Biology subjects

Buccellato, A.

Publications and source records attributed to Buccellato, A..

2 recordsLinked to original sources

bioRxiv2025

How Intrinsic Neural Timescales Relate To Event-Related Activity - Key Role For Intracolumnar Connections

The relationship of the brains intrinsic neural timescales (INTs) during the resting state with event-related activity in response to external stimuli remains poorly understood. Here, we bridge this gap by combining computational modeling with magnetoencephalography (MEG) data to investigate the relation of intrinsic neuronal timescales (INT) with task-related activity, e.g., event-related fields (ERFs). Using the Jansen-Rit model, we first show that intracolumnar (and thus intra-regional) excitatory and inhibitory connections (rather than inter-regional feedback, feedforward and lateral connections between the columns of different regions) strongly influence both resting state INTs and task-related ERFs. Secondly, our results demonstrate a positive relationship between the magnitude of event-related fields (mERFs) and INTs, observed in both model simulations and empirical MEG data collected during an emotional face recognition task. Thirdly, modeling shows that the positive relationship of mERF and INT depends on intracolumnar connections through observing that the correlation between them disappears for fixed values of intracolumnar connections. Together, these findings highlight the importance of intracolumnar connections as a shared biological mechanism underlying both the resting-states INTs and the task-states event-related activity including their interplay.

neuroscience↗

bioRxiv2021

Rat sensitivity to multipoint statistics is predicted by efficient coding of natural scenes

Efficient processing of sensory data requires adapting the neuronal encoding strategy to the statistics of natural stimuli. Previously, in Hermundstad et al. 2014, we showed that local multipoint correlation patterns that are most variable in natural images are also the most perceptually salient for human observers, in a way that is compatible with the efficient coding principle. Understanding the neuronal mechanisms underlying such adaptation to image statistics will require performing invasive experiments that are impossible in humans. Therefore, it is important to understand whether a similar phenomenon can be detected in animal species that allow for powerful experimental manipulations, such as rodents. Here we selected four image statistics (from single- to four-point correlations) and trained four groups of rats to discriminate between white noise patterns and binary textures containing variable intensity levels of one of such statistics. We interpreted the resulting psychometric data with an ideal observer model, finding a sharp decrease in sensitivity from 2- to 4-point correlations and a further decrease from 4- to 3-point. This ranking fully reproduces the trend we previously observed in humans, thus extending a direct demonstration of efficient coding to a species where neuronal and developmental processes can be interrogated and causally manipulated.

neuroscience↗