Walter Senn

The paper proposes a computational framework where cortico-subcortical circuits implement multi-head self-attention, aligning with human brain recordings during speech perception.

This paper introduces a neural circuit model that learns to segment relevant stimuli from continuous sensory input using reinforcement learning in spiking neuron populations.

The paper shows that perceptual reliability reduces membrane potential variability in cortical neurons during task-relevant stimuli.

The paper investigates how uncertainty modulates prediction errors in cortical microcircuits and shows that layer 2/3 neurons represent uncertainty-modulated prediction errors through specific inhibitory mechanisms.

The paper develops a computational model to explain how context shapes sensory processing by analyzing prediction errors in mice during tactile discrimination learning.

The paper introduces a neuronal least-action principle to model real-time learning in cortical circuits by minimizing voltage dynamics and error correction.

This paper shows dendritic modulation enables multitask representation learning in hierarchical sensory processing by allowing neurons to adaptively extract features across different contexts.

The paper shows that dendritic responses in L5 pyramidal neurons during learning correspond to unsigned temporal difference error components, with salience signals aiding task learning.

This paper presents alternative models for predictive olfactory learning in Drosophila, challenging traditional associative plasticity models and offering a framework to interpret DAN activity during learning.

The paper introduces a method to simplify dendritic neuron models while preserving their responses to various spikes, using parameter fits and experimental data without requiring morphological reconstructions.

The paper proposes a vision-independent learning mechanism for temporal integration in the oculomotor system using a recurrent network with segregated dendritic and somatic compartments.

This paper introduces a synaptic plasticity model that enables fast learning without catastrophic forgetting by combining early weight decay and selective consolidation.

The paper develops a stochastic model of Drosophila larval phototaxis using light intensity and directionality, showing larvae minimize light exposure while maximizing distance from the light source.