Biology subjects

Rougier, N. P.

Publications and source records attributed to Rougier, N. P..

9 recordsLinked to original sources

bioRxiv2026

A reduced multicompartment network model of CA1 theta-gamma oscillations under extracellular stimulation

Deep brain stimulation has demonstrated its therapeutic potential in modulating pathological oscillations associated with Parkinsons disease and epilepsy. However, its efficacy in treating disrupted theta-gamma phase-amplitude coupling seen in memory-related disorders, such as Alzheimers disease, remains poorly understood. While recent studies have targeted the entorhinal-hippocampal circuit, results remain inconsistent. This discrepancy stems from a lack of mechanistic understanding regarding how stimulation protocols affect this circuit. In this work, we present a reduced multicompartment model of the hippocampal CA1 area that reproduces theta-nested gamma oscillations characteristic of healthy neural activity during memory performance. The model comprises pyramidal, basket and OLM cells with simplified morphologies. We also incorporated CA3-to-CA1 axonal projections, providing a foundational framework for studying how stimulation-induced recruitment of afferent pathways modulates CA1 dynamics. By balancing computational efficiency with anatomical accuracy, our model enables systematic investigation of the effects of electrode placement and orientation, as well as stimulation amplitude and frequency on CA1 neural activity. We demonstrate that the excitatory response in CA1 is primarily driven by the recruitment of Schaffer collateral projections. Overall, this work provides a computationally efficient template for exploring diverse stimulation configurations and could be expanded for developing neuromodulatory strategies to restore physiological network dynamics. Author summaryDeep brain stimulation has shown success in treating Parkinsons disease by suppressing abnormal neural activity responsible for movement disorders. However, when applied to memory-related pathologies, such as Alzheimers disease, the therapeutic outcomes remain unpredictable, ranging from cognitive improvement to impairment. This discrepancy highlights a critical gap in our understanding of how stimulation protocols interact with neural dynamics of the targeted circuits. To address this, we developed a computationally efficient model of the hippocampus, which is involved in memory processes, in order to understand how deep brain stimulation might influence its activity. Our model maintains enough biological accuracy to capture essential memory-related neural activity while remaining lightweight enough for rapid execution and systematic exploration of different protocols. This computational efficiency allowed us to conduct systematic investigations of several stimulation configurations to study their effects on hippocampal dynamics. Overall, this model could provide a useful and computationally cost-efficient tool for exploring the mechanisms of deep brain stimulation and help optimize stimulation protocols aimed at alleviating memory disorders.

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bioRxiv2026

Dual pathway architecture in songbirds enables robust sensorimotor learning

The acquisition of sensorimotor skills critically depends on basal ganglia (BG)-thalamo-cortical circuits. Prevailing theories propose that the BG optimize motor output through reinforcement learning (RL), using internal performance evaluations to approximate stochastic gradient ascent. However, this framework struggles in non-convex performance landscapes, where local optima hinder efficient learning. Songbirds provide a compelling biological example of robust sensorimotor learning, mastering complex vocalizations through trial-and-error within a specialized BG-thalamo-cortical architecture. Here, we present a computational model constrained by the anatomy, physiology, and developmental trajectory of the zebra finch song system. The model combines a BG-driven RL pathway with a parallel cortical motor pathway that progressively consolidates successful motor patterns via Hebbian plasticity. In addition, we incorporate synaptic volatility within the BG pathway, introducing structured variability across learning. Through simulations of vocal learning using both a biophysical syrinx model and synthetic performance landscapes, we demonstrate that this dual-pathway architecture reliably converges to global optima and outperforms standard and noise-annealed RL approaches. The model reproduces key experimental features of song learning, including non-monotonic learning trajectories, a gradual reduction in motor variability, and the developmental transfer of motor control from subcortical to cortical circuits. Mechanistically, delayed maturation of the cortical pathway provides an implicit regulation of the exploration-exploitation trade-off, while synaptic volatility enables escape from local optima. These results highlight the importance of neural circuit architecture and dynamics in efficient learning, and suggest biologically inspired design principles for improving the robustness and sample efficiency of artificial RL systems in complex sensorimotor domains.

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bioRxiv2024

On the epistemic role of hippocampal cells: the case of splitter cells

During the past decades, hippocampal formation has undergone extensive studies, leading researchers to identify a vast collection of cells with functional properties. Several investigations, supported by carefully crafted models, have examined the origin of such cells. The most recent models hypothesize that temporal sequences underlie the observed spatial properties. We aim at investigating whether a random recurrent structure is sufficient to allow such latent sequence to appear. To do so, we simulated an agent with egocentric sensory inputs that must navigate and alternate choices at intersections. We were subsequently able to identify several splitter cells inside the model. Remarkably, when we systematically lesioned the identified splitter cells, the models behavioral performance remained intact: in the vast majority of cases, new splitter cells re-emerged through network reorganization, while in the remaining cases, the task was solved without any detectable splitter cells, demonstrating that splitter cells are not necessary to the task resolution. Position, orientation, and decision representations could also be successfully decoded from the reservoir activity, even after repeated lesioning. Subspace alignment analysis further revealed that this reorganization preserves the task-relevant population geometry while redistributing activity within the null sub-space, with the trajectory-encoding dimension rotating in neuron space across lesions. Together, these findings demonstrate that splitter cell activity is primarily task-driven and does not derive from a specific architecture or learning rule: splitter cells emerge generically across random recurrent networks that successfully solve the task, across a broad and robust range of dynamical parameters, and are not necessary for task performance. Our results therefore challenge the notion of functional necessity for specific neural populations.

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bioRxiv2023

A dynamical computational model of theta generation in hippocampal circuits to study theta-gamma oscillations during neurostimulation

Neurostimulation of the hippocampal formation has shown promising results for modulating memory but the underlying mechanisms remain unclear. In particular, the effects on hippocampal theta-nested gamma oscillations and theta phase reset, which are both crucial for memory processes, are unknown. Moreover, these effects cannot be investigated using current computational models, which consider theta oscillations with a fixed amplitude and phase velocity. Here, we developed a novel computational model that includes the medial septum, represented as a set of abstract Kuramoto oscillators producing a dynamical theta rhythm with phase reset, and the hippocampal formation, composed of biophysically-realistic neurons and able to generate theta-nested gamma oscillations under theta drive. We showed that, for theta inputs just below the threshold to induce self-sustained theta-nested gamma oscillations, a single stimulation pulse could switch the network behavior from non-oscillatory to a state producing sustained oscillations. Next, we demonstrated that, for a weaker theta input, pulse train stimulation at the theta frequency could transiently restore seemingly physiological oscillations. Importantly, the presence of phase reset influenced whether these two effects depended on the phase at which stimulation onset was delivered, which has practical implications for designing neurostimulation protocols that are triggered by the phase of ongoing theta oscillations. This novel model opens new avenues for studying the effects of neurostimulation on the hippocampal formation. Furthermore, our hybrid approach that combines different levels of abstraction could be extended in future work to other neural circuits that produce dynamical brain rhythms.

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bioRxiv2022

Dual pathway architecture underlying vocal learning in songbirds

Song acquisition and production in songbirds is governed by a dedicated neural circuitry that involves two parallel pathways: a motor pathway for the production and a basal ganglia (BG) pathway for the acquisition. Juveniles learn by imitating adult vocalizations and proceed by trial and error, errors being conveyed by a dopaminergic signal. The complex nature of the relationship between neural control and syrinx musculature makes song learning a complicated problem to solve. Reinforcement learning (RL) has been widely hypothesized to underlie such sensorimotor learning even though this can lead to sub-optimal solutions under uneven contours in continuous action spaces. In this article, we propose to re-interpret the role of a dual pathway architecture, underlying avian vocal learning, that helps overcome these limitations. We posit that the BG pathway conducts exploration by inducing large daily shifts in the vocal production while the motor pathway gradually consolidates this exploration. This process can be understood as a modified form of a simulated annealing process. Simulations on Gaussian performance landscapes and a syrinx-based performance landscape are demonstrated and compared with standard approaches. Taking behavioral constraints into account (60 days of learning, 1000 trials per day), the model allows to reach the global optimum in complex landscapes and thus provides a sound insight into the role of the dual pathway architecture underlying vocal learning.

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bioRxiv2019

Identification of distinct pathological signatures induced by patient-derived α-synuclein structures in non-human primates

AO_SCPLOWBSTRACTC_SCPLOWDopaminergic neuronal cell death, associated with intracellular -synuclein (-syn)-rich protein aggregates (termed Lewy bodies), is a well-established characteristic of Parkinsons disease. Much evidence, accumulated from multiple experimental models has suggested that -syn plays a role in PD pathogenesis, not only as a trigger of pathology but also as a mediator of disease progression through pathological spreading. Here we have used a machine learning-based approach to identify unique signatures of neurodegeneration in monkeys induced by distinct -syn pathogenic structures derived from PD patients. Unexpectedly, our results show that, in non-human primates, a small amount of singular -syn aggregates is as toxic as larger amyloid fibrils present in the LBs, thus reinforcing the need for preclinical research in this species. Furthermore, our results provide evidence supporting the true multifactorial nature of PD as multiple causes can induce similar outcome regarding dopaminergic neurodegeneration.

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bioRxiv2019

A Robust Model of Gated Working Memory

Gated working memory is defined as the capacity of holding arbitrary information at any time in order to be used at a later time. Based on electrophysiological recordings, several computational models have tackled the problem using dedicated and explicit mechanisms. We propose instead to consider an implicit mechanism based on a random recurrent neural network. We introduce a robust yet simple reservoir model of gated working memory with instantaneous updates. The model is able to store an arbitrary real value at random time over an extended period of time. The dynamics of the model is a line attractor that learns to exploit reentry and a non-linearity during the training phase using only a few representative values. A deeper study of the model shows that there is actually a large range of hyper parameters for which the results hold (number of neurons, sparsity, global weight scaling, etc.) such that any large enough population, mixing excitatory and inhibitory neurons can quickly learn to realize such gated working memory. In a nutshell, with a minimal set of hypotheses, we show that we can have a robust model of working memory. This suggests this property could be an implicit property of any random population, that can be acquired through learning. Furthermore, considering working memory to be a physically open but functionally closed system, we give account on some counter-intuitive electrophysiological recordings.

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bioRxiv2017

Modeling the Role of Striatum in Stochastic Multi Context Tasks

Decision making tasks in changing environments with probabilistic reward schemes present various challenges to the agents performing the task. These agents must use the experience gained in the past trials to characterize the environment which guides their actions. We present two models to predict an agents behavior in these tasks - a theoretical model which defines a Bayes optimal solution to the problem under realistic task conditions. The second is a computational model of the basal ganglia which presents a neural mechanism to solve the same. Both the models are shown to reproduce results in behavioral experiments and are compared to each other. This comparison allows us to characterize the theoretical model as a bound on the neural model and the neural model as a biologically plausible implementation of the theoretical model. Furthermore, we predict the performance of the agents in various stochastic regimes which could be tested in future studies.

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bioRxiv2017

Dual Competition between the Basal Ganglia and the Cortex: from Action-Outcome to Stimulus-Response

Action-outcome (A-O) and stimulus-response (S-R) processes that are two forms of instrumental conditioning that are important components of decision making and action selection. The former adapts its response according to the outcome while the latter is insensitive to the outcome. An unsolved question is how these two processes emerge, cooperate and interact inside the brain in order to issue a unique behavioral answer. Here we propose a model of the interaction between the cortex, the basal ganglia and the thalamus based on a dual competition. We hypothesize that the striatum, the subthalamic nucleus, the internal pallidum (GPi), the thalamus, and the cortex are involved in closed feedback loops through the hyperdirect and direct pathways. These loops support a competition process that results in the ability for the basal ganglia to make a cognitive decision followed by a motor decision. Considering lateral cortical interactions (short range excitation, long range inhibition), another competition takes place inside the cortex allowing this latter to make a cognitive and a motor decision. We show how this dual competition endows the model with two regimes. One is oriented towards action-outcome and is driven by reinforcement learning, the other is oriented towards stimulus-response and is driven by Hebbian learning. The final decision is made according to a combination of these two mechanisms with a gradual transfer from the former to the latter. We confirmed these theoretical results on primates using a two-armed bandit task and a reversible bilateral inactivation of the internal part of the globus pallidus.

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