Jacob A. Zavatone-Veth

This study explores how structured and disordered connectivity in recurrent neural networks affects population dynamics and task performance, finding that minimal restructuring can achieve task-relevant representations.

This study shows that efficient coding recovers canonical olfactory features by maximizing mutual information layer-wise under biophysical assumptions.

This paper introduces a model for olfactory compressed sensing that simultaneously detects odor presence and estimates concentrations, showing it can be implemented in a biologically plausible circuit.

This paper presents a spiking neural network model that combines sampling-based inference with attractor dynamics to study head-direction systems, making testable predictions about neural activity patterns.

This paper develops a normative model of place field reorganization during reward maximization, showing it unifies navigation phenomena and improves policy learning.

The paper shows that partial observation of neural circuits can lead to mechanistic mismatches in data-constrained models of neural dynamics.

This paper proposes a rate-based Poisson compressed sensing model for the olfactory bulb that accurately detects odors within a single sniff.

The paper manipulates neuron dynamics in Drosophila to study their role in motion detection and velocity sensitivity.

This paper shows how natural gradient methods can enable fast sampling in spiking neural networks by leveraging population geometry for probabilistic inference.

The paper analyzes trial-to-trial timing variability in synfire chains, identifying local, global, and jitter components and validating findings in zebra finch models.

The paper develops a minimal synaptic model for Drosophila T4 cells to explain direction-selective computations and tests its ability to reproduce experimental responses across various stimuli.