Conductance-based models of neural activity produce large amounts of data that can be
hard to visualize and interpret. We introduce visualization methods to display the dynamics …

Conventionally, the parameters of neuronal models are hand-tuned using trial-and-error
searches to produce a desired behavior. Here, we present an alternative approach. We …

The electrical characteristics of many neurons are remarkably robust in the face of changing
internal and external conditions. At the same time, neurons can be highly sensitive to …

Parameters for models of biological systems are often obtained by averaging over
experimental results from a number of different preparations. To explore the validity of this …

Most neurons have large numbers of voltage-and time-dependent currents that contribute to
their electrical firing patterns. Because these currents are nonlinear, it can be difficult to …

The rich dynamical nature of neurons poses major conceptual and technical challenges for
unraveling their nonlinear membrane properties. Traditionally, various current waveforms …

Neurons, and realistic models of neurons, typically express several different types of voltage-
gated conductances. These conductances are subject to continual regulation. Therefore it is …

Neurons and circuits each with a distinct balance of intrinsic and synaptic conductances can
generate similar behavior but sometimes respond very differently to perturbation. Examining …

We analyze neuron models in which the maximal conductances of membrane currents are
slowly varying dynamic variables regulated by the intracellular calcium concentration. These …

I summarize recent computational and experimental work that addresses the inherent
variability in the synaptic and intrinsic conductances in normal healthy brains and shows that …