Matthias H. Hennig

This paper shows that excitatory-inhibitory networks with local plasticity can perform predictive inference without explicit error representations, using a BCM-like rule to maintain weights on a consistency manifold.

The paper shows thalamic input coordinates excitation and inhibition in motor cortex to reduce neural variability during movement initiation.

This paper introduces a local inhibitory plasticity rule that controls neuronal firing rate and supralinear dendritic integration in striatal projection neurons.

UnitRefine automates spike sorting curation using machine learning models trained on expert annotations, achieving human-level performance across species and datasets.

This paper introduces the BAND model to extract neural dynamics for feedback-driven motor corrections during reaching, finding that corrections are encoded in small neural variability in M1 cortex and are transient and sensory-driven.

The paper introduces a novel plasticity model and shows that fast fluctuation sensitivity enables precise temporal regulation of plasticity via inhibition in recurrent networks.

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

SpikeInterface provides a unified framework to run, compare, and benchmark spike sorting algorithms, reducing manual curation and enabling comprehensive benchmarking of automated methods.

The paper examines movement-invariant and movement-specific signaling in layer 5B of motor cortex in mice, finding that most neurons show movement-invariant responses while a minority display movement specificity.

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.

This paper introduces a Bayesian model using variational autoencoder for localizing spike sources in extracellular recordings, showing improved accuracy over heuristic methods.

This paper presents a non-parametric method for classifying retinal ganglion cells using spike train distance measures and evaluates it on mouse retinal recordings.

This paper introduces an efficient spike sorting method for high-density multielectrode arrays, enabling fast and reliable clustering of millions of events.