Biology subjects

Leblois, A.

Publications and source records attributed to Leblois, A..

9 recordsLinked to original sources

bioRxiv2026

Generation mechanisms and species-specific properties of parkinsonian beta oscillations in the basal ganglia

The basal ganglia (BG), thalamus and cortex form the BG-thalamo-cortical (BGTC) network, which is essential for voluntary movement and centrally involved in Parkinson's disease (PD). In both patients and animal models, BG neuronal activity exhibits exaggerated oscillatory synchronization in the beta frequencies (13-30 Hz). Theoretical studies have proposed multiple network mechanisms for the generation of these abnormal beta oscillations. However, key properties, such as frequency and power distribution across BG nuclei, vary substantially among patients and between animal models (rodents vs. non-human primates). This variability complicates direct comparisons between theoretical models and experimental data, and questions whether distinct neuronal mechanisms may underlie beta oscillations across species. In an experimentally constrained BGTC network model with species-specific parameters (synaptic, neuronal, and network properties), we evaluate the features of abnormal beta oscillatory activity generated by different mechanisms. The network's negative feedback loops serve as potential sources of spontaneous oscillations. Using rodent- or primate-constrained models, we compare the spectral properties of oscillatory activity across network populations for each loop and derive the expected phase relationships between populations to align predictions with existing rodent data and propose testable hypotheses for primates. We also demonstrate how oscillation frequency can be modulated when multiple generation mechanisms interact as coupled oscillators in the full network. Our results, combined with observed cross-species beta oscillations characteristics, suggest that abnormal beta oscillations likely arise from distinct mechanisms in rodents and primates.

neuroscience↗

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.

neuroscience↗

bioRxiv2025

A lobule-specific neuronal representation of song temporal structure in the songbird cerebellum

The cerebellum is involved in the acquisition and production of speech as revealed by clinical evidence and imaging studies, but its specific role however remains unclear. Songbirds provide a unique model to study the neural mechanisms of speech learning and production. Recent evidence highlights a contribution of the cerebellum to syllable duration in songbirds. Here, we aim at better understanding how and which cerebellar circuits may contribute to the tight control of syllable duration in zebra finches. We first confirmed that lesions in the lateral cerebellum affect syllable duration. We then recorded neuronal activity in the various cerebellar lobules in response to song playback and during singing with or without auditory feedback perturbation. We found that many cerebellar lobules receive non-selective auditory information locked to syllable boundaries during song playback, both in anaesthetized and awake behaving birds. During singing, cerebellar neurons in several lobules display song-locked responses with varying degrees of alignment to their playback responses and no alteration during auditory feedback perturbation. Singing-related activity tends to correlate with the fluctuations in syllable duration rather than any acoustic song feature. Importantly, neurons in lobule IV of the cerebellum are largely unaffected by auditory stimulation but display syllable-locked firing rate modulations during singing that precisely encode syllable boundaries with a sharp and tight increase in firing at syllable onsets and offsets. Altogether, our findings reveal cerebellar signals that may contribute to the control of the duration of vocal elements during singing, possibly serving as a forward model of song temporal features.

neuroscience↗

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.

neuroscience↗

bioRxiv2024

Neural population dynamics during sleep in a songbird vocal circuit resemble sharp wave ripple activity

Similar to motor skill learning in mammals, vocal learning in songbirds requires a set of interconnected brain areas that make up an analogous basal ganglia-thalamocortical circuit known as the anterior forebrain pathway (AFP). Although neural activity in the AFP has been extensively investigated during awake singing, very little is known about its neural activity patterns during other behavioral states. Here, we used chronically implanted Neuropixels probes to investigate spontaneous neural activity in the AFP during natural sleep and awake periods in male zebra finches. We found that during sleep, neuron populations in the pallial region LMAN (lateral magnocellular nucleus of the nidopallium) spontaneously exhibited synchronized bursts that were characterized by a negative sharp deflection in the local field potential (LFP) and a transient increase in gamma power. LMAN population bursts occurred primarily during non-rapid eye movement (NREM) sleep and were highly reminiscent of sharp-wave ripple (SWR) activity observed in rodent hippocampus. We also examined the functional connectivity within the AFP by calculating the pairwise LFP coherence. As expected, delta and theta band coherence within LMAN and Area X was higher during sleep compared to awake periods. Contrary to our expectations, we did not observe strong coherence between LMAN and Area X during sleep, suggesting that the input from LMAN into Area X is spatially restricted. Overall, these results provide the first description of spontaneous neural dynamics within the AFP across behavioral states. Significance statementAlthough cortical and basal ganglia circuits are known to be required for learning in both mammals and birds, little is known about the ongoing spontaneous activity patterns within these circuits, or how they are modulated by behavioral state. Here we prove the first description of cortical-basal ganglia network activity during sleep and awake periods in birds. Within the pallial area LMAN, we observed population-wide bursting events that were highly reminiscent of hippocampal sharp-wave ripple (SWR) activity, suggesting that large-scale population events have diverse functions across vertebrates.

neuroscience↗

bioRxiv2023

Synaptic changes in pallidostriatal circuits observed in parkinsonian model triggers abnormal beta synchrony with accurate spatio-temporal properties across the basal ganglia

Excessive oscillatory activity across basal ganglia (BG) nuclei in the {beta} frequencies (12-30Hz) is a hallmark of Parkinsons disease (PD). While the link between oscillations and symptoms remains debated, exaggerated {beta} oscillations constitute an important biomarker for therapeutic effectiveness in PD. The neuronal mechanisms of {beta}-oscillation generation however remain unknown. Many existing models rely on a central role of the subthalamic nucleus (STN) or cortical inputs to BG. Contrarily, neural recordings and optogenetic manipulations in normal and parkinsonian rats recently highlighted the central role of the external pallidum (GPe) in abnormal {beta} oscillations, while showing that the integrity of STN or motor cortex is not required. Here, we evaluate the mechanisms for the generation of abnormal {beta} oscillations in a BG network model where neuronal and synaptic time constants, connectivity, and firing rate distributions are strongly constrained by experimental data. Guided by a mean-field approach, we show in a spiking neural network that several BG sub-circuits can drive oscillations. Strong recurrent STN-GPe connections or collateral intra-GPe connections drive gamma oscillations (> 40Hz), whereas strong pallidostriatal loops drive low-{beta} (10-15Hz) oscillations. We show that pathophysiological strengthening of striatal and pallidal synapses following dopamine depletion leads to the emergence of synchronized oscillatory activity in the mid-{beta} range with spike-phase relationships between BG neuronal populations in-line with experiments. Furthermore, inhibition of GPe, contrary to STN, abolishes oscillations. Our modeling study uncovers the neural mechanisms underlying PD {beta} oscillations and may thereby guide the future development of therapeutic strategies. Significance statementIn Parkinsons disease, neural activity in subcortical nuclei called the basal ganglia displays abnormal oscillatory synchronization that constitutes an important biomarker for therapeutic effectiveness. The neural mechanisms for the generation of these oscillations remain unknown. Here, in a theoretical neuronal network model strongly constrained by anatomical and physiological data, we show that specific circuit modifications in basal ganglia connectivity during Parkinsons disease lead to the emergence of synchronized oscillatory activity in the network with properties that strongly agree with available experimental evidence. This and future theoretical investigations of the neural mechanisms underlying abnormal neuronal activity in Parkinsons disease are necessary to guide the future development of therapeutic strategies to ameliorate symptoms.

neuroscience↗

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.

neuroscience↗

bioRxiv2017

A subcortical circuit linking the cerebellum to the basal ganglia engaged in vocal learning

Speech is a complex sensorimotor skill, and vocal learning involves both the basal ganglia and the cerebellum. These subcortical structures interact indirectly through their respective loops with thalamo-cortical and brainstem networks, and directly via subcortical pathways, but the role of their interaction during sensorimotor learning remains undetermined. While songbirds and their song-dedicated basal ganglia-thalamo-cortical circuitry offer a unique opportunity to study subcortical circuits involved in vocal learning, the cerebellar contribution to avian song learning remains unknown. We demonstrate that the cerebellum provides a strong input to the song-related basal ganglia nucleus in zebra finches. Cerebellar signals are transmitted to the basal ganglia via a disynaptic connection through the thalamus and then conveyed to their cortical target and to the premotor nucleus controlling song production. Finally, cerebellar lesions impair juvenile song learning, opening new opportunities to investigate how subcortical interactions between the cerebellum and basal ganglia contribute to sensorimotor learning.

neuroscience↗

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↗