Biology subjects

Hansel, D.

Publications and source records attributed to Hansel, D..

10 recordsLinked to original sources

bioRxiv2025

The fluctuation-based regime of thalamocortical circuitry

Neocortical neurons in primary visual cortex (V1) integrate excitatory inputs from both thalamic and cortical sources to generate selectivity for orientation, direction and disparity. Feedforward models have posited that thalamocortical input can drive these selectivities, but the relative sparsity of thalamic synapses raises questions about the sufficiency of this input. To determine whether thalamic drive is sufficient to evoke cortical responses, we quantified the current threshold required to evoke action potentials in vivo using whole-cell recordings in mouse V1. We then isolated thalamic input via optogenetic cortical silencing and compared its strength to the measured spiking threshold. We find that although average thalamic input is below the threshold for action potential generation, trial-by-trial fluctuations in thalamic drive are sufficient to evoke visually driven action potentials. These results demonstrate that thalamic excitation alone can drive cortical spiking, but its effectiveness depends on fluctuations in synaptic input.

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bioRxiv2024

Synchrony dynamics underlie irregular neocortical spiking

Cortical neurons are characterized by their variable spiking patterns. We challenge prevalent theories for the origin of spiking variability. We examine the specific hypothesis that cortical synchrony drives spiking variability in vivo. Using dynamic clamp, we demonstrate that intrinsic neuronal properties do not contribute substantially to spiking variability, but rather spiking variability emerges from weakly synchronous network drive. With large-scale electrophysiology we quantify the degree of synchrony and its time scale in cortical networks in vivo. We demonstrate that physiological levels of synchrony are sufficient to generate irregular responses found in vivo. Further, this synchrony shifts over timescales ranging from 25 to 200 ms, depending on the presence of external sensory input. Such shifts occur when the network moves from spontaneous to driven modes, leading naturally to a decline in response variability as observed across cortical areas. Finally, while individual neurons exhibit reliable responses to physiological drive, different neurons respond in a distinct fashion according to their intrinsic properties, contributing to stable synchrony across the neural network.

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bioRxiv2024

Reward-driven adaptation of movements requires strong recurrent basal ganglia-cortical loops

The basal ganglia (BG) are a collection of subcortical nuclei involved in motor control, sensorimotor integration, and procedural learning. They play a key role in the acquisition and adaptation of movements, a process driven by dopamine-dependent plasticity at cortico-striatal projections, which serve as BG input. However, BG output is not necessary for executing many well-learned movements. This raises a fundamental question: how can plasticity at BG input contribute to the acquisition and adaptation of movements which execution does not require BG output? Existing models of BG function often neglect the feedback dynamics within cortico-BG-thalamo-cortical circuitry and do not capture the interaction between the cortex and BG in movement generation and adaptation. In this work, we address the above question in a theoretical model of the BG-thalamo-cortical multiregional network, incorporating anatomical, physiological, and behavioral evidence. We examine how its dynamics influence the execution and reward-based adaptation of reaching movements. We demonstrate how the BG-thalamo-cortical network can shape cortical motor output through the combination of three mechanisms: (i) the diverse dynamics emerging from its closed-loop architecture, (ii) attractor dynamics driven by recurrent cortical connections, and (iii) reinforcement learning via dopamine-dependent cortico-striatal plasticity. Our study highlights the role of the cortico-BG-thalamo-cortical feedback in efficient visuomotor adaptation. It also suggests a mechanism for early-stage acquisition of reaching movements through motor babbling. More generally, our model explains how the BG-cortical network refines motor output through its intricate closed-loop dynamics and dopamine-dependent plasticity at cortico-striatal synapses. Significance statementThe basal ganglia (BG) are essential for learning and adapting movements, yet their output is often unnecessary for executing well-learned actions. This paradox challenges existing models of BG function. Here, we present a biologically grounded model of the cortico-BG-thalamo-cortical loop, showing how this closed-loop network supports efficient motor adaptation and early movement acquisition. Our findings reveal that dopamine-driven plasticity, recurrent cortical dynamics, and feedback from the BG together shape motor output, even when BG output is not directly required for execution. This work provides a unified framework explaining how the BG-cortical circuit contributes to both learning and refinement of goal-directed actions.

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bioRxiv2024

Idiosyncratic choice bias and feedback-induced bias differ in their long-term dynamics

A well-known observation in repeated-choice experiments is that a tendency to prefer one response over the others emerges if the feedback consistently favors that response. Choice bias, a tendency to prefer one response over the others, however, is not restricted to biased-feedback settings and is also observed when the feedback is unbiased. In fact, participant-specific choice bias, known as idiosyncratic choice bias (ICB), is common even in symmetrical experimental settings in which feedback is completely absent. Here we ask whether feedback-induced bias and ICB share a common mechanism. Specifically, we ask whether ICBs reflect idiosyncrasies in choice-feedback associations prior to the measurement of the ICB. To address this question, we compare the long-term dynamics of ICBs with feedback-induced biases. We show that while feedback effectively induced choice preferences, its effect is transient and diminished within several weeks. By contrast, we show that ICBs remained stable for at least 22 months. These results indicate that different mechanisms underlie the idiosyncratic and feedback-induced biases.

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bioRxiv2019

Mechanisms underlying the response of mouse cortical networks to optogenetic manipulation

GABAergic interneurons can be subdivided into three subclasses: parvalbumin positive (PV), somatostatin positive (SOM) and serotonin positive neurons. With principal cells (PCs) they form complex networks. We examine PCs and PV responses in mouse anterior lateral motor cortex (ALM) and barrel cortex (S1) upon PV photostimulation in vivo. In layer 5, the PV response is paradoxical: photoexcitation reduces their activity. This is not the case in ALM layer 2/3. We combine analytical calculations and numerical simulations to investigate how these results constrain the architecture. Two-population models cannot account for the results. Networks with three inhibitory populations and V1-like architecture account for the data in ALM layer 2/3. Our data in layer 5 can be accounted for if SOM neurons receive inputs only from PCs and PV neurons. In both four-population models, the paradoxical effect implies not too strong recurrent excitation. It is not evidence for stabilization by inhibition.

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bioRxiv2019

Spatiotemporal limits of optogenetic manipulations in cortical circuits

Neuronal inactivation is commonly used to assess the involvement of groups of neurons in specific brain functions. Optogenetic tools allow manipulations of genetically and spatially defined neuronal populations with excellent temporal resolution. However, the targeted neurons are coupled with other neural populations over multiple length scales. As a result, the effects of localized optogenetic manipulations are not limited to the targeted neurons, but produces spatially extended excitation and inhibition with rich dynamics. Here we benchmarked several optogenetic silencers in transgenic mice and with viral gene transduction, with the goal to inactivate excitatory neurons in small regions of neocortex. We analyzed the effects of the perturbations in vivo using electrophysiology. Channelrhodopsin activation of GABAergic neurons produced more effective photoinhibition of pyramidal neurons than direct photoinhibition using light-gated ion pumps. We made transgenic mice expressing the light-dependent chloride channel GtACR under the control of Cre-recombinase. Activation of GtACR produced the most potent photoinhibition. For all methods, localized photostimuli produced photoinhibition that extended substantially beyond the spread of light in tissue, although different methods had slightly different resolution limits (radius of inactivation, 0.5 mm to 1 mm). The spatial profile of photoinhibition was likely shaped by strong coupling between cortical neurons. Over some range of photostimulation, circuits produced the \"paradoxical effect\", where excitation of inhibitory neurons reduced activity in these neurons, together with pyramidal neurons, a signature of inhibition-stabilized neural networks. The offset of optogenetic inactivation was followed by rebound excitation in a light dose-dependent manner, which can be mitigated by slowly varying photostimuli, but at the expense of time resolution. Our data offer guidance for the design of in vivo optogenetics experiments and suggest how these experiments can reveal operating principles of neural circuits.

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bioRxiv2019

Neuronal activity and learning in local cortical networks are modulated by the action-perception state

During sensorimotor learning, neuronal networks change to optimize the associations between action and perception. In this study, we examine how the brain harnesses neuronal patterns that correspond to the current action-perception state during learning. To this end, we recorded activity from motor cortex while monkeys either performed a familiar motor task (movement-state) or learned to control the firing rate of a target neuron using a brain-machine interface (BMI-state). Before learning, monkeys were placed in an observation-state, where no action was required. We found that neuronal patterns during the BMI-state were markedly different from the movement-state patterns. BMI-state patterns were initially similar to those in the observation-state and evolved to produce an increase in the firing rate of the target neuron. The overall activity of the non-target neurons remained similar after learning, suggesting that excitatory-inhibitory balance was maintained. Indeed, a novel neural-level reinforcement-learning network model operating in a chaotic regime of balanced excitation and inhibition predicts our results in detail. We conclude that during BMI learning, the brain can adapt patterns corresponding to the current action-perception state to gain rewards. Moreover, our results show that we can predict activity changes that occur during learning based on the pre-learning activity. This new finding may serve as a key step toward clinical brain-machine interface applications to modify impaired brain activity.

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bioRxiv2018

Idiosyncratic choice bias in decision tasks naturally emerges from neuronal network dynamics

Idiosyncratic tendency to choose one alternative over others in the absence of an identified reason is a common observation in two-alternative forced-choice experiments. It is tempting to account for it as resulting from the (unknown) participant-specific history and thus treat it as a measurement noise. Here we quantify idiosyncratic choice biases in a perceptual discrimination task and a motor task. We report substantial and significant biases in both cases that cannot be accounted for by the experimental context. Then, we present theoretical evidence that even in idealized experiments, in which the settings are symmetric, idiosyncratic choice bias is expected to emerge from the dynamics of competing neuronal networks. We thus argue that idiosyncratic choice bias reflects the microscopic dynamics of choice and therefore is virtually inevitable in any comparison or decision task.

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bioRxiv2018

How strong are correlations in strongly recurrent neuronal networks?

Cross-correlations in the activity in neural networks are commonly used to characterize their dynamical states and their anatomical and functional organizations. Yet, how these latter network features affect the spatiotemporal structure of the correlations in recurrent networks is not fully understood. Here, we develop a general theory for the emergence of correlated neuronal activity from the dynamics in strongly recurrent networks consisting of several populations of binary neurons. We apply this theory to the case in which the connectivity depends on the anatomical or functional distance between the neurons. We establish the architectural conditions under which the system settles into a dynamical state where correlations are strong, highly robust and spatially modulated. We show that such strong correlations arise if the network exhibits an effective feedforward structure. We establish how this feedforward structure determines the way correlations scale with the network size and the degree of the connectivity. In networks lacking an effective feedforward structure correlations are extremely small and only weakly depend on the number of connections per neuron. Our work shows how strong correlations can be consistent with highly irregular activity in recurrent networks, two key features of neuronal dynamics in the central nervous system.

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bioRxiv2016

A Canonical Neural Mechanism for Behavioral Variability

The ability to generate variable movements is essential for learning and adjusting complex behaviors. This variability has been linked to the temporal irregularity of neuronal activity in the central nervous system. However, how neuronal irregularity actually translates into behavioral variability is unclear. Here we combine modeling, electrophysiological and behavioral studies to address this issue. We demonstrate that a model circuit comprising topographically organized and strongly recurrent neural networks can autonomously generate irregular motor behaviors. Simultaneous recordings of neurons in singing finches reveal that neural correlations increase across the circuit driving song variability, in agreement with the model predictions. Analyzing behavioral data, we find remarkable similarities in the babbling statistics of 5-6 month-old human infants and juveniles from three songbird species, and show that our model naturally accounts for these universal statistics.

neuroscience↗