Timothy O’Leary

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.

Distinct hippocampal codes emerge during brain-machine interface navigation

This study shows that hippocampal place codes reconfigure during BMI-controlled navigation to form representations independent of locomotion and external cues.

Statistics of cortical representational drift can enable robust readout

The paper shows that heavy-tailed statistics of neural drift in the brain can be exploited by adaptive readout mechanisms to maintain stable information extraction.

Cell-type specific sensing and control of firing rate statistics via channel dynamics

The paper shows that calcium dynamics in neurons sense and control both mean and variance of spiking activity through conductance-based feedback, with homeostatic responses depending on cell-type-specific conductance mixtures.

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.

Small circuit principles for learning in large worlds

This paper introduces Hetlearn, a learning framework inspired by Drosophila neural circuits, which improves learning in uncertain environments with limited data and cognitive capacity.

Rapid, interpretable, data-driven models of neural dynamics using Recurrent Mechanistic Models

This paper introduces Recurrent Mechanistic Models (RMMs) for predicting neural dynamics, showing they can be trained quickly and are interpretable for small circuits.

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.

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.

How cerebellar architecture facilitates rapid online learning

This paper uses mathematical analysis and simulations to show that cerebellar architecture, specifically size expansions, helps overcome challenges in online learning of dynamic tasks by enabling dense granule cell activity.

Autoregulation of switching behavior by cellular compartment size

The paper proposes a self-regulating signaling motif that enables reliable switching in small cellular compartments by exploiting stochastic chemical reactions and inhibitory loops.

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.

Optimal synaptic dynamics for memory maintenance in the presence of noise

The paper shows that optimal memory maintenance requires systematic synaptic plasticity to be less than internal noise, predicting 50% or more of synaptic turnover is random.

Paradoxical relationships between active transport and global protein distributions in neurons

The paper quantifies Kv4.2 ion channel transport in neurons, revealing higher axonal transport despite dendritic expression, and explains this with mathematical models of intracellular trafficking.

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.

Homeostatic plasticity rules that compensate for cell size are susceptible to channel deletion

The paper shows that homeostatic plasticity mechanisms that compensate for cell size changes are vulnerable to channel deletions.

Fundamental bounds on learning performance in neural circuits

The paper shows that neural circuits have an optimal size for learning performance, where larger circuits can either enhance complex behaviors or improve learning of simpler tasks, but beyond a point, increased size impairs learning due to noise.

Neural circuit robustness to acute, global physiological perturbations

The paper shows that a crustacean pacemaker circuit is robust to temperature and pH perturbations, with consistent network state transitions despite high parameter variability.