Lorenzo Posani

The paper identifies a novel neural code in the mouse medial frontal cortex that supports spatial generalization through extrapolation of coordinates across regions.

The paper investigates how the basolateral amygdala encodes emotional states through representational geometry, finding that valence and motor responses are encoded in orthogonal directions enabling specialized readouts.

Decodanda is a Python toolbox for decoding and geometric analysis of neural representations, providing tools to infer encoded variables and assess representational geometry with best-practice safeguards.

This study examines how advanced amyloid beta pathology in APP NL-G-F mice affects spatial coding and neuronal activity in the medial entorhinal cortex, revealing impaired spatial information processing and hyperactivity.

The paper analyzes neural representations across the cortical hierarchy and finds that while categorical encoding is rare within regions, overall cortical structure supports categorical organization and maximal separability.

The paper investigates prefrontal cortex (PFC) neuronal activity during a complex spatial working memory task in rodents and finds retrospective mnemonic representations of spatial information during the delay phase, particularly on error trials.

The paper introduces a method to identify minimal epistatic networks in proteins using sequence and mutational data, achieving performance comparable to deep networks with greater interpretability.

The paper identifies a synaptic novelty signal in hippocampal circuits that drives a switch from generalization to discrimination during memory formation.

This study investigates how hippocampal CA2 neurons represent familiar and novel individuals, finding that familiarity alters the geometric structure of neural representations to support different social memory processes.

This study uses 2-photon imaging in mice to show that the dentate gyrus enhances environmental differences while CA1 reflects behavioral performance during memory encoding.

This paper develops a dual neural activity decoder and attractor neural network model to analyze hippocampal recordings and show how the hippocampus maintains spatial and contextual representations during rapid contextual changes.

This paper introduces a functional-connectivity-based decoder for spatial representation decoding in hippocampal CA1, showing improved performance over existing methods and revealing prolonged instability after teleportation events.