Book contents
- Frontmatter
- Contents
- List of contributors
- Preface
- Neurons and neural networks: general principles
- Synaptic plasticity, topological and temporal features, and higher cortical processing
- Spin glass models and cellular automata
- Cyclic phenomena and chaos in neural networks
- The cerebellum and the hippocampus
- Olfaction, vision and cognition
- Applications to experiment, communication and control
- 32 Computer-aided design of neurobiological experiments
- 33 Simulation of the prolactin level fluctuations during pseudopregnancy in rats
- 34 Applications of biological intelligence to command, control and communications
- 35 Josin's computational system for use as a research tool
- Author index
- Subject index
32 - Computer-aided design of neurobiological experiments
from Applications to experiment, communication and control
Published online by Cambridge University Press: 05 February 2012
- Frontmatter
- Contents
- List of contributors
- Preface
- Neurons and neural networks: general principles
- Synaptic plasticity, topological and temporal features, and higher cortical processing
- Spin glass models and cellular automata
- Cyclic phenomena and chaos in neural networks
- The cerebellum and the hippocampus
- Olfaction, vision and cognition
- Applications to experiment, communication and control
- 32 Computer-aided design of neurobiological experiments
- 33 Simulation of the prolactin level fluctuations during pseudopregnancy in rats
- 34 Applications of biological intelligence to command, control and communications
- 35 Josin's computational system for use as a research tool
- Author index
- Subject index
Summary
Motivation
A neuron's electrophysiological response to a disturbance is within the millisecond range and consists of action potentials which are produced when the threshold potential is being crossed. This response can be measured with microelectrodes and related to the stimulus. With neuroanatomical responses, this is a completely different task. Morphogenetic changes, such as reactive synaptogenesis or degeneration, take hours to days, so they cannot be directly observed by microelectrodes or the like. Besides, it would be very hard to relate the measured functional changes of electrical signals to changes in the synaptical ‘hardware’. Therefore, animals have to be taken at different stages of time and the development of the central nervous system (with or without disturbances) has to be concluded from changes of their individual morphology. Moreover, to get reliable data, several animals have to be sacrificed at each time step. Sometimes, even that may not be enough, as the following example may show.
Measuring the development of the number of synapses in rat cortex from postnatal day 2 to adulthood (Balcar, Dammasch & Wolff, 1983), we expected to fit the data with a sigmoid curve corresponding to logistic growth, but the material appeared to be systematically disturbed. A population-kinetic model (Wagner & Wolff, subm.) developed at that time, could explain the disturbance as a temporary overshoot of free synaptic offers that were not distinguishable from bound synaptic elements.
- Type
- Chapter
- Information
- Computer Simulation in Brain Science , pp. 495 - 503Publisher: Cambridge University PressPrint publication year: 1988