Computational neuroscience

Computational neuroscience is a branch of neuroscience which uses computational approaches, to study the nervous system. Computational approaches include mathematics, statistics, computer simulations, and abstractions which are used across many subareas of neuroscience including development, structure, physiology and cognitive abilities of the nervous system. Computational neuroscience is a branch of neuroscience which uses computational approaches, to study the nervous system. Computational approaches include mathematics, statistics, computer simulations, and abstractions which are used across many subareas of neuroscience including development, structure, physiology and cognitive abilities of the nervous system. The computational neuroscience discipline roughly divides into two subfields. A first, which may be called theoretical neuroscience focuses on principled approaches towards arriving at meaningful models of the nervous system. This field contains many aspects of mathematical neuroscience which employs mathematical techniques to arrive at models. Models in theoretical neuroscience are often aimed at capturing the essential features of the biological system at multiple spatial-temporal scales, from membrane currents, and chemical coupling via network oscillations, columnar and topographic architecture, all the way up to behavior. These computational models frame hypotheses that can often be directly tested by biological or psychological experiments. A second subfield, which is often called neural data science focuses on approaches towards making sense of the progressively larger datasets in neuroscience. This may include the processing of electrophysiological or imaging data, the fitting of models to data, and the comparison of models. These two subfields are highly synergistic and many papers draw from both traditions. The term 'computational neuroscience' was introduced by Eric L. Schwartz, who organized a conference, held in 1985 in Carmel, California, at the request of the Systems Development Foundation to provide a summary of the current status of a field which until that point was referred to by a variety of names, such as neural modeling, brain theory and neural networks. The proceedings of this definitional meeting were published in 1990 as the book Computational Neuroscience. The first open international meeting focused on Computational Neuroscience was organized by James M. Bower and John Miller in San Francisco, California in 1989 and has continued each year since as the annual CNS meeting. The first graduate educational program in computational neuroscience was organized as the Computational and Neural Systems Ph.D. program at the California Institute of Technology in 1985. The early historical roots of the field can be traced to the work of people such as Louis Lapicque, Hodgkin & Huxley, Hubel & Wiesel, and David Marr, to name a few. Lapicque introduced the integrate and fire model of the neuron in a seminal article published in 1907. This model is still popular today for artificial neural networks studies because of its simplicity (see a recent review). About 40 years later, Hodgkin & Huxley developed the voltage clamp and created the first biophysical model of the action potential. Hubel & Wiesel discovered that neurons in the primary visual cortex, the first cortical area to process information coming from the retina, have oriented receptive fields and are organized in columns. David Marr's work focused on the interactions between neurons, suggesting computational approaches to the study of how functional groups of neurons within the hippocampus and neocortex interact, store, process, and transmit information. Computational modeling of biophysically realistic neurons and dendrites began with the work of Wilfrid Rall, with the first multicompartmental model using cable theory.