Tommaso Fellin

The paper investigates how odor information is routed through feedforward and feedback pathways in the mouse olfactory cortex, finding distinct temporal encoding and plasticity in neurons projecting to different targets.

This paper corrects optical aberrations in GRIN lens-based microendoscopes to improve imaging performance in deep brain regions.

MINT is a toolbox for analyzing multivariate neural information coding and transmission using information theory and dimensionality reduction, applied to simulated and real neural data.

This study investigates how mouse auditory cortex neurons encode ultrasound vocalizations from P12 to P21, finding early tuning to USV syllables and distinct neuronal clusters for different auditory stimuli.

The paper develops bias correction methods for Partial Information Decomposition (PID) in neural data, improving estimates of synergy and redundancy in mouse brain regions.

ASTRA is a deep learning tool for automated segmentation of astrocytic calcium signals in two-photon imaging, outperforming existing methods and enabling large-scale analysis.

NIT is an open-source toolbox for information-theoretic analysis of neural data, including calcium imaging, offering tools for encoding quantification, information transmission, and partial information decompositions.

The paper develops a method to classify network states from local field potentials in awake mice, showing that population signals predict single-cell activity across different network states.

This study shows that astrocytic calcium signals in the hippocampus encode spatial information complementary to neurons, enhancing spatial position decoding.

This paper develops and validates EEG proxies derived from point-neuron network models to accurately estimate EEG signals for comparison with experimental data.

This paper develops eFOV-microendoscopes for high-resolution two-photon imaging in awake mice, showing improved signal-to-noise ratio and precise neuronal activity evaluation.

This paper demonstrates how spiking neuron networks with moderate recurrent interactions generate a spectrum of non-rhythmic waking states in the neocortex, showing how different states encode presynaptic activity or afferent input strength.