Toward modeling a dynamic biological neural network
โ Scribed by M.D. Ross; J.E. Dayhoff; D.H. Mugler
- Publisher
- Elsevier Science
- Year
- 1990
- Tongue
- English
- Weight
- 854 KB
- Volume
- 13
- Category
- Article
- ISSN
- 0895-7177
No coin nor oath required. For personal study only.
โฆ Synopsis
Mammalian macular endorgans are linear bioaccelerometers located in the vestibular membranous labyrinth of the inner ear. In this paper, the organization of the endorgan is interpreted on physical and engineering principles. This is a necessary prerequisite to mathematical and symbolic modeling of information processing by the macular neural network. Mathematical notations that describe the functioning system were used to produce a novel, symbolic model. The model is six-tiered and is constructed to mimic the neural system. Initial simulations show that the network functions best when some of the detecting elements (type I hair cells) are excitatory and others (type II hair cells) are weakly inhibitory. The simulations also illustrate the importance of disinhibition of receptors located in the third tier in shaping nerve discharge patterns at the sixth tier in the model system.
๐ SIMILAR VOLUMES
## Abstract We consider static and dynamic approaches to the specification of probability distributions on graphs, consistent with desired statistical properties such as degree distributions, for use in modeling biological networks. In the static approach we develop analytical approximations to the
In this paper, a new method of finite element model updating using neural networks is presented. Many previous model updating techniques have exhibited inconsistent performance when subjected to noisy experimental data. From this background it is clear that a successful model updating method must be
This paper presents a Recursive Neural Network (RNN) manoeuvring simulation model for surface ships. Inputs to the simulation are the orders of rudder angle and ship's speed and also the recursive outputs velocities of sway and yaw. This model is used to test the capabilities of artificial neural ne