Large-scale biophysical parameter estimation in single neurons via constrained linear regression (original) (raw)

Realistic single-neuron modeling

Seminars in Neuroscience, 1992

Realistic single-neuron modeling organizes and clarifies physiological hypotheses. It extends the experimenter's intuition and leads to testable predictions. A powerful new algorithm, several user-friendly software packages and the advent of fast, cheap computers have together made this tool accessible to a broad range of neurobiologists. Equally dramatic advances in experimental findings have increased the level of sophistication of the models. Here we provide a guide to single-neuron modeling, illustrate its power with a few examples and speculate on possible future directions for this rapidly growing field. Author Keywords: computer model; compartmental model; cable model; channel kinetics

Single-neuron models linking electrophysiology, morphology and transcriptomics across cortical cell types

Identifying the cell types constituting brain circuits is a fundamental question in neuroscience and motivates the generation of taxonomies based on electrophysiological, morphological and molecular single cell properties. Establishing the correspondence across data modalities and understanding the underlying principles has proven challenging. Bio-realistic computational models offer the ability to probe cause-and-effect and have historically been used to explore phenomena at the single-neuron level. Here we introduce a computational optimization workflow used for the generation and evaluation of more than 130 million single neuron models with active conductances. These models were based on 230 in vitro electrophysiological experiments followed by morphological reconstruction from the mouse visual cortex. We show that distinct ion channel conductance vectors exist that distinguish between major cortical classes with passive and h-channel conductances emerging as particularly importa...