A hierarchical organization of synaptic integration complexity across cerebellar neuron types

Connectome analysis of a cerebellum-like circuit for sensory prediction.

Connectomics is used to map the cell types and synaptic connections underlying a form of multi-layer continual learning that cancels predictable sensory responses in a cerebellum-like structure in electric fish, highlighting the potential of connectomics, in combination with cell-type-specific physiological recordings...

Gradients of local circuit architecture shape stability and information integration across the cortical hierarchy

The mammalian neocortex is a complex structure with many interacting regions and cell types. Multiple efforts have recently generated large datasets from multiple cortical regions to determine generalizing principles of cortical organization and activity. One such generalizing principle is the concept of a cortical hie...

Excitability across scales: a unifying EI model

Neuronal excitability is commonly modeled using different principles at the cellular and population scales, making it difficult to study how microscopic mechanisms shape macroscopic activity. We introduce a spatiotemporal excitatory-inhibitory (EI) model that unifies these scales through a common mixed-feedback archite...

Plasticity degeneracy underlies flexible formation and reconfiguration of spatial representations in hippocampal granule cells

A Dendritic Voltage Surrogate-Based Synaptic Learning Framework for Biophysically Detailed Neurons and Networks.

This work introduces a dendritic voltage surrogate that couples passive dendritic transfer impedances with the exact active currents computed from the original model, yielding explicit, differentiable approximations of membrane voltage.

Integrating astrocytic biophysical mechanisms into neural computations underlying cognitive and motor functions