Biomaterial Database

Biologically plausible neural computation. 1997

G Bugmann

Neurodynamics Research Group, School of Computing, University of Plymouth, UK. gbugmann@soc.plym.ac.uk

The function of a neuron can be described simultaneously at several levels of abstraction. For instance, a spike train represents the result of a computation done by a single neuron with its inputs, but it also represents the result of a complex function realized by the network in which the neuron is embedded. When models of large parts of the brain are considered, it may be desirable to use computational modules operating at a very abstract level. However, it is shown here that abstract neural functions depend on detailed features of the single neuron model used in the network reproducing the abstract function. Examples are given of the multiplicative function, motion detection, short-term memory and timing. All these operations rely on one or another feature of the extended Leaky Integrate-and-Fire neuron used in this paper, e.g. probabilistic synapses, post-synaptic currents modelled with alpha functions or partial reset of the somatic membrane. Consequently it is suggested that neural modelling at an abstract level does not obviate the need for a clear statement on the nature of the underlying model of biological neuron. In that sense, not many abstract functions are convincingly grounded, not even the standard formal neurons used in most artificial neural networks.

UI	MeSH Term	Description	Entries
D008564	Membrane Potentials	The voltage differences across a membrane. For cellular membranes they are computed by subtracting the voltage measured outside the membrane from the voltage measured inside the membrane. They result from differences of inside versus outside concentration of potassium, sodium, chloride, and other ions across cells' or ORGANELLES membranes. For excitable cells, the resting membrane potentials range between -30 and -100 millivolts. Physical, chemical, or electrical stimuli can make a membrane potential more negative (hyperpolarization), or less negative (depolarization).	Resting Potentials,Transmembrane Potentials,Delta Psi,Resting Membrane Potential,Transmembrane Electrical Potential Difference,Transmembrane Potential Difference,Difference, Transmembrane Potential,Differences, Transmembrane Potential,Membrane Potential,Membrane Potential, Resting,Membrane Potentials, Resting,Potential Difference, Transmembrane,Potential Differences, Transmembrane,Potential, Membrane,Potential, Resting,Potential, Transmembrane,Potentials, Membrane,Potentials, Resting,Potentials, Transmembrane,Resting Membrane Potentials,Resting Potential,Transmembrane Potential,Transmembrane Potential Differences
D008570	Memory, Short-Term	Remembrance of information for a few seconds to hours.	Immediate Recall,Memory, Immediate,Working Memory,Memory, Shortterm,Immediate Memories,Immediate Memory,Immediate Recalls,Memories, Immediate,Memories, Short-Term,Memories, Shortterm,Memory, Short Term,Recall, Immediate,Recalls, Immediate,Short-Term Memories,Short-Term Memory,Shortterm Memories,Shortterm Memory,Working Memories
D008954	Models, Biological	Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.	Biological Model,Biological Models,Model, Biological,Models, Biologic,Biologic Model,Biologic Models,Model, Biologic
D009039	Motion Perception	The real or apparent movement of objects through the visual field.	Movement Perception,Perception, Motion,Perception, Movement
D009474	Neurons	The basic cellular units of nervous tissue. Each neuron consists of a body, an axon, and dendrites. Their purpose is to receive, conduct, and transmit impulses in the NERVOUS SYSTEM.	Nerve Cells,Cell, Nerve,Cells, Nerve,Nerve Cell,Neuron