Wenhao Zhang

Scale-mixture Langevin sampling in the subspaces of recurrent cortical circuit dynamics

The paper identifies a scale-mixture Langevin sampling mechanism in recurrent cortical circuits, showing how nonlinear dynamics produce heavy-tailed statistics without explicit non-Gaussian components.

Concurrent model evidence computation and posterior sampling in continuous attractor network subspaces

This study shows continuous attractor networks can simultaneously compute model evidence and posterior sampling in distinct subspaces, advancing understanding of neural computation.

From Representation to Action: A Unified Laplacian Framework for Spatial Representation and Path Planning

This paper introduces a unified Laplacian framework that links spatial representations to navigation policies, showing that spectral decompositions of the Laplacian can enable efficient goal-reaching in complex environments.

Holistic Motor Control of Zebra Finch Song Syllable Sequences

This study shows that the zebra finch's HVC region generates learned song syllable sequences independently of major input pathways, relying on local circuits for sustained production.

Towards a mental programming neural circuit: Insights from working memory sequence manipulation

This paper proposes a neural circuit model for working memory sequence manipulation, demonstrating its ability to perform two-item swapping and generalize to complex permutations using algebraic principles.

Canonical recurrent neural circuits: A unified sampling machine for static and dynamic inference

This paper studies how a canonical recurrent neural circuit performs dynamic inference using sampling algorithms, showing it can infer both static and dynamic latent states.

Natural gradient Bayesian sampling automatically emerges in canonical cortical circuits

The paper shows canonical cortical circuits naturally implement advanced Bayesian sampling algorithms, including natural gradient methods, through nonlinear dynamics and interneuron control.

Firing rate adaptation affords place cell theta sweeps, phase precession and procession

This study shows that firing rate adaptation in a continuous attractor neural network explains theta phase precession and procession in hippocampal place cells.

Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons

The paper shows that recurrent neural circuits with Poisson spiking variability perform optimal Bayesian inference by sampling from posterior stimulus distributions.

Representational geometries reveal differential effects of response correlations on population codes in neurophysiology and functional magnetic resonance imaging

The paper investigates how response correlations affect population codes in neurophysiology and fMRI by analyzing Fisher information and principal components.

A Recurrent Neural Circuit Mechanism of Temporal-scaling Equivariant Representation

This paper proposes a neural circuit model that generates temporally scalable sequences through Lie group principles, showing consistency with experimental data.

A spiking neural circuit model for learning multi-sensory integration

This paper proposes a spiking neural circuit model that explains the emergence of congruent and opposite neurons in multi-sensory integration and suggests it may be a canonical circuit in cortical areas.

Recurrent circuit based neural population codes for stimulus representation and inference

The paper shows that recurrent neural circuits can encode stimulus features through population codes, with variability aligned to stimulus directions and modulated by internal priors.

Distributed Sampling-based Bayesian Inference in Coupled Neural Circuits

This paper develops a model of coupled neural circuits that perform distributed Bayesian inference in high-dimensional stimulus spaces using Langevin sampling.

Emergence of opposite neurons in a decentralized firing-rate model of multisensory integration

The paper introduces a model where congruent and opposite neurons emerge through Hebbian and anti-Hebbian learning rules, reproducing experimentally observed tuning properties in multisensory integration.

Emergence of opposite neurons in a firing-rate model of multisensory integration

This paper introduces a network model that learns opposite neurons using Hebbian and Anti-Hebbian rules, explaining their formation and role in multisensory integration.

Concurrent Multisensory Integration and Segregation with Complementary Congruent and Opposite Neurons

The paper proposes a neural model for concurrent multisensory integration and segregation using congruent and opposite neurons to rapidly recognize stimuli based on sensory cues.