Biology subjects

La Camera, G.

Publications and source records attributed to La Camera, G..

10 recordsLinked to original sources

bioRxiv2025

Linear and categorical coding units in the mouse gustatory cortex drive population dynamics and behavior in taste decision-making

Cortical circuits produce time-varying patterns of population and single neuron activity that play a fundamental role in perceptual and behavioral processes. However, the functional contributions of individual neuron activity to population dynamics and behavior remain unclear. Here we addressed this issue focusing on the mouse gustatory cortex (GC) and using a taste mixture-based decision-making task, high-density electrophysiology, and computational modeling. GC population dynamics represented stimuli linearly during taste sampling and choices categorically before decisions. Single neurons were classified by their linear and categorical activity patterns, revealing subpopulations encoding sensory, perceptual, and decisional variables. To test their functional role, we built a recurrent neural network model of GC. Model perturbations showed linear and categorical neurons were essential for driving normal population dynamics and behavioral performance, whereas many units with other activity patterns could be silenced without consequence. These results have implications that extend beyond GC, and demonstrate the role of linear and categorical coding neurons in cortical dynamics and behavior during perceptual decision-making.

neuroscience↗

bioRxiv2024

A sticky Poisson Hidden Markov Model for spike data

The application of hidden Markov models (HMMs) to neural data has uncovered hidden states and signatures of neural dynamics that are relevant for sensory and cognitive processes. However, training an HMM on cortical data requires a careful handling of model selection, since models with more numerous hidden states generally have a higher likelihood on new (unseen) data. A potentially related problem is the occurrence of very rapid state switching after decoding the data with an HMM. The first problem can lead to overfitting and over-segmentation of the data. The second problem is due to intermediate- to-low self-transition probabilities and is at odds with many reports that hidden states in cortex tend to last from hundred of milliseconds to seconds. Here, we show that we can alleviate both problems by regularizing a Poisson-HMM during training so as to enforce large self-transition probabilities. We call this algorithm the sticky Poisson-HMM (sPHMM). The sPHMM successfully eliminates rapid state switching, outperforming an alternative strategy based on an HMM with a large prior on the self-transition probabilities. When used together with the Bayesian Information Criterion for model selection, the sPHMM also captures the ground truth in surrogate datasets built to resemble the statistical properties of the experimental data.

neuroscience↗

bioRxiv2024

The sea urchin Paracentrotus lividus orients to visual stimuli

Though lacking eyes, some sea urchins can see: Several species exhibit resolving vision, as distinct from mere light detection. How and where light is captured in the eyeless sea urchins, and how this information is integrated to elicit visual behaviour, remains a fascinating enigma. We assessed the spatial resolution of the sea urchin Paracentrotus lividus in laboratory experiments using fifty adults from the Bay of Naples. This keystone species is an important grazer of the NE Atlantic and Mediterranean and a model system to study development. We carried out behavioural trials in which individuals were placed in a submerged cylindrical arena to determine if they orient towards a visual stimulus on the arena wall, under diffuse, downwelling light. We adopted a novel isoluminant stimulus, necessitating vision of a given resolving power around the horizon to be detected. We tested individuals at five stimulus widths, including a uniform control. Animals oriented (upon clearing an obstacle) only to the widest stimuli (45 deg and above). This acuity may suffice for tasks such as finding nearby shelters or distant patches of habitat. We modelled the visual and neuronal processes to recapitulate these responses in P. lividus, by fine-tuning the model of Li et al. (2022), as applied to the sea urchin Diadema africanum. While these species differ morphologically, the model robustly predicts angular sensitivity in keeping with the behavioural experiments. We find that P. lividus (and likely many Echinacea) possesses coarse spatial vision and that the neurosensory model applies broadly to sea urchins.

zoology↗

bioRxiv2023

Co-existence of synaptic plasticity and metastable dynamics in a spiking model of cortical circuits

Evidence for metastable dynamics and its role in brain function is emerging at a fast pace and is changing our understanding of neural coding by putting an emphasis on hidden states of transient activity. Clustered networks of spiking neurons have enhanced synaptic connections among groups of neurons forming structures called cell assemblies; such networks are capable of producing metastable dynamics that is in agreement with many experimental results. However, it is unclear how a clustered network structure producing metastable dynamics may emerge from a fully local plasticity rule, i.e., a plasticity rule where each synapse has only access to the activity of the neurons it connects (as opposed to the activity of other neurons or other synapses). Here, we propose a local plasticity rule producing ongoing metastable dynamics in a deterministic, recurrent network of spiking neurons. The metastable dynamics co-exists with ongoing plasticity and is the consequence of a self-tuning mechanism that keeps the synaptic weights close to the instability line where memories are spontaneously reactivated. In turn, the synaptic structure is stable to ongoing dynamics and random perturbations, yet it remains sufficiently plastic to remap sensory representations to encode new sets of stimuli. Both the plasticity rule and the metastable dynamics scale well with network size, with synaptic stability increasing with the number of neurons. Overall, our results show that it is possible to generate metastable dynamics over meaningful hidden states using a simple but biologically plausible plasticity rule which co-exists with ongoing neural dynamics.

neuroscience↗

bioRxiv2022

Temporal progression along discrete coding states during decision-making in the mouse gustatory cortex

The mouse gustatory cortex (GC) is involved in taste-guided decision-making in addition to sensory processing. Rodent GC exhibits metastable neural dynamics during ongoing and stimulus-evoked activity, but how these dynamics evolve in the context of a taste-based decision-making task remains unclear. Here we employ analytical and modeling approaches to i) extract metastable dynamics in ensemble spiking activity recorded from the GC of mice performing a perceptual decision-making task; ii) investigate the computational mechanisms underlying GC metastability in this task; and iii) establish a relationship between GC dynamics and behavioral performance. Our results show that activity in GC during perceptual decision-making is metastable and that this metastability may serve as a substrate for sequentially encoding sensory, abstract cue, and decision information over time. Perturbations of the models metastable dynamics indicate that boosting inhibition in different coding epochs differentially impacts network performance, explaining a counterintuitive effect of GC optogenetic silencing on mouse behavior.

neuroscience↗

bioRxiv2022

A model of decentralized vision in the sea urchin Diadema africanum

AO_SCPLOWBSTRACTC_SCPLOWSea urchins can detect light and move in relation to luminous stimuli despite lacking eyes. They presumably detect light through photoreceptor cells distributed on their body surface. However, there is currently no mechanistic explanation of how these animals can process light to detect visual stimuli and produce oriented movement. Here, we present a model of decentralized vision in echinoderms that includes all known processing stages, from photoreceptor cells to radial nerve neurons to neurons contained in the oral nerve ring encircling the mouth of the animals. In the model, light stimuli captured by photoreceptor cells produce neural activity in the radial nerve neurons. In turn, neural activity in the radial nerves is integrated in the oral nerve ring to produce a profile of neural activity reaching spatially across several ambulacra. This neural activity is read out to produce a model of movement. The model captures previously published data on the behavior of sea urchin Diadema africanum probed with a variety of physical stimuli. The specific pattern of neural connections used in the model makes testable predictions on the properties of single neurons and aggregate neural behavior in Diadema africanum and other echinoderms, offering a potential understanding of the mechanism of visual orientation in these animals.

systems biology↗

bioRxiv2021

Slower prefrontal metastable dynamics during deliberation predicts error trials in a distance discrimination task

AO_SCPLOWBSTRACTC_SCPLOWPrevious studies have established the involvement of prefrontal cortex (PFC) neurons in decision processes in many task contexts. Single neurons and populations of neurons have been found to represent stimuli, actions, and internal deliberations. However, it is much less clear which underlying computations are affected during errors. Neural activity during errors can help to disambiguate confounds and clarify which computations are essential during a specific task. Here, we used a hidden Markov model (HMM) to perform a trial-by-trial analysis of ensembles of simultaneously recorded neurons from the dorsolateral prefrontal (PFdl) cortex of two rhesus monkeys performing a distance discrimination task. The HMM segments the neural activity into sequences of metastable states, allowing to link neural ensemble dynamics with task and behavioral features in the absence of external triggers. We report a precise relationship between the modulation of the metastable dynamics and task features. Specifically, we found that errors were made more often when the metastable dynamics slowed down, while trial difficulty influenced the latency of state transitions at a pivotal point during the trial. Both these phenomena occurred during the decision interval and not following the action, with errors occurring in both easy and difficult trials. Thus, modulations of metastable dynamics reflected a state of internal deliberation rather than actions taken or, in the case of error trials, objective trial difficulty. Our results show that temporal modulations of PFdl activity are key determinants of internal deliberations, providing further support for the emerging role of metastable cortical dynamics in mediating complex cognitive functions and behavior.

neuroscience↗

bioRxiv2020

Temporal stimulus segmentation by reinforcement learning in populations of spiking neurons

Learning to detect, identify or select stimuli is an essential requirement of many behavioral tasks. In real life situations, relevant and non-relevant stimuli are often embedded in a continuous sensory stream, presumably represented by different segments of neural activity. Here, we introduce a neural circuit model that can learn to identify action-relevant stimuli embedded in a spatio-temporal stream of spike trains, while learning to ignore stimuli that are not behaviorally relevant. The model uses a biologically plausible plasticity rule and learns from the reinforcement of correct decisions taken at the right time. Learning is fully online; it is successful for a wide spectrum of stimulus-encoding strategies; it scales well with population size; and can segment cortical spike patterns recorded from behaving animals. Altogether, these results provide a biologically plausible framework of reinforcement learning in the absence of prior information on the identity, relevance and timing of input stimuli.

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↗

bioRxiv2017

Contributions of different prefrontal cortical regions to abstract rule acquisition and reversal in monkeys

The ability to learn and follow abstract rules relies on intact prefrontal regions including the lateral prefrontal cortex (LPFC) and the orbitofrontal cortex (OFC). Here, we investigate the specific roles of these brain regions in learning rules that depend critically on the formation of abstract concepts as opposed to simpler input-output associations. To this aim, we tested monkeys with bilateral removals of either LPFC or OFC on a rapidly learned task requiring the formation of the abstract concept of same vs. different. While monkeys with OFC removals were significantly slower than controls at both acquiring and reversing the concept-based rule, monkeys with LPFC removals were not impaired in acquiring the task, but were significantly slower at rule reversal. Neither group was impaired in the acquisition or reversal of a delayed visual cue-outcome association task without a concept-based rule. These results suggest that OFC is essential for the implementation of a concept-based rule, whereas LPFC seems essential for its modification once established.

neuroscience↗