Biology subjects

Zerlaut, Y.

Publications and source records attributed to Zerlaut, Y..

10 recordsLinked to original sources

bioRxiv2024

A dendritic substrate for temporal diversity of cortical inhibition

In the mammalian neocortex, GABAergic interneurons (INs) inhibit cortical networks in profoundly different ways. The extent to which this depends on how different INs process excitatory signals along their dendrites is poorly understood. Here, we reveal that the functional specialization of two major populations of cortical INs is determined by the unique association of different dendritic integration modes with distinct synaptic organization motifs. We found that somatostatin (SST)-INs exhibit NMDAR-dependent dendritic integration and uniform synapse density along the dendritic tree. In contrast, dendrites of parvalbumin (PV)-INs exhibit passive synaptic integration coupled with proximally enriched synaptic distributions. Theoretical analysis shows that these two dendritic configurations result in different strategies to optimize synaptic efficacy in thin dendritic structures. Yet, the two configurations lead to distinct temporal engagement of each IN during network activity. We confirmed these predictions with in vivo recordings of IN activity in the visual cortex of awake mice, revealing a rapid and linear recruitment of PV-INs as opposed to a long-lasting integrative activation of SST-INs. Our work reveals the existence of distinct dendritic strategies that confer distinct temporal representations for the two major classes of neocortical INs and thus dynamics of inhibition.

neuroscience↗

bioRxiv2024

Feedforward circuits enable modality-specific control of cortical inhibition during behavioral state transitions.

Local inhibitory neurons (INs), specifically those involved in disinhibitory circuits like somatostatin (SST) and vasoactive intestinal peptide (VIP)-positive INs, are essential for regulating cortical function during behavior. However, it is unclear the mechanisms by which these INs are recruited during active states and whether their activity is consistent across different sensory cortices. We now reveal that in mice, locomotor activity strongly recruits SST-INs in primary somatosensory (S1) but not visual (V1) cortex. This diverse engagement of SST-INs could not be explained by differences in VIP-IN function but was absent in the presence of visual sensory drive suggesting involvement of feedforward sensory pathways. In agreement, inactivating the somatosensory thalamus, but not decreasing VIP-INs activity, significantly reduces the recruitment of SST-INs in S1 by locomotion. Finally, model simulations suggest differences in SST-INs activity can be explained by varying ratios of VIP-driven inhibition and thalamus-driven cortical excitation. Our work suggests that by integrating feedforward activity with neuromodulation, SST-INs play a central role in adapting sensory processing to behavioral states.

neuroscience↗

bioRxiv2022

A multi-layer mean-field model for the cerebellar cortex: design, validation, and prediction

Mean-field (MF) models can be used to summarize in a few statistical parameters the salient properties of an inter-wired neuronal network incorporating different types of neurons and synapses along with their topological organization. MF are crucial to efficiently implement the modules of large-scale brain models maintaining the specificity of local microcircuits. While MFs have been generated for the isocortex, they are still missing for other parts of the brain. Here we have designed and simulated a multi-layer MF of the cerebellar network (including Granule Cells, Golgi Cells, Molecular Layer Interneurons, and Purkinje Cells) and validated it against experimental data and the corresponding spiking neural network (SNN) microcircuit model. The cerebellar MF was built using a system of equations, where properties of neuronal populations and topological parameters are embedded in inter-dependent transfer functions. The model time constant was optimised using local field potentials recorded experimentally from acute mouse cerebellar slices as a template. The MF satisfactorily reproduced the average dynamics of the different neuronal populations in response to various input patterns and predicted the modulation of Purkinje Cells firing depending on cortical plasticity, which drives learning in associative tasks, and the level of feedforward inhibition. The cerebellar MF provides a computationally efficient tool that will allow to investigate the causal relationship between microscopic neuronal properties and ensemble brain activity in virtual brain models addressing both physiological and pathological conditions.

bioengineering↗

bioRxiv2022

Network States Classification based on Local Field Potential Recordings in the Awake Mouse Neocortex

Recent studies using intracellular recordings in awake behaving mice revealed that cortical network states, defined based on membrane potential features, modulate sensory responses and perceptual outcomes. Single cell intracellular recordings are difficult to achieve and have low yield compared to extracellular recordings of population signals, such as local field potentials (LFPs). However, it is currently unclear how to identify these behaviorally-relevant network states from the LFP. We used simultaneous LFP and intracellular recordings in the somatosensory cortex of awake mice to design and calibrate a model-based analysis method, the Network State Index (NSI), that enables network state classification from the LFP. We used the NSI to analyze the relationship between single-cell (intracellular) and population (LFP) signals over different network states of wakefulness. We found that graded levels of population signal faithfully predicted the levels of single cell depolarization in non-rhythmic regimes whereas, in delta ([2-4 Hz]) oscillatory regimes, the graded levels of rhythmicity in the LFP mapped into a stereotypical oscillatory pattern of membrane potential. Finally, we showed that the variability of network states, beyond the occurrence of slow oscillatory activity, critically shaped the average correlations between single cell and population signals. NSI-based characterization provides a ready-to-use tool to understand from LFP recordings how the modulation of local network dynamics shapes the flexibility of sensory processing during behavior. Significance statementSensation during behaviour is strongly modulated by the animals internal state. Such context-dependent modulation of sensory processing is believed to largely stem from top-down control of network states in sensory cortices, with different network states being associated with distinct computational properties of the circuit. So far, a detailed characterization of network states in the awake cortex has mostly been achieved through single-cell intracellular recordings, which however cannot be easily recorded. Here, we developed a new method to classify network states from the easily accessible extracellular LFP recordings of population activity. Given the widespread use of LFPs, our work provides a critical methodology to greatly expand our understanding of the mechanisms underlying state-dependent computations in neocortex.

neuroscience↗

bioRxiv2021

Activity-dependent modulation of NMDA receptors by endogenous zinc shapes dendritic function in cortical neurons.

Activation of NMDA receptors (NMDARs) has been proposed to be a key component of single neuron computations in vivo. However is unknown if specific mechanisms control the function of such receptors and modulate input-output transformations performed by cortical neurons under in vivo-like conditions. Here we found that in layer 2/3 pyramidal neurons (L2/3 PNs), repeated synaptic stimulation results in an activity-dependent decrease in NMDARs activity by vesicular zinc. Such a mechanism shifted the threshold for dendritic non-linearities and strongly reduced LTP induction. Modulation of NMDARs was cell- and pathway-specific, being present selectively in L2/3-L2/3 connections but absent in ascending bottom-up inputs originating from L4 neurons. Numerical simulations highlighted that activity-dependent modulation of NMDARs has an important influence in dendritic computations endowing L2/3 PN dendrites with the ability to sustain dendritic non-linear integrations constant across different regimes of synaptic activity like those found in vivo. The present results therefore provide a new perspective on the action of vesicular zinc in cortical circuits by highlighting the role of this endogenous ion in normalizing dendritic integration of PNs during a constantly changing synaptic input pattern.

neuroscience↗

bioRxiv2019

A mean-field approach to the dynamics of networks of complex neurons, from nonlinear Integrate-and-Fire to Hodgkin-Huxley models

We present a mean-field formalism able to predict the collective dynamics of large networks of conductance-based interacting spiking neurons. We apply this formalism to several neuronal models, from the simplest Adaptive Exponential Integrate-and-Fire model to the more complex Hodgkin-Huxley and Morris-Lecar models. We show that the resulting mean-field models are capable of predicting the correct spontaneous activity of both excitatory and inhibitory neurons in asynchronous irregular regimes, typical of cortical dynamics. Moreover, it is possible to quantitatively predict the populations response to external stimuli in the form of external spike trains. This mean-field formalism therefore provides a paradigm to bridge the scale between population dynamics and the microscopic complexity of the individual cells physiology. NEW & NOTEWORTHYPopulation models are a powerful mathematical tool to study the dynamics of neuronal networks and to simulate the brain at macroscopic scales. We present a mean-field model capable of quantitatively predicting the temporal dynamics of a network of complex spiking neuronal models, from Integrate-and-Fire to Hodgkin-Huxley, thus linking population models to neurons electrophysiology. This opens a perspective on generating biologically realistic mean-field models from electrophysiological recordings.

neuroscience↗

bioRxiv2018

The Spectrum of Asynchronous Dynamics in Spiking Networks: A Theory for the Diversity of Non-Rhythmic Waking states in Neocortex

The cerebral cortex of awake animals exhibits frequent transitions between diverse non-rhythmic network states. However, it is still unclear how these different activity states emerge within the same network and how each state impacts network function. Here, we demonstrate that model networks of spiking neurons with moderate recurrent interactions dynamically change their asynchronous dynamics depending upon the level of afferent excitation. We found that the model network displayed a spectrum of asynchronous states, ranging from afferent input-dominated (AD) regimes, characterized by unbalanced synaptic currents and sparse firing, to recurrent input-dominated (RD) regimes, characterized by balanced synaptic currents and dense firing. The model predicted regime-specific relationships between several different neural biophysical properties which were all experimentally confirmed by intracellular recordings in the somatosensory cortex of awake mice. Moreover, theoretical analysis showed that AD regimes more precisely encode spatiotemporal patterns of presynaptic activity, while RD regimes better encoded the strength of afferent inputs. These results provide a theoretical foundation for how recurrent neocortical circuits generate non-rhythmic waking states and how these different states modulate the processing of incoming information.

neuroscience↗

bioRxiv2018

Suppressive waves disambiguate the representation of long-range apparent motion in awake monkey V1

The \"apparent motion\" illusion is evoked when stationary stimuli are successively flashed in spatially separated positions. It depends on the precise spatial and temporal separations of the stimuli. For large spatiotemporal separation, the long-range apparent motion (lrAM), it remains unclear how the visual system computes unambiguous motion signals. Here we investigated whether intracortical interactions within retinotopic maps could shape a global motion representation at the level of V1 population in response to a lrAM. In fixating monkeys, voltage-sensitive dye imaging revealed the emergence of a spatio-temporal representation of the motion trajectory at the scale of V1 population activity, shaped by systematic backward suppressive waves. We show that these waves are the expected emergent property of a recurrent gain control fed by the horizontal intra-cortical network. Such non-linearities explain away ambiguous correspondence problems of the stimulus along the motion path, preformating V1 population response for an optimal read-out by downstream areas.

neuroscience↗

bioRxiv2017

Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons

Voltage-sensitive dye imaging (VSDi) has revealed fundamental properties of neocortical processing at macroscopic scales. Since for each pixel VSDi signals report the average membrane potential over hundreds of neurons, it seems natural to use a mean-field formalism to model such signals. Here, we present a mean-field model of networks of Adaptive Exponential (AdEx) integrate-and-fire neurons, with conductance-based synaptic interactions. We study here a network of regular-spiking (RS) excitatory neurons and fast-spiking (FS) inhibitory neurons. We use a Master Equation formalism, together with a semi-analytic approach to the transfer function of AdEx neurons to describe the average dynamics of the coupled populations. We compare the predictions of this mean-field model to simulated networks of RS-FS cells, first at the level of the spontaneous activity of the network, which is well predicted by the analytical description. Second, we investigate the response of the network to time-varying external input, and show that the mean-field model predicts the response time course of the population. Finally, to model VSDi signals, we consider a one-dimensional ring model made of interconnected RS-FS mean-field units. We found that this model can reproduce the spatio-temporal patterns seen in VSDi of awake monkey visual cortex as a response to local and transient visual stimuli. Conversely, we show that the model allows one to infer physiological parameters from the experimentally-recorded spatio-temporal patterns.

neuroscience↗

bioRxiv2016

Heterogeneous firing responses predict diverse couplings to presynaptic activity in mice layer V pyramidal neurons

In this study, we present a theoretical framework combining experimental characterizations and analytical calculus to capture the firing rate input-output properties of single neurons in the fluctuation-driven regime. Our framework consists of a two-step procedure to treat independently how the dendritic input translates into somatic fluctuation variables, and how the latter determine action potential firing. We use this framework to investigate the functional impact of the heterogeneity in firing responses found experimentally in young mice layer V pyramidal cells. We first design and calibrate in vitro a simplified morphological model of layer V pyramidal neurons with a dendritic tree following Rall's branching rule. Then, we propose an analytical derivation for the membrane potential fluctuations at the soma as a function of the properties of the synaptic input in dendrites. This mathematical description allows us to easily emulate various forms of synaptic input: either balanced, unbalanced, synchronized, purely proximal or purely distal synaptic activity. We find that those different forms of input activity lead to various impact on the membrane potential fluctuations properties, thus raising the possibility that individual neurons will differentially couple to specific forms of activity as a result of their different firing response. We indeed found such a heterogeneous coupling between synaptic input and firing response for all types of presynaptic activity. This heterogeneity can be explained by different levels of cellular excitability in the case of the balanced, unbalanced, synchronized and purely distal activity. A notable exception appears for proximal dendritic inputs: increasing the input level can either promote firing response in some cells, or suppress it in some other cells whatever their individual excitability. This behavior can be explained by different sensitivities to the speed of the fluctuations, which was previously associated to different levels of sodium channel inactivation and density. Because local network connectivity rather targets proximal dendrites, our results suggest that this aspect of biophysical heterogeneity might be relevant to neocortical processing by controlling how individual neurons couple to local network activity.

neuroscience↗