Biology subjects

Piasini, E.

Publications and source records attributed to Piasini, E..

16 recordsLinked to original sources

bioRxiv2026

Striatal interneuron microcircuits gate reinforcement to stabilize adaptive choice

The dorsomedial striatum guides learning and adaptive decision-making through excitatory synaptic control of its spiny projection neuron outputs. However, the contributions of local inhibitory microcircuitry remain poorly understood. Here, we identify an interneuron circuit in the dorsomedial striatum that links outcome processing to adaptive action selection. During probabilistic push-pull reversal learning, interneurons represented immediate outcomes: somatostatin interneurons were recruited on unrewarded trials and unexpected rewarded trials, whereas tyrosine hydroxylase interneurons were suppressed on unrewarded trials and recruited on rewarded trials. In vivo recruitment of tyrosine hydroxylase interneurons suppressed somatostatin interneuron activity and increased activity in both direct- and indirect-pathway striatal projection neurons, revealing a polysynaptic disinhibitory microcircuit. Transient inhibition of somatostatin interneurons in this pull-tuned region produced a sustained increase in aberrant pull choices and occupancy of a suboptimal pull-preferring behavioral state, whereas inhibition of tyrosine hydroxylase interneurons produced a sustained impairment of pull reinforcement. Longer-term policy changes following somatostatin interneuron inhibition coincided with postsynaptic potentiation of excitatory synapses onto striatal projection neurons, suggesting a potential substrate for the persistence of altered behavioral policies. Together, these findings identify a disinhibitory striatal circuit motif gating reinforcement which transforms individual trial outcomes into temporally broader policy.

neuroscience↗

bioRxiv2026

Prion Protein Deficiency Results in Synaptic, Neural Network and Behavioral Alterations

The cellular form of the prion protein (PrPC) is known for its involvement in the pathogenesis of prion diseases. Recent research implicates the physiological isoform of PrP in neuronal development, excitability, and synaptic plasticity, as well as in other biological processes. However, its precise function in the development and function of neurons remains poorly understood. Here, we investigated its role during different developmental stages, both in vitro and in vivo, using different PrP knock-out (KO) mouse lines (Prnp-/-). Prion protein KO neurons cultured on microelectrode arrays (MEAs) displayed altered network dynamics compared to wild type cultures, comprising reduced burst frequency, and abnormal spike patterns, indicative of impaired maturation of the synaptic circuitry. These functional alterations were associated with a reduced expression of key presynaptic and postsynaptic proteins, including elements of the SNARE complex and regulators of excitation-inhibition balance. Similar molecular changes were also confirmed in a second Prnp-/- model, suggesting that PrPC is directly involved in these mechanisms regardless of genetic backgrounds. Alterations in neuronal networks were traceable into adulthood: in vivo recordings in adult Prnp-/- mice revealed increased neuronal responses to visual danger stimuli, which correlated with behaviorally increased fear responses to those stimuli. Together, our findings support a critical role for PrPC in the establishment and maintenance of functional neuronal networks, from early developmental stages in vitro to behaviorally mature relevant circuits in vivo, beyond genomic background. These results indicate that PrPC acts as a key regulator of synaptic development and function both in physiological and pathological conditions.

molecular biology↗

bioRxiv2026

Noisy models of the ventral stream reveal the impact of recurrence and learned representations on information processing timescales

AO_SCPLOWBSTRACTC_SCPLOWIn vision neuroscience, the temporal dynamics of the sensory stream and of its neural representations are thought to be deeply linked to the function of the hierarchy of cortical areas that deal with object recognition, known as the visual ventral stream. Neural representations that are invariant under identity-preserving object transformations, and therefore allow for efficient learning of object identity, are theorized to emerge from a self-supervised learning process that attempts to extract "temporally stable" features from the sensory input. Conversely, invariance increases along the hierarchy, putatively implying progressively slower codes in higher-level areas. Recent neurophysiological evidence shows that indeed, as one moves along this cortical hierarchy, neural representations of dynamic stimuli become slower, and additionally the temporal scales of the within-trial fluctuations of these representations (called "intrinsic timescales") increase starkly. However, the network determinants of these timescale hierarchies are not understood in realistic systems, as the classical theory is based on models without noise, recurrence, or adaptive mechanisms. Here we investigate the temporal structure of the neural code in a noisy, recurrent and adaptive model of the ventral visual stream. We show that, surprisingly, the organization of the representation timescales is set by the broad architectural features of the network, regardless of training, while the intrinsic timescales depend on the details of the functions implemented on each layer. Our work underscores the importance of the temporal structure of the neural code as a probe for the link between structure and function in models of the vertebrate visual system.

neuroscience↗

bioRxiv2026

Sample size dependence of Occam's razor in human decision-making

To make sense of a noisy world, living beings constantly face decisions between competing interpretations for ambiguous sensory data. This process parallels statistical model selection, where most frameworks, like the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), are based on a trade-off between a models goodness-of-fit and its complexity and prescribe a bias towards simpler models (explanations). This same bias towards simplicity is generally reflected in human behavior. However, a core tenet of normative frameworks is that the trade-off should depend on the sample size (N): as more data becomes available, the goodness-of-fit grows faster than the "cost" of complex models, weakening the overall bias towards simplicity. It is unknown whether humans also conform to an analogous scaling principle, and if so, whether this behavior arises from an internal computation similar to that leading to the normative solution or from a simpler heuristic. Here, we investigate these questions using a preregistered visual task where participants inferred the number of latent Gaussian sources generating clusters of data-points, and where the number of points (N) presented on each trial is varied systematically. We consider three kinds of descriptions for participant behavior: one arising from a linear scaling of the weight of sensory evidence in N (as in BIC and AIC), one with no scaling, and one with sublinear scaling inspired by known biases in numerosity perception. Our results show that the normative, linear scaling description provides the worst account of human behavior. Instead, we find strong evidence for a sublinear scaling of effective sample size. By inferring the shape of this scaling with Gaussian Processes, we reveal two distinct scaling regimes for different ranges of N, consistent with numerosity perception biases. Our findings suggest that, when selecting between competing explanations for sensory data, humans employ an efficient heuristic that repurposes lower-level perceptual mechanisms to dynamically weight evidence against model complexity.

neuroscience↗

bioRxiv2025

Predisposed and learned preferences for multipoint visual statistics in visually naïve newly hatched chicks

Significance statementWe show that visually naive chicks spontaneously prefer specific multipoint correlation patterns, mirroring preferences seen in humans and rats, and reflecting the most informative structures in natural scenes. This provides evidence that efficient coding mechanisms may be innately driven by evolutionary predispositions. Notably, early visual experience through imprinting can alter these preferences, highlighting a role for learning in shaping visual processing. Recent studies have revealed that human and non-human animals (rats) can detect luminance distribution and correlations between pixels in an image (ranging from 2-point to 4-point). This sensitivity is believed to stem from optimization processes in the visual system that operate through efficient coding mechanisms to extract the most informative image statistics from the environment. However, it is yet to be determined whether this optimization is evolutionarily given by inborn mechanisms or shaped by visual experience. Here we report that newly-hatched visually naive domestic chicks spontaneously prefer to approach luminance, 2-point and 4-point correlation patterns (respectively, horizontal lines and rectangular patterns), while showing no preference for 3-point correlation over white noise controls. This parallels the ranking observed in adult humans and rats, thus suggesting that evolutionarily given biological predispositions largely drive efficient coding of natural images. We also found that learning by exposure to visual stimuli, as occurs naturally during visual imprinting, induced a preference for white noise over point correlation patterns in chicks exposed to 3- and 4-point patterns. We hypothesize that this behavior could reflect chicks preference for stimuli of lower statistical complexity.

animal behavior and cognition↗

bioRxiv2025

Seeing what you hear: sound intensity alters rat visual perception in a temporal frequency classification task

The brain combines information from multiple sensory modalities to build a consistent view of the world. The principles by which multimodal stimuli are integrated in cortical hierarchies are well studied, but it is less clear whether and how unimodal inputs systematically shape the processing of signals carried by a different modality. Here we use a visual classification task in rats to investigate how task-irrelevant sounds modify the processing of visual stimuli. Our data shows that the intensity of a sound, but not its temporal modulation frequency, enacts a powerful, effective compression of the visual perceptual space. This result underscores the importance of cross-modal influences in perceptual pathways and suggests an important role for inhibition as the mediator of auditory-visual interactions at the neural representation level.

animal behavior and cognition↗

bioRxiv2023

A mixed mechano-olfactory code for sniff-invariant odor representations

Sampling behaviors have sensory consequences that can hinder perceptual stability. In olfaction, sniffing affects early odor encoding, mimicking a sudden change in odor concentration. Here, we examined how the inhalation speed impacts the representation of odor concentration in the main olfactory cortex. Neurons combine the odor input with a global top-down signal preceding the sniff and a mechanosensory feedback generated by the air passage through the nose during an inhalation. Still, the population representation of concentration is remarkably sniff-invariant. This is because the mechanosensory and olfactory responses are uncorrelated within and across neurons. Thus, a faster odor inhalation and a concentration increase change the cortical activity pattern in distinct ways. This encoding strategy affords tolerance to potential concentration fluctuations caused by varying inhalation speeds. Since mechanosensory reafferences are widespread across sensory systems, the coding scheme described here may be a canonical strategy to mitigate the sensory ambiguities caused by movements.

neuroscience↗

bioRxiv2023

How Occam's razor guides human decision-making

Occam's razor is the principle that, all else being equal, simpler explanations should be preferred over more complex ones. This principle is thought to guide human decision-making, but the nature of this guidance is not known. Here we used preregistered behavioral experiments to show that people tend to prefer the simpler of two alternative explanations for uncertain data. These preferences align quantitatively with predictions of formal theories of model selection that penalize excessive flexibility. These penalties emerge when considering not just the best explanation but the integral over all possible, relevant explanations. We further show that these simplicity preferences persist in humans, but not in certain artificial neural networks, even when they are maladaptive. Our results imply that principled notions of statistical model selection, including integrating over possible, latent causes to avoid overfitting to noisy data, may play a central role in human decision-making.

neuroscience↗

bioRxiv2022

The interplay of uncertainty, relevance and learning influences auditory categorization

Auditory perception requires categorizing sound sequences, such as speech or music, into classes, such as syllables or notes. Auditory categorization depends not only on the acoustic waveform, but also on variability and uncertainty in how the listener perceives the sound - including sensory and stimulus uncertainty, the listeners estimated relevance of the particular sound to the task, and their ability to learn the past statistics of the acoustic environment. Whereas these factors have been studied in isolation, whether and how these factors interact to shape categorization remains unknown. Here, we measured human participants performance on a multi-tone categorization task and modeled each participants behavior using a Bayesian framework. Task-relevant tones contributed more to category choice than task-irrelevant tones, confirming that participants combined information about sensory features with task relevance. Conversely, participants poor estimates of task-relevant tones or high-sensory uncertainty adversely impacted category choice. Learning the statistics of sound category over both short and long timescales also affected decisions, biasing the decisions toward the overrepresented category. The magnitude of this effect correlated inversely with participants relevance estimates. Our results demonstrate that individual participants idiosyncratically weigh sensory uncertainty, task relevance, and statistics over both short and long timescales, providing a novel understanding of and a computational framework for how sensory decisions are made under several simultaneous behavioral demands.

neuroscience↗

bioRxiv2022

Time as a supervisor: temporal regularity and auditory object learning

Sensory systems appear to learn to transform incoming sensory information into perceptual representations, or "objects", that can inform and guide behavior with minimal explicit supervision. Here, we propose that the auditory system can achieve this goal by using time as a supervisor, i.e., by learning features of a stimulus that are temporally regular. We will show that this procedure generates a feature space sufficient to support fundamental computations of auditory perception. In detail, we consider the problem of discriminating between instances of a prototypical class of natural auditory objects, i.e., rhesus macaque vocalizations. We test discrimination in two ethologically relevant tasks: discrimination in a cluttered acoustic background and generalization to discriminate between novel exemplars. We show that an algorithm that learns these temporally regular features affords better or equivalent discrimination and generalization than conventional feature-selection algorithms, i.e., principal component analysis and independent component analysis. Our findings suggest that the slow temporal features of auditory stimuli may be sufficient for parsing auditory scenes and that the auditory brain could utilize these slowly changing temporal features.

neuroscience↗

bioRxiv2021

Distributed processing for action control by prelimbic circuits targeting anterior-posterior dorsal striatal subregions.

Fronto-striatal circuits have been extensively implicated in the cognitive control of behavioral output for both social and appetitive rewards. The functional diversity of prefrontal cortical populations is strongly dependent on their synaptic targets, with control of motor output strongly mediated by connectivity to the dorsal striatum. Despite evidence for functional diversity along the anterior-posterior axis of the dorsomedial striatum (DMS), it is unclear how distinct fronto- striatal sub-circuits support neural computations essential for action selection. Here we identify prefrontal populations targeting distinct DMS subregions and characterize their functional roles. We first performed neural circuit tracing to reveal segregated prefrontal populations defined by anterior/posterior dorsomedial striatal target. We then probed the functional relevance of these parallel circuits via in vivo calcium imaging and temporally precise causal manipulations during a feedback-based 2-alternative choice task. Single-photon imaging revealed circuit-specific representations of task-relevant information with prelimbic neurons targeting anterior DMS (PL::A- DMS) uniquely encoded choices and responses to negative outcomes, while prelimbic neurons targeting posterior DMS (PL::P-DMS) encoded internal representations of value and positive outcomes contingent on prior choice. Consistent with this distributed coding, optogenetic inhibition of PL::A-DMS circuits strongly impacted choice monitoring and behavioral control in response to negative outcomes while perturbation of PL::P-DMS signals impaired task engagement and strategies following positive outcomes. Di-synaptic retrograde tracing uncovered differences in afferent connectivity that may underlie these pathways functional divergence. Together our data uncover novel PL populations engaged in distributed processing for action control. SUMMARYO_LIPrelimbic cortex engages A- and P-DMS via distinct circuits C_LIO_LIPL::A-DMS and PL::P-DMS pathways encode divergent aspects of a simple goal-directed task C_LIO_LIPL::A-DMS pathways shape responding to negative outcomes via multiple mechanisms C_LIO_LIPL::P-DMS pathways guide engagement and choices in response to positive outcomes C_LIO_LIAfferent connectomes of PL neurons defined by A-P DMS target are distinct C_LI

neuroscience↗

bioRxiv2021

Cortical efficient coding dynamics shape behavioral performance.

Neurons throughout the sensory pathway adapt their responses depending on the statistical structure of the sensory environment. Contrast gain control is a form of adaptation in the auditory cortex, but it is unclear whether the dynamics of gain control reflect efficient adaptation, and whether they shape behavioral perception. Here, we trained mice to detect a target presented in background noise shortly after a change in the contrast of the background. The observed changes in cortical gain and behavioral detection followed the dynamics of a normative model of efficient contrast gain control; specifically, target detection and sensitivity improved slowly in low contrast, but degraded rapidly in high contrast. Auditory cortex was required for this task, and cortical responses were not only similarly affected by contrast but predicted variability in behavioral performance. Combined, our results demonstrate that dynamic gain adaptation supports efficient coding in auditory cortex and predicts the perception of sounds in noise.

neuroscience↗

bioRxiv2021

Rat sensitivity to multipoint statistics is predicted by efficient coding of natural scenes

Efficient processing of sensory data requires adapting the neuronal encoding strategy to the statistics of natural stimuli. Previously, in Hermundstad et al. 2014, we showed that local multipoint correlation patterns that are most variable in natural images are also the most perceptually salient for human observers, in a way that is compatible with the efficient coding principle. Understanding the neuronal mechanisms underlying such adaptation to image statistics will require performing invasive experiments that are impossible in humans. Therefore, it is important to understand whether a similar phenomenon can be detected in animal species that allow for powerful experimental manipulations, such as rodents. Here we selected four image statistics (from single- to four-point correlations) and trained four groups of rats to discriminate between white noise patterns and binary textures containing variable intensity levels of one of such statistics. We interpreted the resulting psychometric data with an ideal observer model, finding a sharp decrease in sensitivity from 2- to 4-point correlations and a further decrease from 4- to 3-point. This ranking fully reproduces the trend we previously observed in humans, thus extending a direct demonstration of efficient coding to a species where neuronal and developmental processes can be interrogated and causally manipulated.

neuroscience↗

bioRxiv2020

The complexity of model-free and model-based learning strategies

Different learning strategies are thought to fall along a continuum that ranges from simple, inflexible, and fast "model-free" strategies, to more complex, flexible, and deliberative "model-based strategies". Here we show that, contrary to this proposal, strategies at both ends of this continuum can be equally flexible, effective, and time-intensive. We analyzed behavior of adult human subjects performing a canonical learning task used to distinguish between model-free and model-based strategies. Subjects using either strategy showed similarly high information complexity, a measure of strategic flexibility, and comparable accuracy and response times. This similarity was apparent despite the generally higher computational complexity of model-based algorithms and fundamental differences in how each strategy learned: model-free learning was driven primarily by observed past responses, whereas model-based learning was driven primarily by inferences about latent task features. Thus, model-free and model-based learning differ in the information they use to learn but can support comparably flexible behavior. Statement of RelevanceThe distinction between model-free and model-based learning is an influential framework that has been used extensively to understand individual- and task-dependent differences in learning by both healthy and clinical populations. A common interpretation of this distinction that model-based strategies are more complex and therefore more flexible than model-free strategies. However, this interpretation conflates computational complexity, which relates to processing resources and generally higher for model-based algorithms, with information complexity, which reflects flexibility but has rarely been measured. Here we use a metric of information complexity to demonstrate that, contrary to this interpretation, model-free and model-based strategies can be equally flexible, effective, and time-intensive and are better distinguished by the nature of the information from which they learn. Our results counter common interpretations of model-free versus model-based learning and demonstrate the general usefulness of information complexity for assessing different forms of strategic flexibility.

neuroscience↗

bioRxiv2019

Intrinsic dynamics enhance temporal stability of stimulus representation along a visual cortical hierarchy

Along the ventral stream, cortical representations of brief, static stimuli become gradually more invariant to identity-preserving transformations. In the presence of long, temporally structured dynamic stimuli, higher invariance should imply temporally persistent representations at the top of this functional hierarchy. However, such stimuli could engage adaptive and predictive processes, whose impact on neural coding dynamics is unknown. By probing the rat analogue of the ventral stream with movies, we uncovered a hierarchy of temporal scales, with deeper areas encoding visual information more persistently. Furthermore, the impact of intrinsic dynamics on the stability of stimulus representations gradually grew along the hierarchy. Analysis of a large dataset of recordings from the mouse visual hierarchy yielded similar trends, revealing also their dependence on the behavioral state of the animal. Overall, these findings show that visual representations become progressively more stable along rodent visual processing hierarchies, with an important contribution provided by intrinsic processing.

neuroscience↗

bioRxiv2018

Open Source Brain: a collaborative resource for visualizing, analyzing, simulating and developing standardized models of neurons and circuits

Computational models are powerful tools for investigating brain function in health and disease. However, biologically detailed neuronal and circuit models are complex and implemented in a range of specialized languages, making them inaccessible and opaque to many neuroscientists. This has limited critical evaluation of models by the scientific community and impeded their refinement and widespread adoption. To address this, we have combined advances in standardizing models, open source software development and web technologies to develop Open Source Brain, a platform for visualizing, simulating, disseminating and collaboratively developing standardized models of neurons and circuits from a range of brain regions. Model structure and parameters can be visualized and their dynamical properties explored through browser-controlled simulations, without writing code. Open Source Brain makes neural models transparent and accessible and facilitates testing, critical evaluation and refinement, thereby helping to improve the accuracy and reproducibility of models, and their dissemination to the wider community.

neuroscience↗