Biology subjects

Rinaldi, F. G.

Publications and source records attributed to Rinaldi, F. G..

3 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

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

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↗