionally, to illustrate its broader applicability, we extend the framework to two distinct systems without altering the core methodology: a stomatogastric microcircuit in Cancer borealis (ex vivo) and single-neuron intracellular recordings in mouse, where DeepDAM significantly improves efficiency and accuracy over standard approaches. By effectively leveraging synthetic data for in vivo and ex vivo analysis, DeepDAM offers a generalizable strategy for overcoming model mismatch and represents a critical step towards data-driven reconstruction of functional neural circuits.

neuroscience↗

bioRxiv2022

Alterations of the axon initial segment in multiple sclerosis

Grey matter damage has been established as a key contributor to disability progression in multiple sclerosis. Aside from neuronal loss and axonal transections, which predominate in cortical demyelinated lesions, synaptic alterations have been detected in both demyelinated plaques and normal-appearing grey matter, resulting in functional neuronal damage. The axon initial segment is a key element of neuronal function, responsible for action potential initiation and maintenance of neuronal polarity. Despite several reports of profound axon initial segment alterations in different pathological models, among which experimental auto-immune encephalomyelitis, whether the axon initial segment is affected in multiple sclerosis is still unknown. Using immunohistochemistry, we analyzed axon initial segments from control and multiple sclerosis tissue, focusing on layer 5/6 pyramidal neurons in the neocortex and Purkinje cells in the cerebellum and performed analysis on the parameters known to control neuronal excitability, i.e., axon initial segment length and position. We found that the axon initial segment length was unchanged among the different multiple sclerosis samples, and not different from controls. In contrast, in both cell types, the axon initial segment position was altered, with an increased soma-axon initial segment gap, in both active and inactive demyelinated lesions. In addition, using a computational model, we show that this increased gap between soma and axon initial segment might increase neuronal excitability. Taken together, these results show for the first time changes of axon initial segments in multiple sclerosis, in active as well as inactive grey matter lesions in both neocortex and cerebellum, which might alter neuronal function.

neuroscience↗

bioRxiv2021

A rapid and efficient learning rule for biological neural circuits

The dominant view in neuroscience is that changes in synaptic weights underlie learning. It is unclear, however, how the brain is able to determine which synapses should change, and by how much. This uncertainty stands in sharp contrast to deep learning, where changes in weights are explicitly engineered to optimize performance. However, the main tool for that, backpropagation, has two problems. One is neuro-science related: it is not biologically plausible. The other is inherent: networks trained with this rule tend to forget old tasks when learning new ones. Here we introduce the Dendritic Gated Network (DGN), a variant of the Gated Linear Network, which offers a biologically plausible alternative to backpropagation. DGNs combine dendritic gating (whereby interneurons target dendrites to shape neuronal responses) with local learning rules to yield provably efficient performance. They are significantly more data efficient than conventional artificial networks, and are highly resistant to forgetting. Consequently, they perform well on a variety of tasks, in some cases better than backpropagation. Importantly, DGNs have structural and functional similarities to the cerebellum, a link that we strengthen by using in vivo two-photon calcium imaging to show that single interneurons suppress activity in individual dendritic branches of Purkinje cells, a key feature of the model. Thus, DGNs leverage targeted dendritic inhibition and local learning - two features ubiquitous in the brain - to achieve fast and efficient learning.

neuroscience↗

bioRxiv2020

Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings

To study the dynamics of neural processing across timescales, we require the ability to follow the spiking of thousands of individually separable neurons over weeks and months, during unrestrained behavior. To address this need, we introduce the Neuropixels 2.0 probe together with novel analysis algorithms. The new probe has over 5,000 sites and is miniaturized such that two probes plus a headstage, recording 768 sites at once, weigh just over 1 g, suitable for implanting chronically in small mammals. Recordings with high quality signals persisting for at least two months were reliably obtained in two species and six different labs. Improved site density and arrangement combined with new data processing methods enable automatic post-hoc stabilization of data despite brain movements during behavior and across days, allowing recording from the same neurons in the mouse visual cortex for over 2 months. Additionally, an optional configuration allows for recording from multiple sites per available channel, with a penalty to signal-to-noise ratio. These probes and algorithms enable stable recordings from >10,000 sites during free behavior in small animals such as mice.

neuroscience↗