Biology subjects

Mattia, M.

Publications and source records attributed to Mattia, M..

17 recordsLinked to original sources

bioRxiv2026

Heterogeneous self-couplings enable scale-invariant memory and cognitive flexibility without criticality

The hierarchical organization of the brain underlies its ability to process information across multiple time scales and support flexible behavior through context-dependent computation. Here, we study a recurrent neural network model with heterogeneous self-interactions across nodes, capturing key features of neuronal circuits, including intrinsic diversity of dynamical time scales and multistable low-dimensional manifolds. We investigate the emergence of scale-invariant dynamics without assuming criticality, challenging the view that power-law statistics necessarily arise from self-organized critical states. We show that heterogeneous self-interactions alone are sufficient to generate scale-invariant memory in subcritical regimes, while enhancing the network's dynamical richness and computational capacity. Notably, this mechanism promotes the spontaneous emergence of hierarchical organization. We assess the functional implications of this framework using short-term memory tasks and the Wisconsin Card Sorting Test, which probes cognitive flexibility and adaptive rule switching. Compared to models with heterogeneous time constants, our approach achieves comparable performance with more parsimonious synaptic modifications, while preserving scale-invariant dynamics.

neuroscience↗

bioRxiv2026

Generating whole-brain neural activity and behavior through unified latent dynamics

Understanding how high-dimensional neural activity and behavior emerge from shared underlying dynamics remains a fundamental challenge in neuroscience. Addressing this problem is key to enabling digital twins that can faithfully reproduce and predict the multiscale brain-behavior dynamics of living systems. Here we present NEBULA (NEural and Behavioral modeling through Unified LAtent dynamics), a generative framework that jointly models whole-brain neural activity and behavior. By applying NEBULA to brain-wide recordings from C. elegans, we identify a shared low-dimensional dynamical manifold that underlies both neural activity and behavior, enabling long-horizon generation and targeted in silico interventions. Perturbations of the learned dynamics reveal behaviorally relevant transition points, whereas steering interventions enable controlled manipulation of neural and behavioral states without retraining. These results establish a framework for linking brain dynamics to behavior in a living organism and provide a foundation for scalable virtual experimentation in neuroscience.

animal behavior and cognition↗

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↗

bioRxiv2024

Circuit-Level Dynamics of Slow Wave Activity and Propagation During the Awakening Process

Slow-wave activity (SWA) is a hallmark of the loss of consciousness in non-REM sleep and anesthesia. The mechanistic underpinnings of SWA, and its evolution when transitioning towards the conscious brain state is poorly understood. We address this topic by recording multi-area and laminar activity in posterior parietal (PPC) and primary visual (V1) cortices of mice spontaneously awakening from isoflurane anesthesia. Spectral power is stronger in PPC (especially in superficial layers) during deep unconsciousness, but stronger in V1 when awakening. Rostro-caudal (feedback-like) propagation of SWA also shows state-dependent modulation, particularly in layer 5. The excitability of layer 2/3 neurons, hindered at high isoflurane, recovers during awakening, when V1 and the feedforward pathway reacquire a strong role. Detailing the hierarchical and laminar properties of spontaneous traveling oscillations, we provide evidence that SWA is a multiscale phenomenon. Explicating the functional role of these processes is critical to understand the neuronal mechanisms of consciousness.

neuroscience↗

bioRxiv2024

Global and local nature of cortical slow waves

Explaining the macroscopic activity of a recorded neuronal population from its known microscopic properties still poses a great challenge, not just because of the many local agents that shape the output of a circuit, but due to the impact of long-range connections from other brain regions. Here we use a computational model to explore how local and global components of a network shape the Slow Wave Activity (SWA). We performed a sensitivity analysis of multiple cellular and synaptic features in models of isolated and connected networks. This allowed us to explore how the interaction of local properties and long-range connections shape the SWA of a population and its neighbors, as well as how the sequential propagation of active "Up" states lead to the emergence of preferred modes of propagation. We described relevant features of cortical Up states that are modulated by "stiff" combinations of parameters of the local circuit as opposed to other that are sensitive to the level of excitability of the whole network and the input coming from neighbor populations. We found that while manipulations in the synaptic excitatory/inhibitory balance can create local changes, cellular components that modulate the excitability or adaptation of a population have a long-range effect that leads to changes in neighbor populations too. Additionally, our simulations guided in vivo experiments that showed how heterogeneities in excitability between cortical areas can determine the directionality of travelling waves during SWA. We expect these results to motivate future research exploring and comparing cortical circuits through the analysis of their Up states.

neuroscience↗

bioRxiv2024

Learning to infer transitively: serial ordering on a mental line in premotor cortex

Transitive inference (TI) is a form of deductive reasoning that allows to infer unknown relationships among premises. It is hypothesized that this cognitive task is accomplished by mapping stimuli onto a linear workspace, referred to as the mental line, based on their arbitrarily assigned ranks. However, open questions remain: does this mental line have a neural correlate, and if so, where and how is it represented and learned in the brain? In this study, we investigate the role of monkeys dorsal premotor cortex (PMd) in encoding the hypothesized mental line during the acquisition of item relationships. Our findings provide evidence that the TI task can be solved through a linear transformation of the neural representations of arbitrarily ranked items. We show that PMd multi-unit activity organizes along a theoretically informed direction, implementing a geometrical solution that effectively explains animal behavior. Our results suggest that the premotor cortex plays a crucial role in integrating item representations into a geometric mental line, where the symbolic distance (i.e., rank difference) between items influences the related motor decisions. Furthermore, we observe an ongoing learning process characterized by a rotation of this mental line, which aligns to the linear manifold where motor plan unfolds. This elucidates a cortical optimization strategy based on the statistical structure of the task.

animal behavior and cognition↗

bioRxiv2024

Linearizing and forecasting: a reservoir computing route to digital twins of the brain

Exploring the dynamics of a complex system, such as the human brain, poses significant challenges due to inherent uncertainties and limited data. In this study, we enhance the capabilities of noisy linear recurrent neural networks (lRNNs) within the reservoir computing framework, demonstrating their effectiveness in creating autonomous in silico replicas - digital-twins - of brain activity. Our findings reveal that the poles of the Laplace transform of high-dimensional inferred lRNNs are directly linked to the spectral properties of observed systems and to the kernels of auto-regressive models. Applying this theoretical framework to resting-state fMRI, we successfully predict and decompose BOLD signals into spatiotemporal modes of a low-dimensional latent state space confined around a single equilibrium point. lRNNs provide an interpretable proxy for clustering among subjects and different brain areas. This adaptable digital-twin framework not only enables virtual experiments but also offers computational efficiency for real-time learning, highlighting its potential for personalized medicine and intervention strategies.

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↗

bioRxiv2023

Ranking and serial thinking: A geometric solution

A general mathematical description of the way the brain encodes ordinal knowledge of sequences is still lacking. Coherently with the well-established idea of mixed selectivity in high-dimensional state spaces, we conjectured the existence of a linear solution for serial learning tasks. In this theoretical framework, the neural representation of the items in a sequence are read out as ordered projections along a suited "geometric" mental line learned via classical conditioning (delta rule learning). We show that the derived model explains all the behavioral effects observed in humans and other animal species performing the transitive inference task in presence of noisy sensory information and stochastic neural activity. This result is generalized to the case of recurrent neural networks performing motor decision, where the same geometric mental line is learned showing a tight correlation with the motor plan of the responses. Network activity is then eventually modulated according to the symbolic distance of presented item pairs, as observed in associative cortices of nonhuman primates. Serial ordering is thus predicted to emerge as a linear mapping between sensory input and behavioral output, highlighting a possible pivotal role of motor-related associative cortices in the transitive inference task.

neuroscience↗

bioRxiv2022

Adaptation shapes local cortical reactivity: from bifurcation diagram and simulations to human physiological and pathological responses

Human studies employing intracerebral and transcranial perturbations suggest that the input-output properties of cortical circuits are dramatically affected during sleep in healthy subjects as well as in awake patients with multifocal and focal brain injury. In all these conditions, cortical circuits react to direct stimulation with an initial activation followed by suppression of activity (Off-period) that disrupts the build-up of sustained causal interactions typically observed in healthy wakefulness. The transition to this stereotypical response is of clinical relevance, being associated with loss of consciousness or loss of function. Here, we provide a mechanistic explanation of these findings by means of mean-field theory and simulations of a cortical-like module endowed with activity-dependent adaptation. First, we show that fundamental aspects of the local responses elicited in humans by direct cortical stimulation can be replicated by systematically varying the relationships between adaptation strength and excitation level in the network. Then, we reveal a region in the adaptation-excitation parameter space of key relevance for both physiological and pathological conditions, where spontaneous activity and responses to perturbation diverge in their ability to reveal Off-periods. Finally, we substantiate through simulations of connected cortical-like modules the role of adaptation mechanisms in preventing cortical neurons from engaging in reciprocal causal interactions, as suggested by empirical studies. These modeling results provide a general theoretical framework and a mechanistic interpretation for a body of neurophysiological measurements that bears key relevance for physiological states as well as for the assessment and rehabilitation of brain-injured patients. Significance StatementSuppression of cortical activity following an initial activation is a defining feature of deep sleep in healthy subjects and wakefulness in patients affected by focal and multifocal brain injuries. Experimental findings suggest that these bimodal responses disrupt the emergence of complex interactions among cortical regions, leading to loss of consciousness or functional impairments. Given their practical implications, it is important to study the mechanisms involved within a general theoretical framework. Using a neuronal network model, we provide evidence for a key role of activity-dependent adaptation mechanisms in shaping the responses to perturbation and in affecting the build-up of complex cortical interactions. Overall, this work provides a mechanistic interpretation relevant for the stratification, follow-up, and rehabilitation of brain-injured patients.

neuroscience↗

bioRxiv2021

Multiscale dynamics underlying neocortical slow oscillations

Slow oscillations in the sleeping and anesthetized brain invariantly emerge as an alternation between Up (high firing) and Down (almost quiescent) states. In cortex, they occur simultaneously in cell assemblies in different layers and propagate as traveling waves, a concerted activity at multiple scales whose interplay and role is still under debate. Slow oscillations have been reported to start in deep layers, more specifically in layer 5. Here, we studied the laminar organization of slow oscillations in the anesthetized rat cortex and we found that the activity leading to Up states actually initiates in layer 6, then spreads towards upper layers. Layer 5 cell assemblies have a threshold-like activation that can persist after layer 6 inactivation, giving rise to hysteresis loops like in "flip-flop" computational units. We found that such hysteresis is finely tuned by the columnar circuitry depending on the recent history of the local ongoing activity. Furthermore, thalamic inactivation reduced infragranular excitability without affecting the columnar activation pattern. We propose a role for layer 6 acting as a hub unraveling a hierarchy of cortical loops.

neuroscience↗

bioRxiv2021

Slow waves form expanding, memory-rich mesostates steered by local excitability in fading anesthesia

During sleep and anesthesia, large groups of neurons throughout the entire cortex activate rhythmically producing wavefronts of activity referred to as slow-wave activity (SWA). In the arousal process, the brain restores its integrative and complex activity. The network mechanisms underlying this global state transition remain however to be elucidated. Here we investigated the network features shaping the SWA under fading anesthesia. Using electrocorticographical recordings of wide cortical areas of the mouse brain, we developed a quantitative measure of the anesthesia level based on slow-wave frequency and complexity. At deep anesthesia, we document a stringent alternation of posterior-anterior-posterior modes of propagation. With fading anesthesia, SWA evolves to produce a plethora of metastable spatiotemporal patterns. A network model of spiking neurons reproduced the data using short-range connectivity, subcortical input and a local activity-dependent adaptation. The emergence from deep anesthesia does not require modifying the connectivity, but small changes in the local excitability of cortical cell assemblies, further supporting the hypothesis of a tight bound between scales in the brain.

neuroscience↗

bioRxiv2020

Instability with a purpose: how the visual brain makes decisions in a volatile world

In ambiguous or conflicting sensory situations, perception is often multistable in that it changes abruptly at irregular intervals, shifting perpetually between distinct alternatives. Intriguingly, the interval statistics of these alternations exhibits quasi-universal characteristics, suggesting a general mechanism. Here we show that the stereotypical features of multistable perception, exemplified by binocular rivalry, are reproduced in detail by a hierarchical dynamics operating out of equilibrium. Its constitutive elements are discretely stochastic and idealize the metastability of cortical networks. Independent elements accumulate visual evidence at one level, while groups of coupled elements compete for dominance at another level. As soon as one group dominates perception, feedback inhibition suppresses supporting evidence. This mechanism is corroborated compellingly by unexpected serial dependencies of perceptual alternations. Moreover, it satisfies normative constraints of continuous decision-making. We conclude that multistable perception reflects decision-making in a volatile world: integrating evidence over space and time, choosing categorically between hypotheses, while concurrently evaluating alternatives.

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↗

bioRxiv2019

A state-dependent mean-field formalism to model different activity states in conductance based networks of spiking neurons

Higher and higher interest has been shown in the recent years to large scale spiking simulations of cerebral neuronal networks, coming both from the presence of high performance computers and increasing details in the experimental observations. In this context it is important to understand how population dynamics are generated by the designed parameters of the networks, that is the question addressed by mean field theories. Despite analytic solutions for the mean field dynamics has already been proposed generally for current based neurons (CUBA), the same for more realistic neural properties, such as conductance based (COBA) network of adaptive exponential neurons (AdEx), a complete analytic model has not been achieved yet. Here, we propose a novel principled approach to map a COBA on a CUBA. Such approach provides a state-dependent approximation capable to reliably predict the firing rate properties of an AdEx neuron with non-instantaneous COBA integration. We also applied our theory to population dynamics, predicting the dynamical properties of the network in very different regimes, such as asynchronous irregular (AI) and synchronous irregular (SI) (slow oscillations, SO). This results show that a state-dependent approximation can be successfully introduced in order to take into account the subtle effects of COBA integration and to deal with a theory capable to correctly predicts the activity in regimes of alternating states like slow oscillations.

neuroscience↗

bioRxiv2019

Attractor competition enriches cortical dynamics during awakening from anesthesia

Cortical slow oscillations ([lsim] 1 Hz) are a hallmark of slow-wave sleep and deep anesthesia across animal species. They arise from spatiotemporal patterns of activity with low degree of complexity, eventually increasing as wakefulness is approached and cognitive functions emerge. The arousal process is then an open window on the widely unknown mechanisms underlying the emergence of the dynamical richness of awake cortical networks. Here, we investigated the changes in the network dynamics as anesthesia fades out and wakefulness is approached in layer 5 neuronal assemblies of the rat visual cortex. Far from being a continuum, this transition displays both gradual and abrupt activity changes. Starting from deep anesthesia, slow oscillations increase their frequency eventually expressing maximum regularity. This stage is followed by the abrupt onset of an infra-slow (~ 0.2 Hz) alternation between sleep-like oscillations and activated states. A population rate model reproduces this transition driven by an increased excitability that brings it to periodically cross a critical point. We conclude that dynamical richness emerges as a competition between two metastable attractor states whose existence is here experimentally confirmed.

neuroscience↗

bioRxiv2018

Modulation of slow and fast oscillations by direct current stimulation in the cerebral cortex in vitro

Non-invasive brain stimulation techniques, such as transcranial direct current stimulation (tDCS), play a growing role in the treatment of neurological disorders. However, the mechanisms by which electric fields modulate cortical network activity are only partially understood. To explore the spatiotemporal modulation of cortical activity by electric fields (DC fields), we exposed neocortical slices to constant fields of varying intensity and direction and we measured their effect on the low (<1 Hz) and high frequencies (beta 15-30 Hz and gamma 30-90 Hz) of spontaneously generated cortical oscillations. Slow oscillations consist of Up (active) and Down (silent) states. We found that DC fields ranging from -6 to +6 V/m induced an exponential increase in the frequency of slow oscillations through the regulation of the excitability and duration of Down states, while hardly affecting Up states duration. A computational model based on the mean-field theory of attractor dynamics provided a mechanistic and quantitative description of the network dynamics underlying such precise modulation of slow oscillatory frequency. The modulation of high frequencies by DC fields was less consistent, the high frequency power varying with the intensity of the fields only in a fraction of slices. Interestingly, negative DC fields of increasing intensities progressively and effectively reversed the increase in high frequency power induced by kainate application. Our findings have implications for the understanding of cortical oscillations and the mechanisms by which they are modulated by DC fields and may contribute to the future development of tools with an accurate spatiotemporal control of cortical activity.\n\nSignificance statementActing on the brain through electrical stimulation in order to correct dysfunctions or to induce functional recovery is a relatively common technique nowadays used in the clinical realm. In spite of the existence of previous studies on the effect of electric fields on neuronal and network physiology, questions regarding the mechanisms underlying exogenous electrical modulation of cortical dynamics still remain open. We demonstrate that continuous electric fields between -6 and +6 V/m induce a precise modulation of slow and fast cortical rhythms. Based on both experimental evidence and theoretical analysis, we describe some of the mechanistic underpinnings at play and provide useful information for the development of tools with better spatiotemporal control of cortical activity.

neuroscience↗