Biology subjects

Muratore, P.

Publications and source records attributed to Muratore, P..

3 recordsLinked to original sources

bioRxiv2026

An explainable AI latent space of brain dynamics reveals a cerebello-prefrontal signature of schizophrenia symptoms

Schizophrenia presents with several partially independent symptom dimensions, including positive symptoms, negative symptoms, and cognitive impairment; yet no neuroimaging framework has provided individual-level markers of symptom severity that remain anatomically interpretable. Here, we present an interpretable AI-based framework that addresses this gap by mapping high-dimensional resting-state rs-fMRI dynamics onto a low-dimensional latent manifold using self-supervised contrastive learning with a new attribution method to localize the highest decodable regions. Applied to two independent schizophrenia-spectrum cohorts, the label-free latent space supports individual-level prediction across clinical features of the disorder, including symptom severity and cognitive function. The attribution maps identify a disease-specific pathological footprint concentrated in prefrontal, posterior cerebellar and temporal areas that diverge from the manifold organization observed in healthy controls, which was dominated by auditory, limbic, and ventral-striatal circuits. These results establish an interpretable latent space framework for characterizing the distributed neural substrates of schizophrenia symptoms at the level of the individual patient, and provide an anatomically grounded route toward precision decoding of symptom severity.

neuroscience↗

bioRxiv2024

Unraveling the complexity of rat object vision requires a full convolutional network - and beyond

Despite their prominence as model systems to dissect visual cortical circuitry, it remains unclear whether rodents are capable of truly advanced processing of visual information. Here, we considered several psychophysical studies of rat object vision, and we used a deep convolutional neural network (CNN) to measure the computational complexity required to account for the patterns of rat performances reported in these studies, as well as for the animals perceptual strategies. We found that at least half of the CNN depth was required to match the modulation of rat classification accuracy in tasks where objects underwent variations of size, position and orientation. However, the full network was needed to equal the tolerance of rat perception to more severe image manipulations, such as partial occlusion and reduction of objects to their outlines. Finally, rats displayed a perceptual strategy that was way more invariant than that of the CNN, as they more consistently relied on the same set of diagnostic features across object transformations. Overall, these results reveal an unexpected level of sophistication of rat object vision, while reinforcing the intuition that, despite their proficiency in solving challenging image classification tasks, CNNs learn solutions that only marginally match those of biological visual systems.

neuroscience↗

bioRxiv2019

A mean-field approach to the dynamics of networks of complex neurons, from nonlinear Integrate-and-Fire to Hodgkin-Huxley models

We present a mean-field formalism able to predict the collective dynamics of large networks of conductance-based interacting spiking neurons. We apply this formalism to several neuronal models, from the simplest Adaptive Exponential Integrate-and-Fire model to the more complex Hodgkin-Huxley and Morris-Lecar models. We show that the resulting mean-field models are capable of predicting the correct spontaneous activity of both excitatory and inhibitory neurons in asynchronous irregular regimes, typical of cortical dynamics. Moreover, it is possible to quantitatively predict the populations response to external stimuli in the form of external spike trains. This mean-field formalism therefore provides a paradigm to bridge the scale between population dynamics and the microscopic complexity of the individual cells physiology. NEW & NOTEWORTHYPopulation models are a powerful mathematical tool to study the dynamics of neuronal networks and to simulate the brain at macroscopic scales. We present a mean-field model capable of quantitatively predicting the temporal dynamics of a network of complex spiking neuronal models, from Integrate-and-Fire to Hodgkin-Huxley, thus linking population models to neurons electrophysiology. This opens a perspective on generating biologically realistic mean-field models from electrophysiological recordings.

neuroscience↗