Biology subjects

Bardella, G.

Publications and source records attributed to Bardella, G..

9 recordsLinked to original sources

bioRxiv2026

Equivalent volitional learning emerges through circuit-specific population dynamics in motor cortex and hippocampus

Learning operates across different brain circuits to associate population activity patterns with desired outcomes, and to enable volitional reactivation of those patterns to control behavior. These circuits differ profoundly in their architecture and dynamical regimes, yet which features of learning are shared across them and which arise from circuit-specific implementations remains unknown. Here, we use a brain-computer interface (BCI) to train mice to modulate the activity of selected neuronal ensembles toward configurations that trigger reward delivery. By making reward delivery contingent directly on population activity, we impose an identical associative learning problem on two circuits with distinct dynamical regimes: the primary motor cortex (M1) and the hippocampal area CA3. Mice acquired robust volitional control in both regions, and learning produced a set of shared signatures across circuits, including modulation of reward-controlling neurons, network-level sparsification, and greater exploration of reward-related activity patterns. These signatures were underpinned by distinct population dynamics: M1 activity flowed continuously through reward-associated states, whereas CA3 activity traced approach-and-return dynamics around them. Recurrent network models endowed with distinct minimal connectivity constraints chosen to reflect the dominant dynamical regime associated with each region captured key features of these shared signatures and region-specific dynamics, indicating that local architectural constraints are sufficient to account for the distinct implementations of learning. These findings indicate that equivalent learning outcomes arise from divergent dynamical implementations across architecturally distinct circuits. This principled degeneracy reveals that learning is not a single canonical solution, but is implemented through multiple circuit-specific mechanisms shaped by local network architecture.

neuroscience↗

bioRxiv2026

Bridging Neurons to Behaviour: A Generative Neural Engine Mechanistically Rejects the Independent Race Model

The brain produces robust, low-dimensional behaviour from variable, high-dimensional activity. The dominant account of action control, the Independent Race Model (IRM), explains stopping as a "Go" and a "Stop" process racing independently, and predicts behaviour accurately. But independence is a behavioural assumption never confronted with neural dynamics, and the same premotor neurons drive both processes. We therefore trained a generative Deep Markov Model on premotor activity from two macaques; trained on neural data alone, it emergently reproduces the full reaction-time distribution, validating it as a proxy for the neural hardware. Used for in silico experiments, this engine reveals systematic violations of both of the IRMs independence axioms, with violations emerging from the geometry of a single shared manifold. Apparent independence was never real: an artifact of observing behaviour without seeing the manifold that produces it.

neuroscience↗

bioRxiv2025

Individual differences in learning and decision-making: the role of COMT Val158Met polymorphism in transitive inference

Understanding the ordinal relationships between items requires constructing a rank order supporting decision-making between options. This process depends on the ability to learn reciprocal relationships and to select the best option available when making a choice. In such forms of decision-making, the prefrontal cortex (PFC) plays a crucial role in encoding the relative value of alternatives as a decision is formed. Higher-order cognitive abilities are influenced by genetic factors that affect dopamine availability in the PFC, potentially contributing to individual differences. Here, we examined the performance of 83 participants in a transitive inference task (TI), grouped by genotype based on the Val158Met single-nucleotide polymorphism in the Catechol-O-Methyltransferase (COMT) gene. The task included a learning phase in which participants acquired the reciprocal relationships among a set of hierarchically ranked items (A>B>C>D>E>F), followed by a test phase in which they were required to compare all possible item pairs and select the higher-ranked one. While genotype did not significantly influence test-phase performance, it did affect learning efficiency. Specifically, Val homozygotes took a longer learning procedure than both heterozygotes and Met homozygotes during the learning phase. Drift diffusion modelling (DDM) revealed that task performance was explained by the efficiency of evidence accumulation, which was lower in Val homozygotes, accounting for their poorer performance not only during initial learning but also when required to switch to a reversed hierarchical structure (A<B<C<D<E<F). These findings suggest that individual differences in inferential decision-making and cognitive flexibility may be partially driven by genetically determined variations in prefrontal dopamine availability.

neuroscience↗

bioRxiv2025

Dynamics of oscillatory power subtends multiplexing of perceptual and mnemonic information within the premotor cortex

Flexible behaviour requires decision-making that integrates both perceptual and mnemonic-related information. While the dorsal premotor cortex (PMd) is known to support decision-making based on perceptual computations, its contributions to decisions based on mnemonic information for motor planning are underexplored. Here, investigating local field potential (LFP) oscillations in PMd of monkeys performing a transitive inference task required the formation and retrieval of mnemonic representations of an arbitrarily defined rank order among perceptual items. Our results highlight that a dynamic interplay between lower frequencies (Theta, Alpha, Beta) and high-Gamma oscillatory activity of LFP reflects a mechanism for accessing and manipulating memory-related information underlying decision-making. These findings provide evidence that the PMd plays a role in multiplexing both perceptual and mnemonic information, extending its competence beyond the association of perceptual input with motor decisions.

neuroscience↗

bioRxiv2024

The biomechanical state of the effector affects motor control and provides measures of single trial inhibition in a stop signal task

The Stop Signal Task (SST) has been the benchmark for studying the behavioral and physiological basis of movement generation and inhibition. In our study, we extended the scope beyond physiological findings related to muscle activity, focusing our analysis on the initial biomechanical state of the effector. By incorporating a force sensitive resistor (FSR), we continuously monitored the force applied by the effector (here the index finger) during a button release version of the SST. This modified task design allowed us to examine both the baseline force before the relevant Go signal was presented and during the covert state of movement preparation. Notably, variations in force over time in response to the Go signal revealed differences across trials where movement was either generated or successfully inhibited, depending on the amount of force during the baseline period. Specifically, higher baseline force was associated with a delayed movement generation, which simultaneously slowed down the force release, facilitating successful inhibition when requested. Our results highlight the influence of biomechanical variables in movement control, which should be accounted for by the models developed for investigating the physiology of this ability. NEW & NOTEWORTHYMovement involves changing the position of anatomical effectors, like a finger. The initial biomechanical state of an effector impacts movement generation and inhibition. Using the Stop-Signal task, we studied these factors by measuring the force applied to a mouse button before movement onset. Higher initial force delayed movement generation and slowed force release, aiding movement inhibition. This research links behavioral models of action-stopping with movement biomechanics, highlighting the effectors initial state importance.

neuroscience↗

bioRxiv2024

Spatio-temporal transformers for decoding neural movement control

Deep learning tools applied to high-resolution neurophysiological data have significantly progressed, offering enhanced decoding, real-time processing, and readability for practical applications. However, the design of artificial neural networks to analyze neural activity remains a challenge, requiring a delicate balance between efficiency in low-data regimes and the interpretability of the results. To this end, we introduce a novel specialized transformer architecture to analyze single-neuron spiking activity. We test our model on multi electrodes recordings from the dorsal premotor cortex (PMd) of non-human primates while performing a motor inhibition task. The proposed architecture provides a very early prediction of the correct movement direction - no later than 230ms after the Go signal presentation across animals - and can accurately forecast whether the movement will be generated or withheld before a Stop signal, unattended, is actually presented. We also analyze the internal dynamics of the model by computing the predicted correlations between time steps and between neurons at successive layers of the architecture. We find that their evolution mirrors previous theoretical analyses. Overall, our framework provides a comprehensive use case for the practical implementation of deep learning tools in motor control research.

animal behavior and cognition↗

bioRxiv2021

Different contribution of the monkey prefrontal and premotor dorsal cortex in decision-making supported by inferential reasoning

Several studies have reported similar neural modulations between brain areas of the frontal cortex, such as the dorsolateral prefrontal (DLPFC) and the premotor dorsal (PMd) cortex, in tasks requiring encoding of the abstract rules for selecting the proper action. Here we compared the neuronal modulation of the DLPFC and PMd of monkeys trained to choose the higher rank from a pair of abstract images (target item), selected from an arbitrarily rank-ordered set (A>B>C>D>E>F) in the context of a transitive inference task. Once acquired by trial-and-error, the ordinal relationship between pairs of adjacent images (i.e., A>B; B>C; C>D; D>E; E>F), monkeys were tested in indicating the ordinal relation between items of the list not paired during learning. During these decisions, we observed that the choice accuracy increased and the reaction time decreased as the rank difference between the compared items enhanced. This result is in line with the hypothesis that after learning, the monkeys built an abstract mental representation of the ranked items, where rank comparisons correspond to the items position comparison on this representation. In both brain areas, we observed higher neuronal activity when the target item appeared in a specific location on the screen with respect to the opposite position and that this difference was particularly enhanced at lower degrees of difficulty. By comparing the time evolution of the activity of the two areas, we observed that the neural encoding of target item spatial position occurred earlier in the DLPFC than in the PMd.

animal behavior and cognition↗

bioRxiv2021

Changing in the hierarchical organization of local information dynamics during motor decision in the premotor cortex of primates

Recent studies have explored functional and effective neural networks in animal models; however, the dynamics of information propagation among functional modules under cognitive control remain largely unknown. Here, we addressed the issue using Transfer Entropy and graph theory methods on mesoscopic neural activities recorded in the dorsal premotor cortex of rhesus monkeys. We focused our study on the decision time of a Stop-signal task, looking for patterns in the network configuration that could influence motor plan maturation when the Stop signal is provided. When comparing trials with successful inhibition to those with generated movement, the nodes of the network resulted organized into four clusters, hierarchically arranged, and distinctly involved in information transfer. Interestingly, the hierarchies and the strength of information transmission between clusters varied throughout the task, distinguishing between generated movements and canceled ones and corresponding to measurable levels of network complexity. Our results suggest a putative mechanism for motor inhibition in premotor cortex: a topological reshuffle of the information exchanged among ensembles of neurons. AUTHOR SUMMARYIn this study, we investigated the dynamics of information transfer among functionally identified neural modules during cognitive motor control. Our focus was on mesoscopic neural activities in the dorsal premotor cortex of rhesus monkeys engaged in a Stop-signal task. Leveraging multivariate Transfer Entropy and graph theory, we uncovered insights on how behavioral control shapes the topology of information transmission in a local brain network. Task phases modulated the strength and hierarchy of information exchange between modules, revealing the nuanced interplay between neural populations during generated and canceled movements. Notably, during successful inhibition, the network displayed a distinctive configuration, unveiling a novel mechanism for motor inhibition in the premotor cortex: a topological reshuffle of information among neuronal ensembles.

neuroscience↗

bioRxiv2020

Neuronal dynamics of signal selective motor plan cancellation in the macaque dorsal premotor cortex

Primates adopt various strategies to interact with the environment. Yet, no study has examined the effects of behavioral strategies with regard to how movement inhibition is implemented at the neuronal level. We modified a classical approach to study movement control (stop-task) by adding an extra signal - termed the Ignore signal - which influenced movement inhibition only under a specific strategy. We simultaneously recorded multisite neuronal activity from the dorsal premotor (PMd) cortex of macaque monkeys during a task and applied a state-space approach. As a result, we found that movement generation is characterized by neuronal dynamics that evolve between subspaces. When the movement is halted, this evolution is arrested and inverted. Conversely, when the Ignore signal is presented, inversion of the evolution is observed briefly and only when a specific behavioral strategy is adopted. Moreover, neuronal signatures during the inhibitory process were predictive of how PMd processes inhibitory signals, allowing the classification of the resulting behavioral strategy. Our data corroborate the PMd as a critical node in movement inhibition.

neuroscience↗