Mechanistic models of single-neuron dynamics have been extensively studied in
computational neuroscience. However, identifying which models can quantitatively …

Neurons and glial cells are endowed with membranes that express a rich repertoire of ion
channels, transporters, and receptors. The constant flux of ions across the neuronal and glial …

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing
and disparate experimental datasets at multiple scales. The NetPyNE tool (www. netpyne …

The cellular components of mammalian neocortical circuits are diverse, and capturing this
diversity in computational models is challenging. Here we report an approach for generating …

At many scales in neuroscience, appropriate mathematical models take the form of complex
dynamical systems. Parameterizing such models to conform to the multitude of available …

Computational models in neuroscience typically contain many parameters that are poorly
constrained by experimental data. Uncertainty quantification and sensitivity analysis provide …

Dendrites integrate inputs nonlinearly, but it is unclear how these nonlinearities contribute to
the overall input-output transformation of single neurons. We developed statistically …

Hippocampal place cells are activated sequentially as an animal explores its environment.
These activity sequences are internally recreated ('replayed'), either in the same or reversed …

Since more than a decade ago, three statements about spiking neuron (SN)
implementations have been widely accepted: 1) Hodgkin and Huxley (HH) model is …

Complex spike bursting (CSB) is a characteristic electrophysiological signature exhibited by
several neuronal subtypes and has been implicated in neural plasticity, learning, perception …