About

Giancarlo La Camera studied Theoretical Physics at the University of Rome "La Sapienza" and received a Laurea (M. Sci.) in 1999. He obtained a PhD in Neurobiology from the University of Bern in 2003. Between 2004 and 2008, he was a visiting fellow at the National Institute of Mental Health, where he performed research on the neural basis of cognitive functions. He then returned to the University of Bern to focus on reinforcement learning in populations of spiking neurons. In early 2011, he joined the faculty of Stony Brook University as an Assistant Professor of Neurobiology & Behavior and was promoted to Associate Professor with tenure in 2017. His research interests include the neural basis of sensory and cognitive processes such as memory, decision making, and chemosensory processes involving taste perception. His laboratory performs theoretical research using mathematical and computational approaches, emphasizing biologically plausible models often involving populations of spiking neurons. Recent work has characterized the metastable nature of ongoing and evoked activity in cortical areas, notably the primary gustatory cortex, and explored its implications for neural coding. The lab investigates how metastable neural activity supports sensory and cognitive functions and models synaptic plasticity and learning rules for neural circuits. They have achieved results on sensory stream segmentation and decision-making through reinforcement learning in spiking neuron networks, aiming to understand how agents identify relevant environmental features for decision-making. The research team collaborates with other groups to test models against empirical data.

Research topics

• Neuroscience
• Computer science
• Psychology
• Physics
• Cognitive psychology

Selected publications

• On the relationship between equilibria and dynamics in large, random neuronal networks

  ArXiv.org · 2025-10-21

  preprintOpen access

  We investigate the equilibria of a random model network exhibiting extensive chaos. In this regime, a large number of equilibria is present. They are all saddles with low-dimensional unstable manifolds. Surprisingly, despite network's connectivity being completely random, the equilibria are strongly correlated and, as a result, they occupy a very small region in the phase space. The attractor is inside this region. This geometry explains why the collective states sampled by the dynamics are dominated by correlation effects and, hence, why the chaotic dynamics in these models can be described by a fractionally-small number of collective modes.

  PublisherOA PDFDOI

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

  2025-12-12

  peer-reviewOpen access

  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.

  PublisherDOI

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

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-10-07

  preprintOpen access

  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.

  PublisherOA PDFDOI

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

  eLife · 2025-12-12

  articleOpen access

  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.

  PublisherDOI

• A sticky Poisson Hidden Markov Model for solving the problem of over-segmentation and rapid state switching in cortical datasets

  PLoS ONE · 2025-07-01 · 2 citations

  articleOpen accessSenior author

  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.

  PublisherDOI

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

  PLoS Computational Biology · 2024-07-01 · 5 citations

  articleOpen accessSenior authorCorresponding

  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.

  PublisherOA PDFDOI

• The sea urchin <i>Paracentrotus lividus</i> orients to visual stimuli

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-01-07 · 1 citations

  preprintOpen accessCorresponding

  Abstract 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.

  PublisherOA PDFDOI

• A sticky Poisson Hidden Markov Model for solving the problem of over-segmentation and rapid state switching in cortical datasets

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-08-08

  preprintOpen accessSenior authorCorresponding

  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.

  PublisherOA PDFDOI

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

  bioRxiv (Cold Spring Harbor Laboratory) · 2023-12-08

  preprintOpen accessSenior authorCorresponding

  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.

  PublisherOA PDFDOI

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

  PLoS Computational Biology · 2023-02-07 · 17 citations

  articleOpen accessCorresponding

  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 model's 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.

  PublisherOA PDFDOI

Recent grants

• Temporal Stimulus Segmentation with Spiking Neurons

  NSF · $316k · 2012–2016

Frequent coauthors

• Alfredo Fontanini

  50 shared

• Luca Mazzucato

  34 shared

• Sébastien Bouret

  Inserm

  28 shared

• Barry J. Richmond

  National Institute of Mental Health

  21 shared

• Walter Senn

  University of Bern

  13 shared

• Stefano Fusi

  Columbia University

  12 shared

• Alexander Rauch

  8 shared

• John D. Kirwan

  Stazione Zoologica Anton Dohrn

  6 shared