Biology subjects

Brunel, N.

Publications and source records attributed to Brunel, N..

At least 19 recordsLinked to original sources

bioRxiv2026

Synaptic pruning, myelination and the emergence of psychiatric disorders in late adolescence

Adolescence is an important developmental period during which there are diverse changes in the brain and behavior. Goal-directed behaviors and the component processes underlying those behaviors improve during adolescence, including working memory, response inhibition, and reinforcement learning. At the same time there is substantial pruning of excitatory connections in prefrontal cortex and ongoing myelination of axons. However, psychiatric disorders also become increasingly prevalent in late adolescence and early adulthood. In this study, we develop computational models that suggest a hypothesis for how the ongoing changes in the brain can give rise to the increased prevalence of psychiatric disorders. We show that both myelination and pruning during adolescence lead to attractor landscapes in which strongly encoded memories, driven by three-factor learning rules that modulate Hebbian plasticity, come to dominate the landscape of brain activity, at the expense of weakly encoded memories. Pruning and myelination lead to large, strong attractors which, if they are related to aversive emotions, can drive intrusive thoughts and compulsions in obsessive compulsive disorder, rumination in depression, and aversive memories in post-traumatic stress disorder. The link between pruning, myelination and the emergence of dominant attractors for emotionally salient memories is well supported by the models. The way these effects map onto forebrain circuits requires more work.

neuroscience↗

bioRxiv2026

TipQUANT: A robust algorithm for quantitative analysis of spatiotemporally dynamic activities in tip-growing cells

Cell polarity, essential for cell development and function, relies on dynamic subcellular distribution of structural and signaling molecules. Tip growth, an extreme form of polar growth, involves unidirectional expansion at the apical region of cells and requires precise spatiotemporal coordination to achieve periodic and directional growth. Understanding their spatiotemporal dynamics is critical for elucidating mechanisms and functions of cell polarity. However, manual quantification of such dynamics is extremely time-consuming, hindering advancements in the field. Current algorithms have limited power and flexibility in analyzing the distribution and dynamics of molecules and structures, particularly for tip-growing cells with oscillatory and dynamic behavior. To address this challenge, we present TipQuant, an automated analysis tool that robustly detects tips and analyzes spatiotemporal dynamics of fluorescently labeled molecules/structures on plasma membranes and in cytoplasm at apices of tip-growing cells, enabling quantitative understanding of signaling and structural components in these systems.

cell biology↗

bioRxiv2026

A Spatially Structured Spiking Network Model of Beta Traveling Waves and Their Attenuation in Motor Cortex

Beta-band oscillations in primate motor cortex propagate as planar traveling waves whose amplitude attenuates with spatial gradients across the cortical sheet just before movement onset. How local excitatory-inhibitory (E-I) interactions and spatial connectivity jointly generate these waves, their attenuation patterns, and their stereotyped rostro-caudal bias remains unclear. Here we address this question by implementing a spatially structured network of leaky integrate-and-fire neurons with distance-dependent connectivity, conduction delays, and realistic synaptic dynamics. Through linear stability analysis and large-scale simulations validated against macaque electrophysiology, we show that planar beta waves emerge as Turing-Hopf spatiotemporal instabilities, where global beta oscillations coexist with irregular single-neuron firing. When the network operates near the boundary between oscillatory and asynchronous regimes, internally generated fluctuations produce the irregular, transient beta bursts characteristic of single-trial local field potentials. A rapid, spatially homogeneous increase in external drive pushes the circuit into an asynchronous state, reproducing the beta power reduction and spatial attenuation gradients seen at movement onset, alongside the irregular spatiotemporal dynamics of movement execution. By introducing anisotropic excitatory-to-excitatory connectivity, we recover the observed rostro-caudal propagation bias. Our results suggest that motor cortical traveling waves are intrinsic dynamical modes of local E-I circuits, recruited and modulated by behaviorally relevant inputs to organize movement initiation.

neuroscience↗

bioRxiv2025

Climbing fibers selectively recruit disinhibitory interneurons to enhance dendritic calcium signaling in cerebellar Purkinje cells

Climbing fiber (CF) inputs to Purkinje cells (PCs) instruct plasticity and learning in the cerebellum1-3. Paradoxically, CFs also excite molecular layer interneurons (MLIs)4,5, a cell-type that inhibits PCs and can restrict plasticity and learning6,7. However, two types of MLIs with opposing influences have recently been identified: MLI1s inhibit PCs, reduce dendritic calcium signals, and suppress plasticity of granule cell to PC synapses2,6-9, whereas MLI2s inhibit MLI1s and disinhibit PCs8. To determine how CFs can activate MLIs without also suppressing the PC calcium signals necessary for plasticity and learning, we investigated the specificity of CF inputs onto MLIs. Serial EM reconstructions indicate that CFs contact both MLI subtypes without making conventional synapses, but more CFs contact each MLI2 via more sites with larger contact areas. Slice experiments indicate that CFs preferentially excite MLI2s via glutamate spillover4,5. In agreement with these anatomical and slice experiments, in vivo Neuropixels recordings show that spontaneous CF activity excites MLI2s, inhibits MLI1s, and disinhibits PCs. In contrast, learning-related sensory stimulation produced more complex responses, driving convergent CF and granule cell inputs that could either activate or suppress MLI1s. This balance was robustly shifted toward MLI1 suppression when CFs were synchronously active, in turn elevating the PC dendritic calcium signals necessary for LTD. These data provide mechanistic insight into why CF synchrony can be highly effective at inducing cerebellar learning2,3 by revealing a critical disinhibitory circuit that allows CFs to act through MLIs to enhance PC dendritic calcium signals necessary for plasticity.

neuroscience↗

bioRxiv2024

Inter- and Intrahemispheric Sources of Vestibular Signals to V1

Head movements are sensed by the vestibular organs. Unlike classical senses, signals from vestibular organs are not conveyed to a dedicated cortical area but are broadcast throughout the cortex. Surprisingly, the routes taken by vestibular signals to reach the cortex are still largely uncharted. Here we show that the primary visual cortex (V1) receives real-time head movement signals -- direction, velocity, and acceleration -- from the ipsilateral pulvinar and contralateral visual cortex. The ipsilateral pulvinar provides the main head movement signal, with a bias toward contraversive movements (e.g. clockwise movements in left V1). Conversely, the contralateral visual cortex provides head movement signals during ipsiversive movements. Crucially, head movement variables encoded in V1 are already encoded in the pulvinar, suggesting that those variables are computed subcortically. Thus, the convergence of inter- and intrahemispheric signals endows V1 with a rich representation of the animals head movements.

neuroscience↗

bioRxiv2024

Behavioral state and stimulus strength regulate the role of somatostatin interneurons in stabilizing network activity

Inhibition stabilization enables cortical circuits to encode sensory signals across diverse contexts. Somatostatin-expressing (SST) interneurons are well-suited for this role through their strong recurrent connectivity with excitatory pyramidal cells. We developed a cortical circuit model predicting that SST cells become increasingly important for stabilization as sensory input strengthens. We tested this prediction in mouse primary visual cortex by manipulating excitatory input to SST cells, a key parameter for inhibition stabilization, with a novel cell-type specific pharmacological method to selectively block glutamatergic receptors on SST cells. Consistent with our model predictions, we find antagonizing glutamatergic receptors drives a paradoxical facilitation of SST cells with increasing stimulus contrast. In addition, we find even stronger engagement of SST-dependent stabilization when the mice are aroused. Thus, we reveal that the role of SST cells in cortical processing gradually switches as a function of both input strength and behavioral state.

neuroscience↗

bioRxiv2024

Impact of dendritic non-linearities on the computational capabilities of neurons

Multiple neurophysiological experiments have shown that dendritic non-linearities can have a strong influence on synaptic input integration. In this work we model a single neuron as a two-layer computational unit with non-overlapping sign-constrained synaptic weights and a biologically plausible form of dendritic non-linearity, which is analytically tractable using statistical physics methods. Using both analytical and numerical tools, we demonstrate several key computational advantages of non-linear dendritic integration with respect to models with linear synaptic integration. We find that the dendritic non-linearity concurrently enhances the number of possible learned input-output associations and the learning velocity, and we characterize how capacity and learning speed depend on the implemented non-linearity and the levels of dendritic and somatic inhibition. We find that experimentally observed connection probabilities naturally emerge in neurons with sign-constrained synapses as a consequence of non-linear dendritic integration, while in models with linear integration, an additional robustness parameter must be introduced in order to reproduce realistic connection probabilities. Non-linearly induced sparsity comes with a second central advantage for neuronal information processing, i.e. input and synaptic noise robustness. By testing our model on standard real-world benchmark datasets inspired by deep learning practice, we observe empirically that the non-linearity provides an enhancement in generalization performance, showing that it enables to capture more complex input/output relations.

neuroscience↗

bioRxiv2024

Inhibitory Plasticity Enhances Sequence Storage Capacity and Retrieval Robustness

The generation of motor behaviors and the performance of complex cognitive tasks rely on sequential activity in specific brain structures. The mechanisms of learning and retrieval of these temporal patterns of activity are still poorly understood. Emerging evidence has high-lighted the importance of inhibition to learning and memory. However, the specific functions of inhibitory plasticity in the learning and retrieval of sequential activity have been studied very little, apart from its role in maintaining excitation-inhibition (E-I) balance. Using simulations and dynamical mean-field theory of balanced E-I networks, we found that sequences can be stored and retrieved using plasticity in both E-to-I and I-to-E pathways, in the absence of recurrent excitatory plasticity. Networks with both E-to-I and I-to-E plasticity are shown to exhibit higher optimal capacity than models in which plasticity is restricted to recurrent excitation. We further show that inhibitory plasticity enhances robustness to external noise and initial cue perturbation. Thus, our work suggests new computational roles for inhibitory plasticity in improving capacity and robustness of sequence learning.

neuroscience↗

bioRxiv2024

Visual image familiarity learning at multiple timescales in the primate inferotemporal cortex

Humans and other primates can rapidly detect familiar objects and distinguish them from never-before-seen novel objects. We have an astonishing capacity to remember the details of visual scenes even after a single, fleeting experience. This ability is thought to rely in part on experience-dependent changes in the primate inferotemporal cortex (IT). Single neurons in IT encode visual familiarity by discriminating between novel and familiar stimuli, with stronger neural activity on average for novel images. However, key open questions are to understand how neural encoding in IT changes as images progress from novel to highly familiar, and what learning rules and computations can account for learning-dependent changes in IT activity. Here, we investigate the timescales over which novel stimuli become familiar by recording in IT as initially novel images become increasingly familiar both within and across days. We identified salient and persistent memory-related signals in IT that spanned multiple timescales of minutes, hours, and days. Average neural activity progressively decreased with familiarity as firing rates were strongest for novel, weaker for intermediately familiar, and weakest for highly familiar images. Neural signatures of familiarity learning were slow to develop as response reductions to initially-novel images emerged gradually over multiple days (or hundreds of views) of visual experience. In addition to slow changes that emerged across sessions, neural responses to novel images showed rapid decreases with familiarity within single sessions. To gain insight into the mechanisms underlying changes of visual responses with familiarity, we use computational modeling to investigate which plasticity rules are consistent with these changes. Learning rules inferred from the neural data revealed a strong diversity with many neurons following a negative plasticity rule as they exhibited synaptic depression over the course of learning across multiple days. A recurrent network model with two plasticity time constants - a slow time constant for long timescales and a fast time constant for short timescales - captured key dynamic features accompanying the transition from novel to familiar, including a gradual decrease in firing rates over multiple sessions, and a rapid decrease in firing rates within single sessions. Our findings suggest that distinct and complementary plasticity rules operating at different timescales may underlie the inferotemporal code for visual familiarity.

neuroscience↗

bioRxiv2023

Cortical network and projection neuron types that articulate serial order in a skilled motor behavior

The coordination of forelimb and orofacial movements to compose an ethological reach-to-consume behavior likely involves neural communication across brain regions. Leveraging wide-field imaging and photo-inhibition to survey across the cortex, we identified a cortical network and a high-order motor area (MOs-c), which coordinate action progression in a mouse reach-and-withdraw-to-drink (RWD) behavior. Electrophysiology and photo-inhibition across multiple projection neuron types within the MOs-c revealed differential contributions of pyramidal tract and corticothalamic (CTMOs) output channels to action progression and hand-mouth coordination. Notably, CTMOs display sustained firing throughout RWD sequence and selectively enhance RWD-relevant activity in postsynaptic thalamus neurons, which also contribute to action coordination. CTMOs receive converging monosynaptic inputs from forelimb and orofacial sensorimotor areas and are reciprocally connected to thalamic neurons, which project back to the cortical network. Therefore, motor cortex corticothalamic channel may selectively amplify the thalamic integration of cortical and subcortical sensorimotor streams to coordinate a skilled motor sequence.

neuroscience↗

bioRxiv2023

Attractor neural networks with double well synapses

It is widely believed that memory storage depends on activity-dependent synaptic modifications. Classical studies of learning and memory in neural networks describe synaptic efficacy either as continuous [1, 2] or discrete [2-4]. However, recent results suggest an intermediate scenario in which synaptic efficacy can be described by a continuous variable, but whose distribution is peaked around a small set of discrete values [5, 6]. Motivated by these results, we explored a model in which each synapse is described by a continuous variable that evolves in a potential with multiple minima. External inputs to the network can switch synapses from one potential well to another. Our analytical and numerical results show that this model can interpolate between models with discrete synapses which correspond to the deep potential limit [7], and models in which synapses evolve in a single quadratic potential [8]. We find that the storage capacity of the network with double-well synapses exhibits a power law dependence on the network size, rather than the logarithmic dependence observed in models with single well synapses [9]. In addition, synapses with deeper potential wells lead to more robust information storage in the presence of noise. When memories are sparsely encoded, the scaling of the capacity with network size is similar to previously studied network models in the sparse coding limit [2, 10-13].

neuroscience↗

bioRxiv2023

Dynamic control of sequential retrieval speed in networks with heterogeneous learning rules

Temporal rescaling of sequential neural activity has been observed in multiple brain areas during behaviors involving time estimation and motor execution at variable speeds. Temporally asymmetric Hebbian rules have been used in network models to learn and retrieve sequential activity, with characteristics that are qualitatively consistent with experimental observations. However, in these models sequential activity is retrieved at a fixed speed. Here, we investigate the effects of a heterogeneity of plasticity rules on network dynamics. In a model in which neurons differ by the degree of temporal symmetry of their plasticity rule, we find that retrieval speed can be controlled by varying external inputs to the network. Neurons with temporally symmetric plasticity rules act as brakes and tend to slow down the dynamics, while neurons with temporally asymmetric rules act as accelerators of the dynamics. We also find that such networks can naturally generate separate preparatory and execution activity patterns with appropriate external inputs.

neuroscience↗

bioRxiv2022

Mechanisms underlying reshuffling of visual responses by optogenetic stimulation in mice and monkeys

The ability to optogenetically perturb neural circuits opens an unprecedented window into mechanisms governing circuit function. We analyzed and theoretically modeled neuronal responses to visual and optogenetic inputs in mouse and monkey V1. In both species, optogenetic stimulation of excitatory neurons strongly modulated the activity of single neurons, yet had weak or no effects on the distribution of firing rates across the population. Thus, the optogenetic inputs reshuffled firing rates across the network. Key statistics of mouse and monkey responses lay on a continuum, with mice/monkeys occupying the low/high rate regions, respectively. We show that neuronal reshuffling emerges generically in randomly connected excitatory/inhibitory networks, provided the coupling strength (combination of recurrent coupling and external input) is sufficient that powerful inhibitory feedback cancels the mean optogenetic input. A more realistic model, distinguishing tuned visual vs. untuned optogenetic input in a structured network, reduces the coupling strength needed to explain reshuffling.

neuroscience↗

bioRxiv2022

Interplay between external inputs and recurrent dynamics during movement preparation and execution in a network model of motor cortex

The primary motor cortex has been shown to coordinate movement preparation and execution through computations in approximately orthogonal subspaces. The underlying network mechanisms, and the roles played by external and recurrent connectivity, are central open questions that need to be answered to understand the neural substrates of motor control. We develop a recurrent neural network model that recapitulates the temporal evolution of neuronal activity recorded from the primary motor cortex of a macaque monkey during an instructed delayed-reach task. In particular, it reproduces the observed dynamic patterns of covariation between neural activity and the direction of motion. We explore the hypothesis that the observed dynamics emerges from a synaptic connectivity structure that depends on the preferred directions of neurons in both preparatory and movement-related epochs, and we constrain the strength of both synaptic connectivity and external input parameters from data. While the model can reproduce neural activity for multiple combinations of the feedforward and recurrent connections, the solution that requires minimum external inputs is one where the observed patterns of covariance are shaped by external inputs during movement preparation, while they are dominated by strong direction-specific recurrent connectivity during movement execution, in agreement with recent experimental findings on the relationship between motor-cortical and motor-thalamic activity, both before and during movement execution. Our model also demonstrates that the way in which single-neuron tuning properties change over time can explain the level of orthogonality of preparatory and movement-related subspaces.

neuroscience↗

bioRxiv2021

Non-monotonic effects of GABAergic synaptic inputs on neuronal firing

GABA is generally known as the principal inhibitory neurotransmitter in the nervous system, usually acting by hyperpolarizing membrane potential. However, GABAergic currents sometimes exhibit non-inhibitory effects, depending on the brain region, developmental stage or pathological condition. Here, we investigate the diverse effects of GABA on the firing rate of several single neuron models, using both analytical calculations and numerical simulations. We find that GABAergic synaptic conductance and output firing rate exhibit three qualitatively different regimes as a function of GABA reversal potential, EGABA: monotonically decreasing for sufficiently low EGABA (inhibitory), monotonically increasing for EGABA above firing threshold (excitatory); and a non-monotonic region for intermediate values of EGABA. In the non-monotonic regime, small GABA conductances have an excitatory effect while large GABA conductances show an inhibitory effect. We provide a phase diagram of different GABAergic effects as a function of GABA reversal potential and glutamate conductance. We find that noisy inputs increase the range of EGABA for which the non-monotonic effect can be observed. We also construct a micro-circuit model of striatum to explain observed effects of GABAergic fast spiking interneurons on spiny projection neurons, including non-monotonicity, as well as the heterogeneity of the effects. Our work provides a mechanistic explanation of paradoxical effects of GABAergic synaptic inputs, with implications for understanding the effects of GABA in neural computation and development. Author summaryNeurons in nervous systems mainly communicate at chemical synapses by releasing neurotransmitters from the presynaptic side that bind to receptors on the post-synaptic side, triggering ion flow through ion channels on the cell membrane and changes in the membrane potential of the post-synaptic neuron. Gamma-aminobutyric acid (GABA) is the principal neurotransmitter expressed by inhibitory neurons. Its binding to GABAergic ionotropic receptors mainly causes a flow of chloride ions across the membrane, and typically hyperpolarizes the post-synaptic neuron, resulting in firing suppression. While GABA is canonically viewed as an inhibitory neurotransmitter, non-inhibitory effects have been observed in early stages of development, in stress-related disorders, and in specific parts of brain structures such as cortex, cerebellum and hippocampus [1-4]. Here, we employ analytical and computational approaches on spiking neuronal models to investigate the mechanisms of diverse effects of GABAergic synaptic inputs. We find that in addition to monotonically excitatory or monotonically inhibitory effects, GABAergic inputs show non-monotonic effects, for which the effect depends on the strength of the input. This effect is stronger in the presence of noise, and is observed in different models both at the single cell, and at the network level. Our findings provide a mechanistic explanation of several paradoxical experimental observations, with potential implications for neural network dynamics and computation.

neuroscience↗

bioRxiv2021

Multiple forms of working memory emerge from synapse-astrocyte interactions

Competing accounts propose that working memory (WM) is subserved either by persistent activity in single neurons, or by time-varying activity across a neural population, or by activity-silent mechanisms carried out by hidden internal states of the neural population. While WM is traditionally regarded to originate exclusively from neuronal interactions, cortical networks also include astrocytes that can modulate neural activity. We propose that different mechanisms of WM can be brought forth by astrocyte-mediated modulations of synaptic transmitter release. In this account, the emergence of different mechanisms depends on the networks spontaneous activity and the geometry of the connections between synapses and astrocytes.

neuroscience↗

bioRxiv2020

Emergence of irregular activity in networks of strongly coupled conductance-based neurons

Cortical neurons are characterized by irregular firing and a broad distribution of rates. The balanced state model explains these observations with a cancellation of mean excitatory and inhibitory currents, which makes fluctuations drive firing. In networks of neurons with current-based synapses, the balanced state emerges dynamically if coupling is strong, i.e. if the mean number of synapses per neuron K is large and synaptic efficacy is of order [Formula]. When synapses are conductance-based, current fluctuations are suppressed when coupling is strong, questioning the applicability of the balanced state idea to biological neural networks. We analyze networks of strongly coupled conductance-based neurons and show that asynchronous irregular activity and broad distributions of rates emerge if synaptic efficacy is of order 1/log(K). In such networks, unlike in the standard balanced state model, current fluctuations are small and firing is maintained by a drift-diffusion balance. This balance emerges dynamically, without fine tuning, if inputs are smaller than a critical value, which depends on synaptic time constants and coupling strength, and is significantly more robust to connection heterogeneities than the classical balanced state model. Our analysis makes experimentally testable predictions of how the network response properties should evolve as input increases.

neuroscience↗

bioRxiv2020

Altered spike timing-dependent plasticity rules in physiological calcium

Like many forms of long-term synaptic plasticity, spike-timing-dependent plasticity (STDP) depends on intracellular Ca2+ signaling for its induction. Yet, all in vitro studies devoted to STDP used abnormally high external Ca2+ concentration. We measured STDP at the CA3-CA1 hippocampal synapses under different extracellular Ca2+ concentrations and found that the sign, shape and magnitude of plasticity strongly depend on Ca2+. A pre-post protocol that results in robust LTP in high Ca2+, yielded only LTD or no plasticity in the physiological Ca2+ range. LTP could be restored by either increasing the number of post-synaptic spikes or increasing the pairing frequency. A calcium-based plasticity model in which depression and potentiation depend on post-synaptic Ca2+ transients was found to fit quantitatively all the data, provided NMDA receptor-mediated non-linearities were implemented. In conclusion, STDP rule is profoundly altered in physiological Ca2+ but specific activity regimes restore a classical STDP profile.

neuroscience↗