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

5 citations · 7 across the 5 of their papers we have counts for

collaborators

7 papers

q-bio.NC2026

A Time-Resolved Framework for Quantifying Neuronal Network State Transitions

Ilya Auslender, Yasaman Heydari, Asiye Malkoç +3

Electrophysiological recordings provide powerful tools for characterizing the dynamics of neuronal networks. In vitro neuronal cultures offer a controlled setting for investigating…

q-bio.NC2026

Graph Analysis of Neuronal-Culture Connectivity Derived from a Reservoir-Computing Model

Ilya Auslender, Giorgio Letti, Yasaman Heydari +1

Graph-theoretical analysis offers a principled framework for quantifying emergent dynamics in neuronal cultures. Here, we present an analytical pipeline for inferring network-level…

physics.optics2026

Self-Pulsing Microring Resonator Networks for Bandwidth-Efficient Event Detection in an Optical Fiber Sensor

Alessio Lugnan, Yonas Seifu Muanenda, Ilya Auslender +4

The native processing of time-dependent signals from optical sensors by integrated photonic circuits can potentially bring significant advantages in terms of energy consumption, la…

physics.optics2026

Memory in Integrated Photonic Neural Networks: From Physical Mechanisms to Neuromorphic Architectures

Alessandro Foradori, Ilya Auslender, Stefano Biasi +4

The rapid scaling of artificial neural networks has exposed fundamental limitations of conventional von Neumann computing architectures. In these systems, the physical separation b…

physics.bio-ph2026

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

Matteo Dominici, Ilya Auslender, Clara Zaccaria +2

Objective. Long-term potentiation (LTP) is a fundamental mechanism underlying learning and memory, yet its investigation at the network level in vitro remains challenging, particul…

q-bio.QM2023★ 5 cited

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

Ilya Auslender, Giorgio Letti, Yasaman Heydari +2

In this study, we address the challenge of analyzing electrophysiological measurements in neuronal networks. Our computational model, based on the Reservoir Computing Network (RCN)…

q-bio.QM2023★ 2 cited

Reservoir Computing Model For Multi-Electrode Electrophysiological Data Analysis

Ilya Auslender, Lorenzo Pavesi

In this paper we present a computational model which decodes the spatio-temporal data from electro-physiological measurements of neuronal networks and reconstructs the network stru…