Benjamin Adric Dunn

Neural spiketrains and population vectors entangle neural representations

The paper introduces methods to disentangle latent variables driving neural activity by identifying bidirectional local matrices in neural data, applied to reveal movement-driven structures in motor cortex.

Toroidal topology of grid-cell activity precedes spatial navigation during development

This study shows that toroidal manifolds in the MEC emerge early in rat pups, preceding spatial experience and exploration, suggesting innate computational motifs in spatial navigation.

Prefrontal cortex encodes behavior states decoupled from movement

This study shows that prefrontal cortex encodes behavior states independent of movement, with neural ensembles representing specific actions and evolving at slower timescales than motor cortex.

Uncovering 2-D toroidal representations in grid cell ensemble activity during 1-D behavior

The paper shows that grid cell population activity forms a 2-D toroidal state space during 1-D behavior, using UMAP and persistent homology to analyze topology.

Investigating egocentric tuning in hippocampal CA1 neurons using simulated data

The paper investigates egocentric tuning in hippocampal CA1 neurons using simulated data and finds a high false positive rate in detecting egocentric bearing modulation.

Insights in neuronal tuning: Navigating the statistical challenges of autocorrelation and missing variables

This study addresses statistical challenges in neuronal tuning by comparing covariate selection methods using simulated and calcium data, finding that cross-validation with cyclical shift permutation test offers better power while controlling error rates.

A topological perspective on the dual nature of the neural state space and the correlation structure

The paper compares neural correlations and state space approaches using topology to infer neural representations, showing their complementary value in characterizing covariate spaces.

Bayesian inference of spike-timing dependent plasticity learning rules from single neuron recordings in humans

The paper infers spike-timing dependent plasticity learning rules from human neural recordings using a Bayesian framework.

Behavioral decomposition reveals rich encoding structure employed across neocortex

The paper analyzes neural recordings from multiple cortical regions in freely foraging rats to reveal how different brain areas encode behavioral features like movement and posture.

Toroidal topology of population activity in grid cells

The paper shows that grid cell activity forms a toroidal manifold, supporting continuous attractor network dynamics in the medial entorhinal cortex.

Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience

This paper develops a domain adaptation framework to remove inter-experimental variability in systems neuroscience data while preserving biological signals, showing improved performance over existing methods.

Action representation in the mouse parieto-frontal network

This study investigates whether mirror neuron-like activity exists in the mouse parieto-frontal network during action performance and observation, finding no overlap in neural responses.

Efficient cortical coding of 3D posture in freely behaving rats

This study shows that parietal and frontal motor cortices efficiently represent 3D posture in freely behaving rats with high accuracy.

Decoding of neural data using cohomological learning

This paper introduces cohomological learning to decode neural features from mouse movement data, identifying head direction cells and finding that decoded values contain more information than tracked head direction.

Grid cells show field-to-field variability and this explains the aperiodic response of inhibitory interneurons

The paper shows grid cell field firing rates vary, explaining why inhibitory interneurons have aperiodic firing patterns and causing hippocampal cell remapping.