Biology subjects

Mazzucato, L.

Publications and source records attributed to Mazzucato, L..

17 recordsLinked to original sources

bioRxiv2026

Control of cortical population activity with patterned microstimulation

Closed-loop control of cortical activity is a central goal in systems neuroscience and clinical neuromodulation, but most approaches either rely on detailed circuit models that are unattainable in vivo or on open-loop stimulation tuned by trial and error. Here we introduce REACHable manifold Control (REACH-Ctrl), a data-driven brain-computer interface that achieves real-time control of population spiking activity using patterned microstimulation and multi-electrode recordings. REACH-Ctrl learns a finite-horizon controllability map directly from short training epochs in which random multi-electrode pulse sequences are delivered through a subset of electrodes while recording evoked responses. From these input-output data, it identifies the "reachable manifold" of population states and computes low-current microstimulation sequences that steer activity toward designated targets, without explicit knowledge of the underlying connectivity or dynamics. We test REACH-Ctrl in macaque prefrontal cortex, demonstrating high control accuracy, robust across sessions and stimulation parameters. Geometric analyses showed that multi-pulse sequences traverse a well-defined reachable manifold with substantial, but incomplete, overlap with the intrinsic neural activity manifold, revealing both on- and off-manifold components of control. Encoding models further revealed that, in our weak-stimulation regime, population responses to multi-electrode sequences are well approximated by the linear sum of localized "stimulation fields" with modest history dependence, explaining the success of our linear control approach. These results demonstrate precise, sample-efficient control of cortical population activity with clinically relevant microstimulation hardware, and provide a general blueprint for designing perturbations for sparsely observed neural circuits.

neuroscience↗

bioRxiv2025

"Awe-scillations": EEG spectral and complexity representations of awe

Awe is a positive emotion often accompanied by sensations of vastness and unity, with known benefits for well-being and social behavior. However, its neural underpinnings remain poorly understood. We recorded electroencephalography (EEG) and autonomic physiology in 23 healthy older participants while they watched a nature-based audiovisual film and subjectively rated awe events. Awe was the predominant emotion reported - though other positive emotions (e.g., joy) were also highly rated. Awe events were associated with decreased skin conductance level (SCL), and decreased EEG alpha and theta spectral power - physiological changes associated with low arousal and positively valenced emotional states. Interestingly, awe events exhibited increased Lempel Ziv Complexity (LZC) - indicating heightened neural signal entropy and increased richness of the conscious experience. LZC was also positively associated with the intensity of the awe ratings and negatively associated with SCL. Three additional datasets with separate independent induction methods (video clips and pharmacological induction via N,N-dimethyltryptamine) also showed positive occipital LZC associations with awe - suggesting some generalizability of LZC as a neurophysiological marker of awe. These results suggest that awe evokes neurophysiological states linked to the subjective affective experience. Impact StatementAwe, an emotion of increasing interest, has been studied with fMRI and peripheral physiology - but few studies have used electroencephalography (EEG). In this EEG study, we utilize a movie-watching paradigm to explore potential physiological correlates of awe. The results present EEG-based complexity increases as a potential correlate of awe, though showing limited generalization to independent datasets and limited uniqueness compared to joy, another positive emotion.

neuroscience↗

bioRxiv2025

Towards model-based design of causal manipulations of brain circuits with high spatiotemporal precision

Recent advancements in neurotechnology enable precise spatiotemporal patterns of micros- timulations with single-cell resolution. The choice of perturbation sites must satisfy two key criteria: efficacy in evoking significant responses and selectivity for the desired target effects. This choice is currently based on laborious trial-and-error procedures, unfeasible for sequences of multi-site stimulations. Efficient methods to design complex perturbation patterns are ur- gently needed. Can we design a spatiotemporal pattern of stimulation to steer neural activity and behavior towards a desired target? We outline a method for achieving this goal in two steps. First, we identify the most effective perturbation sites, or hubs, only based on short observations of spontaneous neural activity. Second, we provide an efficient method to design multi-site stimulation patterns by combining approaches from nonlinear dynamical systems, control theory and data-driven methods. We demonstrate the feasibility of our approach using multi-site stimulation patterns in recurrent network models.

neuroscience↗

bioRxiv2025

Spontaneous Dynamics Predict the Effects of Targeted Intervention in Hippocampal Neuronal Cultures

Microstimulation is a powerful tool for causal interrogation of neural circuits and for therapeutic neuromodulation. However, predicting network-level responses to focal perturbations remains a major challenge. To address this problem in a tractable manner, we combine experiments on networks of hippocampal neurons cultured on high-density multielectrode arrays with spiking network model simulations. To characterize spontaneous and stimulation-evoked network dynamics, we employ a combination of direct electrophysiological readouts and information-theoretic measures. We find that single-channel stimulation reliably evokes a small set of site-specific, stereotyped network activity patterns. Remarkably, effective connectivity inferred from spontaneous activity captures the spatial organization of perturbation responses, enabling reliable ranking of stimulation-evoked effects across the network. Our spiking network model reproduces these observations and reveals the interplay between short-term synaptic depression and distance-dependent excitatory and inhibitory connectivity in shaping both spontaneous and evoked interactions at different effective scales. Spontaneous activity involves local structural routes, while perturbation-evoked responses engage comparatively longer, polysynaptic pathways. By unifying in silico modeling with experimental measurements, this work links spontaneous network structure to stimulation-evoked dynamics, suggesting that spontaneous effective connectivity may serve as a tractable proxy for stimulation targeting in recurrent circuits, with potential implications for the rational design of neuromodulatory interventions.

neuroscience↗

bioRxiv2024

Probabilistic modeling reveals coordinated social interaction states and their multisensory bases

Social behavior across animal species ranges from simple pairwise interactions to thousands of individuals coordinating goal-directed movements. Regardless of the scale, these interactions are governed by the interplay between multimodal sensory information and the internal state of each animal. Here, we investigate how animals use multiple sensory modalities to guide social behavior in the highly social zebrafish (Danio rerio) and uncover the complex features of pairwise interactions early in development. To identify distinct behaviors and understand how they vary over time, we developed a new hidden Markov model with constrained linear-model emissions to automatically classify states of coordinated interaction, using the movements of one animal to predict those of another. We discovered that social behaviors alternate between two interaction states within a single experimental session, distinguished by unique movements and timescales. Long-range interactions, akin to shoaling, rely on vision, while mechanosensation underlies rapid synchronized movements and parallel swimming, precursors of schooling. Altogether, we observe spontaneous interactions in pairs of fish, develop novel hidden Markov modeling to reveal two fundamental interaction modes, and identify the sensory systems involved in each. Our modeling approach to pairwise social interactions has broad applicability to a wide variety of naturalistic behaviors and species and solves the challenge of detecting transient couplings between quasi-periodic time series. HIGHLIGHTSO_LIZebrafish exhibit distinct correlated interaction states with unique timescales. C_LIO_LIDelayed interactions are visual while synchronization requires mechanosensation. C_LIO_LIA new class of hidden Markov model segments social interactions into discrete states. C_LIO_LIStates alternate within a session, revealing real-time dynamics of social behavior. C_LI

animal behavior and cognition↗

bioRxiv2024

Modulation of metastable ensemble dynamics explains optimal coding at moderate arousal in auditory cortex

Past work has reported inverted-U relationships between arousal and auditory task performance, but the underlying neural network mechanisms remain unclear. To make progress, we recorded auditory cortex activity from behaving mice during passive tone presentation and simultaneously monitored pupil-indexed arousal. In these experiments, neural discriminability of tones was maximized at intermediate arousal, revealing a neural correlate of the inverted-U. We explained this arousal-dependent sound processing using a spiking model with clusters. In the model, stimulus discriminability peaked as the network transitioned from a multi-attractor phase exhibiting slow switching between metastable cluster activations (low arousal) to a single-attractor phase with uniform activity (high arousal). This transition also qualitatively captured arousal-induced reductions of neural variability observed in the data. Altogether, this study elucidates computational principles to explain interactions between arousal, neural discriminability, and variability, and suggests that transitions in the dynamical regime of cortical networks could underlie nonlinear modulations of sensory processing.

neuroscience↗

bioRxiv2023

Differential encoding of temporal context and expectation under representational drift across hierarchically connected areas

The classic view that neural populations in sensory cortices preferentially encode responses to incoming stimuli has been strongly challenged by recent experimental studies. Despite the fact that a large fraction of variance of visual responses in rodents can be attributed to behavioral state and movements, trial-history, and salience, the effects of contextual modulations and expectations on sensory-evoked responses in visual and association areas remain elusive. Here, we present a comprehensive experimental and theoretical study showing that hierarchically connected visual and association areas differentially encode the temporal context and expectation of naturalistic visual stimuli, consistent with the theory of hierarchical predictive coding. We measured neural responses to expected and unexpected sequences of natural scenes in the primary visual cortex (V1), the posterior medial higher order visual area (PM), and retrosplenial cortex (RSP) using 2-photon imaging in behaving mice collected through the Allen Institute Mindscopes OpenScope program. We found that information about image identity in neural population activity depended on the temporal context of transitions preceding each scene, and decreased along the hierarchy. Furthermore, our analyses revealed that the conjunctive encoding of temporal context and image identity was modulated by expectations of sequential events. In V1 and PM, we found enhanced and specific responses to unexpected oddball images, signaling stimulus-specific expectation violation. In contrast, in RSP the population response to oddball presentation recapitulated the missing expected image rather than the oddball image. These differential responses along the hierarchy are consistent with classic theories of hierarchical predictive coding whereby higher areas encode predictions and lower areas encode deviations from expectation. We further found evidence for drift in visual responses on the timescale of minutes. Although activity drift was present in all areas, population responses in V1 and PM, but not in RSP, maintained stable encoding of visual information and representational geometry. Instead we found that RSP drift was independent of stimulus information, suggesting a role in generating an internal model of the environment in the temporal domain. Overall, our results establish temporal context and expectation as substantial encoding dimensions in the visual cortex subject to fast representational drift and suggest that hierarchically connected areas instantiate a predictive coding mechanism.

neuroscience↗

bioRxiv2023

Transitions between discrete performance states in auditory and visual tasks are predicted by arousal and uninstructed movements

During sensory-guided behavior, an animals decision-making dynamics unfold through sequences of distinct performance states, even while stimulus-reward contingencies remain static. Little is known about the factors that underlie these changes in task performance. We hypothesize that these decision-making dynamics can be predicted by externally observable measures, such as uninstructed movements and changes in arousal. Here, combining behavioral experiments in mice with computational modeling, we uncovered lawful relationships between transitions in strategic task performance states and an animals arousal and uninstructed movements. Using hidden Markov models applied to behavioral choices during sensory discrimination tasks, we found that animals fluctuate between minutes-long optimal, sub-optimal and disengaged performance states. Optimal state epochs were predicted by intermediate levels, and reduced variability, of pupil diameter, along with reduced variability in face movements and locomotion. Our results demonstrate that externally observable uninstructed behaviors can predict optimal performance states, and suggest mice regulate their arousal during optimal performance.

neuroscience↗

bioRxiv2022

Coexisting persistent and dynamic representations of stimulus, time and trial outcome emerge in the hippocampus-prefrontal circuitry during learning of a temporal association

Temporal associations between sensory stimuli separated in time rely on the interaction between the hippocampus and medial prefrontal cortex (mPFC). However, it is not known how changes in their neural activity support the emergence of temporal association learning. Here, we use simultaneous electrophysiological recordings in the hippocampal CA1 region and mPFC of mice to elucidate the neural dynamics underlying memory formation in an auditory trace conditioning task. We found that in both areas conditioned (CS+/CS-) and unconditioned stimuli (US) evoked similar temporal sequences of neural responses that progressively diverged during learning. Additionally, persistent CS representations emerged in mPFC after learning, supported by CS+ coding states whose transient reactivation reliably predicted lick onset and behavioral performance on single trials. These results show that coordination of temporal sequences in CA1 and persistent activity in mPFC may underlie temporal association learning, and that transient reactivations of engrams in mPFC predict the animal behavior. HighlightsO_LITemporal representation of CS+, but not CS- and US, strengthens in CA1 after learning. C_LIO_LISimilarity between stimulus and reward temporal representations decrease with learning, but is recovered in error trials. C_LIO_LIRepresentation of stimulus identity is strong and stable in PFC since stimulus onset, while it only emerges in CA1 during trace period. C_LIO_LINeural states defined on faster time-scales reveal the emergence of CS coding states in PFC whose onset predicts lick times and task performance. C_LIO_LIPFC-CA1 states do not increase coordination during late CS+ stimulus and trace. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/497926v2_fig0.gif" ALT="Figure 0"> View larger version (32K): org.highwire.dtl.DTLVardef@93f7a7org.highwire.dtl.DTLVardef@6804a0org.highwire.dtl.DTLVardef@107a6a2org.highwire.dtl.DTLVardef@14cfb23_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 0:C_FLOATNO Graphical Abstract C_FIG

neuroscience↗

bioRxiv2022

Baseline control of optimal performance in recurrent neural networks

Changes in behavioral state, such as arousal and movements, strongly affect neural activity in sensory areas. Recent evidence suggests that they may be mediated by top-down projections regulating the statistics of baseline input currents to sensory areas, inducing qualitatively different effects across sensory modalities. What are the computational benefits of these baseline modulations? We investigate this question within a brain-inspired framework for reservoir computing, where we vary the quenched baseline inputs to a random neural network. We found that baseline modulations control the dynamical phase of the reservoir network, unlocking a vast repertoire of network phases. We uncover a new zoo of bistable phases exhibiting the simultaneous coexistence of fixed points and chaos, of two fixed points, and of weak and strong chaos. Crucially, we discovered a host of novel phenomena, including noise-driven enhancement of chaos and ergodicity breaking; neural hysteresis, whereby transitions across phase boundary retain the memory of the initial phase. Strikingly, we found that baseline control can achieve optimal performance without any fine tuning of recurrent couplings. In summary, baseline control of network dynamics opens new directions for brain-inspired artificial intelligence and provides a new interpretation for the ubiquitously observed behavioral modulations of cortical activity.

neuroscience↗

bioRxiv2021

A reservoir of timescales in random neural networks

The temporal activity of many biological systems, including neural circuits, exhibits fluctuations simultaneously varying over a large range of timescales. The mechanisms leading to this temporal heterogeneity are yet unknown. Here we show that random neural networks endowed with a distribution of self-couplings, representing functional neural clusters of different sizes, generate multiple timescales of activity spanning several orders of magnitude. When driven by a time-dependent broadband input, slow and fast neural clusters preferentially entrain slow and fast spectral components of the input, respectively, suggesting a potential mechanism for spectral demixing in cortical circuits.

neuroscience↗

bioRxiv2021

Distance estimation from monocular cues in an ethological visuomotor task

In natural contexts, sensory processing and motor output are closely coupled, which is reflected in the fact that many brain areas contain both sensory and movement signals. However, standard reductionist paradigms decouple sensory decisions from their natural motor consequences, and head-fixation prevents the natural sensory consequences of self-motion. In particular, movement through the environment provides a number of depth cues beyond stereo vision that are poorly understood. To study the integration of visual processing and motor output in a naturalistic task, we investigated distance estimation in freely moving mice. We found that mice use vision to accurately jump across a variable gap, thus directly coupling a visual computation to its corresponding ethological motor output. Monocular eyelid suture did not affect gap jumping success, thus mice can use cues that do not depend on binocular disparity and stereo vision. Under monocular conditions, mice altered their head positioning and performed more vertical head movements, consistent with a shift from using stereopsis to other monocular cues, such as motion or position parallax. Finally, optogenetic suppression of primary visual cortex impaired task performance under both binocular and monocular conditions when optical fiber placement was localized to binocular or monocular zone V1, respectively. Together, these results show that mice can use monocular cues, relying on visual cortex, to accurately judge distance. Furthermore, this behavioral paradigm provides a foundation for studying how neural circuits convert sensory information into ethological motor output.

neuroscience↗

bioRxiv2020

Predicting perturbation effects from resting state activity using functional causal flow

A crucial challenge in targeted manipulation of neural activity is to identify perturbation sites whose stimulation exerts significant effects downstream (high efficacy), a procedure currently achieved by labor-intensive trial-and-error. Targeted perturbations will be greatly facilitated by understanding causal interactions within neural ensembles and predicting the efficacy of perturbation sites before intervention. Here, we address this issue by developing a computational framework to predict how single-site micorstimulation alters the ensemble spiking activity in an alert monkeys prefrontal cortex. Our framework uses delay embedding techniques to infer the ensembles functional causal flow (FCF) based on the functional interactions inferred at rest. We validate FCF using ground truth data from models of cortical circuits, showing that FCF is robust to noise and can be inferred from brief recordings of even a small fraction of neurons in the circuit. A detailed comparison of FCF with several alternative methods, including Granger causality and transfer entropy, highlighted the advantages of FCF in predicting perturbation effects on empirical data. Our results provide the foundation for using targeted circuit manipulations to develop targeted interventions suitable for brain-machine interfaces and ameliorating cognitive dysfunctions in the human brain.

neuroscience↗

bioRxiv2020

State-dependent control of cortical processing speed via gain modulation

AO_SCPLOWBSTRACTC_SCPLOWTo thrive in dynamic environments, animals must be capable of rapidly and flexibly adapting behavioral responses to a changing context and internal state. Examples of behavioral flexibility include faster stimulus responses when attentive and slower responses when distracted. Contextual or state-dependent modulations may occur early in the cortical hierarchy and may be implemented via top-down projections from cortico-cortical or neuromodulatory pathways. However, the computational mechanisms mediating the effects of such projections are not known. Here, we introduce a theoretical framework to classify the effects of cell-type specific top-down perturbations on the information processing speed of cortical circuits. Our theory demonstrates that perturbation effects on stimulus processing can be predicted by intrinsic gain modulation, which controls the timescale of the circuit dynamics. Our theory leads to counter-intuitive effects such as improved performance with increased input variance. We tested the model predictions using large-scale electrophysiological recordings from the visual hierarchy in freely running mice, where we found that a decrease in single-cell intrinsic gain during locomotion led to an acceleration of visual processing. Our results establish a novel theory of cell-type specific perturbations, applicable to top-down modulation as well as optogenetic and pharmacological manipulations. Our theory links connectivity, dynamics, and information processing via gain modulation.

neuroscience↗

bioRxiv2020

Metastable attractors explain the variable timing of stable behavioral action sequences

Natural animal behavior displays rich lexical and temporal dynamics, even in a stable environment. The timing of self-initiated actions shows large variability even when they are executed in reliable, well-learned sequences. To elucidate the neural mechanism underlying this mix of reliability and stochasticity, we trained rats to perform a stereotyped sequence of self-initiated actions and recorded neural ensemble activity in secondary motor cortex (M2), known to reflect trial-by-trial action timing fluctuations. Using hidden Markov models, we established a dictionary between ensemble activity patterns and actions. We then showed that metastable attractors, with a reliable sequential structure yet high transition timing variability, could be produced by coupling a high-dimensional recurrent network and a low-dimensional feedforward one. Transitions between attractors in our model were generated by correlated variability arising from the feedback loop between the two networks. This mechanism predicted aligned, low-dimensional noise correlations that were empirically verified in M2 ensembles. Our work establishes a novel framework for investigating the circuit origins of self-initiated behavior based on correlated variability.

neuroscience↗

bioRxiv2019

Hippocampal network reorganization underlies the formation of a temporal association memory

Episodic memory requires linking events in time, a function dependent on the hippocampus. In \"trace\" fear conditioning, animals learn to associate a neutral cue with an aversive stimulus despite their separation in time by a delay period on the order of tens of seconds. But how this temporal association forms remains unclear. Here we use 2-photon calcium imaging to track neural population dynamics over the complete time-course of learning and show that, in contrast to previous theories, the hippocampus does not generate persistent activity to bridge the time delay. Instead, learning is concomitant with broad changes in the active neural population in CA1. While neural responses were highly stochastic in time, cue identity could be reliably read out from population activity rates over longer timescales after learning. These results question the ubiquity of neural sequences during temporal association learning, and suggest that trace fear conditioning relies on mechanisms that differ from persistent activity accounts of working memory.

neuroscience↗

bioRxiv2017

Expectation-induced modulation of metastable activity underlies faster coding of sensory stimuli

Sensory stimuli can be recognized more rapidly when they are expected. This phenomenon depends on expectation affecting the cortical processing of sensory information. However, virtually nothing is known on the mechanisms responsible for the effects of expectation on sensory networks. Here, we report a novel computational mechanism underlying the expectation-dependent acceleration of coding observed in the gustatory cortex (GC) of alert rats. We use a recurrent spiking network model with a clustered architecture capturing essential features of cortical activity, including the metastable activity observed in GC before and after gustatory stimulation. Relying both on network theory and computer simulations, we propose that expectation exerts its function by modulating the intrinsically generated dynamics preceding taste delivery. Our model, whose predictions are confirmed in the experimental data, demonstrates how the modulation of intrinsic metastable activity can shape sensory coding and mediate cognitive processes such as the expectation of relevant events. Altogether, these results provide a biologically plausible theory of expectation and ascribe a new functional role to intrinsically generated, metastable activity.

neuroscience↗