Franz Scherr

The paper identifies how sparse cortical activity in mouse V1 is organized into flexible, condition-dependent spike-order codes through a combination of experimental data and a computational model.

This paper shows that biologically realistic spiking neural networks can learn from few examples by storing priors in synaptic weights and using network state for rapid information absorption.

This study trains spiking neural networks to perform a state change detection task and finds that cross-stimulus-modulated inhibition emerges as an optimal solution under Dales law constraints.

The paper trains large-scale models of cortical microcircuits to understand their computational organization and finds they match brain performance and reproduce unique experimental traits.

The paper analyzes a large-scale V1 model using anatomical and neurophysiological data to show it can robustly multiplex visual tasks and exhibit brain-like noise resilience.

This work analyzes a detailed cortical microcircuit model's ability to solve an image-change-detection task and identifies its computational strategy.

This paper introduces a new learning method for spiking neural networks that enables biologically plausible online learning through gradient descent, achieving performance comparable to BPTT.

This paper introduces a new learning approach for spiking neural networks that enables one-shot learning through local synaptic plasticity, inspired by dopamine-based learning signals in the brain.