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Clara Zaccaria

Publications and source records attributed to Clara Zaccaria.

3 recordsLinked to original sources

A Frequency-Optimized Optogenetic Study of Network-Level Potentiation in Cortical Cultures on Microelectrode Arrays

Objective. Long-term potentiation (LTP) is a fundamental mechanism underlying learning and memory, yet its investigation at the network level in vitro remains challenging, particularly when optogenetic stimulation is used. The objective of this work is to develop a robust experimental and analytical framework for inducing and quantifying optogenetically driven LTP in neuronal cultures recorded with microelectrode arrays (MEAs). Approach. We first systematically investigate the effect of widefield optogenetic stimulation frequency on evoked neuronal activity, to identify a test-stimulus that reliably probes network responses without inducing activity modulation. By analyzing spike-rate dynamics during repeated stimulation, we characterize frequency-dependent response adaptation consistent with Channelrhodopsin-2 photocycle kinetics. Based on these results, an optimized low-frequency test-stimulus is selected and combined with a spatially confined tetanic optogenetic stimulation to induce LTP. Network responses are quantified using post-stimulus time histograms and a normalized efficacy metric, enabling electrode-wise and network-level analysis of plasticity. Main results. Low-frequency optical stimulation (<= 0.2 Hz) preserves stable evoked responses, whereas higher frequencies induce a pronounced sigmoid-like decay in firing rate. Following tetanic stimulation, a subset of electrodes exhibits robust and long-lasting potentiation, persisting for several hours. Significance. This work provides a systematic methodology for studying activity-dependent plasticity in optogenetically driven neuronal networks.

physics.bio-ph

Decoding Neuronal Networks: A Reservoir Computing Approach for Predicting Connectivity and Functionality

In this study, we address the challenge of analyzing electrophysiological measurements in neuronal networks. Our computational model, based on the Reservoir Computing Network (RCN) architecture, deciphers spatio-temporal data obtained from electrophysiological measurements of neuronal cultures. By reconstructing the network structure on a macroscopic scale, we reveal the connectivity between neuronal units. Notably, our model outperforms common methods like Cross-Correlation and Transfer-Entropy in predicting the network's connectivity map. Furthermore, we experimentally validate its ability to forecast network responses to specific inputs, including localized optogenetic stimuli.

q-bio.QM

A FEM enhanced Transfer Matrix method for optical grating design

A method to design gratings in integrated photonics, is presented. The method is based on a transfer matrix formalism enhanced by Finite Element Method (FEM) parameter calculations. The main advantages of the proposed technique are the easy of use, the fast optimization time and the versatility of the approach. Few examples of optimized gratings to obtain various scattered light field profiles for different applications are presented: a double-Gaussian profile, a flat top square profile, a spot profile on a chip surface, profiles suited to get efficient and selective coupling to single mode and multimode fibers. A discussion of the limits of the method and some insights on how to improve it are also discussed.

physics.optics