Biology subjects

Serrano-Fernandez, L.

Publications and source records attributed to Serrano-Fernandez, L..

5 recordsLinked to original sources

bioRxiv2026

A Variational Principle for the Emergence of Neural Geometry Underlying Bayesian-Like Behavior

Neural population geometry constrains how task variables are represented and read out into behavior. We propose a variational principle in which learning balances task accuracy and representational smoothness under stimulus-dependent weighting. This balance links stimulus statistics to the shape of a latent neural coordinate. For magnitude estimation, the principle predicts distortions near the edges of bounded ensembles, global affine contraction for Gaussian ensembles, and warping toward high-probability regions for bimodal ensembles. We test the bounded-uniform case in recurrent neural networks trained to reproduce time intervals. Their learned geometries occupy three regimes as the boundary distortions change from too narrow to resolve, to measurable, to extending across the stimulus range. A shape parameter selected from an empirical neural coordinate predicts the nonlinear component of mean behavior through an affine readout, with only behavioral gain and offset fitted afterward. In this bounded-uniform setting, the variational coordinate and Bayesian least-squares mean response have closely matching shapes after affine normalization. These results suggest a variational account of how stimulus statistics shape neural geometry and yield Bayesian-like behavior.

neuroscience↗

bioRxiv2025

Emergence of Value and Action Codes in Bimodular Spiking Actor-Critic Networks

Decision-making depends on coordinated computations distributed across dorsal and ventral circuits, often described as actor-critic systems. We examined how this division of labor gives rise to value- and decision-related representations by training a bimodular recurrent network on an economic choice task and transferring it to an expanded gated spiking model that preserved its latent dynamics while stabilizing the underlying neural representations. The two modules assumed complementary roles: the actor encoded action-value differences and spatial contingencies required for selecting between actions, whereas the critic represented object values predictive of reward. Neurons across modules were selective for object, action, total, and difference values, supporting decision- and confidence-related activity. Low-dimensional analyses revealed structured trajectories reflecting temporal evolution, spatial configuration, and value-dependent divergence. These results clarify how distributed circuits can jointly implement valuation and action selection, providing a foundation for linking reinforcement-trained recurrent models with anatomically inspired dorsal-ventral frameworks.

neuroscience↗

bioRxiv2025

Early Emergence of Perceptual Biases in Working Memory

Working memory (WM) is distributed across multiple cortical areas, suggesting that behaviors relying on WM arise from interactions between these regions. In a recent study, we found that during delayed comparison tasks, the first stimulus is not represented veridically in the prefrontal cortex (PFC), but instead is encoded in a systematically warped manner--biased toward the mean of the stimulus distribution. This neural distortion, which emerges already during the stimulus presentation and persists throughout the delay period, closely mirrors a contraction bias observed in behavior. Furthermore, the behavioral responses could be explained by a Bayesian observer model, in which the brain integrates prior expectations with noisy sensory inputs. These results suggest that the geometry of PFC neural trajectories embodies Bayesian estimates that underlie biased decisions. Here, we investigate whether the secondary somatosensory cortex (S2)--a lower-level sensory area also implicated in tactile WM--exhibits a similar encoding structure. Our analyses reveal that although WM-related signals in S2 are less robust than in PFC, the neural state space in S2 shares key geometric features with that of PFC, including a similarly warped representation of stimulus values. These findings suggest that perceptual biases may originate early in the cortical processing stream and are not exclusively shaped by higher-order associative areas. More broadly, our results support a distributed organization of WM, in which even sensory areas contribute to the formation of bias-prone representations that guide behavior.

neuroscience↗

bioRxiv2023

Prefrontal Cortex Neural Correlates of the Contraction Bias in Frequency Discrimination

Perception is influenced by sensory stimulation, prior knowledge, and contextual cues, which collectively contribute to the emergence of perceptual biases. However, the precise neural mechanisms underlying these biases remain poorly understood. This study aims to address this gap by analyzing neural recordings from the prefrontal cortex (PFC) of monkeys performing a vibrotactile frequency discrimination task. Our findings provide empirical evidence supporting the hypothesis that perceptual biases can be reflected in the neural activity of the PFC. We found that the state-space trajectories of PFC neuronal activity encoded a warped representation of the first frequency presented during the task. Remarkably, this distorted representation of the frequency aligned with the predictions of its Bayesian estimator. The identification of these neural correlates expands our understanding of the neural basis of perceptual biases and highlights the involvement of the PFC in shaping perceptual experiences. Similar analyses could be employed in other delayed comparison tasks and in various brain regions to explore where and how neural activity reflects perceptual biases during different stages of the trial.

neuroscience↗

bioRxiv2022

Emergent perceptual biases from state-space geometry in spiking recurrent neural networks trained to discriminate time intervals

A stimulus held in working memory is perceived as contracted towards the average stimulus. This contraction bias has been extensively studied in psychophysics, but little is known about its origin from neural activity. By training recurrent networks of spiking neurons to discriminate temporal intervals, we explored the causes of this bias and how behavior relates to population firing activity. We found that the trained networks exhibited animal-like behavior. Various geometric features of neural trajectories in state space encoded warped representations of the durations of the first interval modulated by sensory history. Formulating a novel normative model, we showed that these representations conveyed a Bayesian estimate of the interval durations, thus relating activity and behavior. Importantly, our findings demonstrate that Bayesian computations already occur during the sensory phase of the first stimulus and persist throughout its maintenance in working memory, until the time of stimulus comparison.

neuroscience↗