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Pani, P.

Publications and source records attributed to Pani, P..

18 recordsLinked to original sources
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

In search for the invisible: motor inhibition in monkey premotor cortex and its RNN replicas

Controlling actions in dynamically changing environments requires flexible and efficient motor control. A fundamental challenge in neuroscience is to uncover how cortical circuits generate, adjust, and sometimes suppress planned movements. To address this, we combined recordings from dorsal premotor cortex (PMd) of macaque monkeys performing a stop-signal task, with a recurrent neural network (RNN) model inferred directly from multi-unit activity. This data-driven "digital twin" reproduced the cortical population dynamics underlying motor planning and inhibition, revealing how internal network states shape behavior, and generating synthetic neural trajectories for unseen conditions. RNN internal state explained reaction time fluctuations across trials, reflecting stochastic components of motor readiness and endogenous variability of PMd activity. The same pre-Go latent state also constrained movement inhibition modulating the networks response to Stop signals by reshaping the attractor dynamics of a null-potent subspace. These results establish a mechanistic link between latent cortical dynamics and flexible behavioral control, demonstrating how autonomous RNN inference can uncover circuit-level computations.

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

The difficulty in numerical computation impacts motor decisions in a Stop Signal task

The proper interpretation of environmental information is necessary for effective decision-making. The resulting cognitive burden may affect the entire process if interpretation is not instantaneous. In this study, we investigated how numerical distance (ND), a measure of cognitive demand in numerical comparisons, influences movement initiation and inhibition. To this end, 32 participants completed a novel numerical comparison Stop-Signal Task (NC-SST), in which the cognitive demand of each trial was manipulated by varying the ND between pairs of numbers in both Go and Stop signals. Participants were required to initiate or stop a movement if an upcoming number was higher or smaller than the one presented previously. Results showed that larger NDs (i.e., easier comparisons) facilitated faster and more accurate responses during movement initiation and enhanced stopping performance. Using a generalized drift-diffusion model, we found that drift rates increased with ND and were modulated by the spatial location of numerical stimuli, consistent with a left-to-right space number association. A generalized linear mixed-effects model further revealed that Go process parameters, particularly the drift rate, strongly predicted successful stopping and interacted with Stop ND and Stop signal delay (SSD). These findings demonstrate that higher cognitive load impairs both movement initiation and inhibition, and that motor decisions result from the integration of cognitive information onto perceptual features, extending the classical race model framework.

neuroscience↗
bioRxiv2024

Probing the edge of synchronization: Slow waves erode resting activity in behaving monkeys

Brain networks oscillate between sleep and wakefulness, following circadian rhythms. Theoretical models suggest distinct phases within this cycle, separated by a critical point where long-range activity patterns emerge, an advantageous condition for information processing in cortical networks. However, the exact nature of this critical dynamics remains elusive. A key question is whether the brain operates at this critical point during cognitive tasks or only during resting wakefulness. Here, we analyzed neural signals from the premotor cortex (PMC) of two macaque monkeys engaged in a delayed-reaching task and under drug-induced unconsciousness. We found evidence of criticality during resting periods at the end of behavioral trials in the awake state. This scale-free activity appeared as coordinated traveling waves, like those observed during anesthesia. As predicted by spiking networks models, activity-dependent adaptation influences wave size, supporting the hypothesis that the PMC operates near a synchronization phase transition while avoiding it during active behaviors.

neuroscience↗
bioRxiv2024

From solo to collaborative: the global increase in neuroscience authors over two decades

The increase in the number of authors per article is a well-documented phenomenon across various academic disciplines. While prior studies have examined this trend in specific fields and countries, they in most cases did not compare the increase in the number of authors between countries. While it has previously shown that the number of authors in neuroscience publications has risen in the G10 countries, no study has yet addressed whether it reflects a global trend in the field. To address this gap, we quantified the global trend in the number of authors in neuroscience publications from 2001 to 2022. Our findings reveal a consistent increase in authorship across nearly all the countries examined. Italy ranks highest in terms of average authorship per article, while Ukraine ranks the lowest. On the other hand, China shows the largest increase in authorship over the years, followed by Norway and Egypt. South Korea is the only country showing a slight decreasing trend rather than growth. These results contribute to a better understanding of authorship patterns in neuroscience, and can stimulate further investigations on the reasons behind such an increase in terms of socio-economic factors, the need for collaborative efforts in some fields, or, on the negative side, the effect of utilitarian reasons to meet career evaluation criteria.

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

Balancing postural control and motor inhibition during gait initiation

This study examines the relationship between stopping a planned gait initiation due to sudden environmental changes and maintaining body stability. Using a gait initiation version of the Stop Signal Task (SST), we studied changes in anticipatory postural adjustments (APA) during gait initiation and suppression. We found that trial-level variables, such as the time to start or stop stepping, interacted with biomechanical factors like the center of mass displacement relative to the base of support, affecting performance. A critical biomechanical threshold was identified, beyond which stopping movement was unlikely. These findings highlight the strong link between limb action control and body equilibrium, offering a framework within a motor control paradigm. By integrating biomechanical elements, the model effectively simulates real-life scenarios, identifying key variables for studying neural correlations between action and postural control, and aiding in the development of injury prevention and rehabilitation tools for individuals with movement and posture impairments.

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↗
bioRxiv2024

The Increasing Authorship trend in Neuroscience: A scientometric analysis across 11 countries

Previous studies have demonstrated an increasing trend of the number of authors across various fields over the years. This trend has been attributed to the necessity for larger collaborations and, at times, to ethical issues regarding authorship attribution. Our study focuses on the evolution of authorship trends in the field of Neuroscience. We conducted our analysis based on a dataset containing 576,647 neuroscience publications produced from 2000 to 2022, focusing on the publications within the Group of ten (G10) countries. Using a matrix-based methodology, we extracted and analyzed the average number of authors per country. Our findings reveal a consistent rise in authorship across all G10 countries over the past two decades. Italy emerged with the highest average number of authors, while the Netherlands stood out for experiencing the most significant increase, particularly in the last decade. The countries with the lowest number of authors per publication were the USA, UK, and Canada.

neuroscience↗
bioRxiv2023

A biologically plausible decision-making model based on interacting cortical columns

We present a novel decision-making model with two populations. Each population is composed of Regularly Spiking (excitatory) and Fast Spiking (inhibitory) cells in cortical layer 2/3. Each population votes for one of the two visual alternatives shown on a monitor in human and macaque experiments. The model is biophysically plausible since it is based on long-range cortico-cortical connections between the layer 2/3 populations. These connections are excitatory. They contact both Regularly Spiking and Fast Spiking cells. This long-range excitation is conflicted by an inhibition based on local connections within the populations. This configuration introduces a competition between the layer 2/3 populations, sufficient for making a decision to choose between two alternatives shown on the monitor. We integrate the model with a reward-driven learning mechanism. This allows the model to learn the optimal strategy maximizing the cumulative reward in the long term. We test the model on two decision-making tasks applied on human and macaque. This model elaborates certain biophysical details which were not considered by simpler phenomenological models proposed for similar decision-making tasks. Finally, the model can be embedded in a brain simulator such as The Virtual Brain to study decision-making in terms of large-scale brain dynamics.

neuroscience↗
bioRxiv2023

A Theoretical Formalization of Consequence-Based Decision-Making

Learning to make adaptive decisions depends on exploring options, experiencing their consequence, and reassessing ones strategy for the future. Although several studies have analyzed various aspects of value-based decision-making, most of them have focused on decisions in which gratification is cued and immediate. By contrast, how the brain gauges delayed consequence for decision-making remains poorly understood. To investigate this, we designed a decision-making task in which each decision altered future options. The task was organized in groups of consecutively dependent trials, and the participants were instructed to maximize the cumulative reward value within each group. In the absence of any explicit performance feedback, the participants had to test and internally assess specific criteria to make decisions. This task was designed to specifically study how the assessment of consequence forms and influences decisions as learning progresses. We analyzed behavior results to characterize individual differences in reaction times, decision strategies, and learning rates. We formalized this operation mathematically by means of a multi-layered decision-making model. By using a mean-field approximation, the first layer of the model described the dynamics of two populations of neurons which characterized the binary decision-making process. The other two layers modulated the decision-making policy by dynamically adapting an oversight learning mechanism. The model was validated by fitting each individual participants behavior and it faithfully predicted non-trivial patterns of decision-making, regardless of performance level. These findings provided an explanation to how delayed consequence may be computed and incorporated into the neural dynamics of decision-making, and to how learning occurs in the absence of explicit feedback.

neuroscience↗
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↗
bioRxiv2019

Neuronal activity in the premotor cortex of monkeys reflects both cue salience and motivation for action generation and inhibition

Animals adopt different strategies, promoting certain actions and withholding inconvenient ones, to achieve their goals. The motivation to obtain them is the main drive that determines the behavioural performance. While much work has focused on understanding how motor cortices control actions, their role on motivated behaviours remains unclear. We recorded from dorsal premotor cortex (PMd) of monkeys performing a modified version of the stop-signal task, in which the motivation to perform/withhold an action was manipulated by presenting cues that informed on the probability to obtain different amounts of reward in relation to the motor outcome. According to the motivational context, animals performance adapted to maximize reward. Neuronal activity displayed a cue salience related modulation at trial start and, while the behavioural response approached, reflected more the motivation to start/cancel the action. These findings reveal multiple representations of motivation-related signals in PMd, highlighting its involvement in the control of finalized actions.\n\nSIGNIFICATIVE STATEMENTThe motivation to obtain rewards drives how animals act over their environment. To explore the involvement of motor cortices in motivated behaviours, we recorded high-resolution neuronal activity in the premotor cortex of monkeys performing a task that manipulated the motivation to generate/withhold a movement through different cued reward probabilities. Our results show the presence of neuronal signals dynamically reflecting a cue related activity, in the time immediately following its presentation, and a motivation related activity in performing (or cancelling) a motor program, while the behavioural response approached. The encoding of multiple reward-related signals in motor regions, leads to consider an important role of premotor areas in the reward circuitry.

neuroscience↗
bioRxiv2019

Neuronal population dynamics during motor plan cancellation in non-human primates

To understand the cortical neuronal dynamics behind movement generation and control most studies focused on tasks where actions were planned and then executed, using different instances of visuomotor transformations. However, to fully understand the dynamics related to movement control one must also study how movements are actively inhibited. Inhibition, indeed, represents the first level of control both when different alternatives are available and only one solution could be adopted, and when it is necessary to maintain the current position. We recorded neuronal activity from a multielectrode array in the dorsal premotor (PMd) cortex of monkeys performing a countermanding reaching task that requires, in a subset of trials, to cancel a planned movement before its onset. In the analysis of the neuronal state-space of PMd we found a subspace in which activities conveying temporal information were confined during active inhibition and position holding. Movement execution required activities to escape from this subspace toward an orthogonal subspace and, furthermore, surpass a threshold associated with the maturation of the motor plan. These results revealed further details in the neuronal dynamics underlying movement control, extending the hypothesis that neuronal computation confined in an output-null subspace does not produce movements. Significance StatementA core question in neuroscience is how the brain generates arm movements. Most studies have approached this issue by investigating the neuronal dynamics that accompany movement production, leaving unanswered the question of which aspects of this dynamics are logically necessary to make the movement. Here we explored this topic by characterizing the neuronal correlates of movement decisions between active inhibition and release of movements. We found that active inhibition and stillness require neuronal signals to be confined in a functional sub-space while actions depend on the transit of activities in an orthogonal space. This dynamics is characterized by a threshold mechanism finally allowing the translation of the motor plan into overt action.

neuroscience↗
bioRxiv2018

Visual salience of the stop-signal affects neural dynamics of controlled inhibition

The countermanding or stop-signal task is broadly used to evaluate response inhibition: it sporadically requires to inhibit a movement upon an incoming salient stop-signal.\n\nTo study the neural basis of arm movements inhibition we combined the approach typically employed for the study of perceptual-decision making with the countermanding task, that is broadly used to evaluate response inhibition\n\nTo this aim we modified the salience of the stop-signal and we found that this modulation affected the ability to inhibit in macaque monkeys: coherently to what already observed in humans, we found that less salient stimuli deteriorate inhibitory performance. These behavioural results were subtended by neural modulations representing an inhibitory process that started later in time and showed a less steeper dynamic for stimuli difficult to be processed.\n\nThis study shows that the neural patterns observed when deciding to stop are broadly similar to the neural patterns observed when deciding to act in the literature; thus it is a first step in investigating the perceptual decision making process involved in movement inhibition.

neuroscience↗