Michael E. Rule

Active Dendrites Enable Robust Spiking Computations despite Timing Jitter

The paper shows that long dendritic potentials enable reliable spiking computations despite timing jitter in asynchronous inputs.

Coordinated acetylcholine release and adaptation of neuronal representations in the retrosplenial cortex during contextual uncertainty

The paper shows acetylcholine release in the retrosplenial cortex correlates with changes in expected uncertainty and influences neuronal representation shifts and learning speed in mice.

Information theoretic measures of neural and behavioural coupling predict representational drift

The paper uses information theory to analyze neural population dynamics and finds that stable neurons show higher redundancy and synergy in pairwise interactions with other neurons.

An optical brain-machine interface reveals a causal role of posterior parietal cortex in goal-directed navigation

This study uses a calcium imaging-based brain-machine interface to show that posterior parietal cortex activity can directly drive navigational behavior in mice without requiring learning.

Spatiotemporal resonance in mouse primary visual cortex

The paper shows that mouse primary visual cortex exhibits spatiotemporal resonance patterns similar to humans, with multiple frequency peaks and standing wave characteristics in response to flicker.

Variational Log-Gaussian Point-Process Methods for Grid Cells

The paper develops variational log-Gaussian point-process methods to efficiently calculate spatial statistics for grid cells in large environments using Gaussian processes.

Simultaneous representation of multiple time horizons by entorhinal grid cells and CA1 place cells

This study shows that grid cells and place cells in rats have time shifts proportional to their spatial scales, enabling the representation of multiple time horizons for navigation.

Self-healing codes: how stable neural populations can track continually reconfiguring neural representations

The paper investigates how neural populations can maintain stable representations despite changing activity patterns through Hebbian learning and homeostasis.

The Information Theory of Developmental Pruning: Optimizing Global Network Architecture Using Local Synaptic Rules

The paper shows how local synaptic pruning rules can optimize global network architecture in a deep Boltzmann machine model of sensory encoding.

Stable task information from an unstable neural population

The paper shows that neural population drift in sensorimotor cortex is systematically constrained, allowing for stable task information despite apparent instability.

Neural Field Models for Latent State Inference: Application to Large-Scale Neuronal Recordings

The paper develops a method to infer latent neural states from large-scale neuronal recordings using neural field models and moment closure, applied to mouse retinal data to reveal refractory states in retinal waves.