Key Word Search
by term...

by definition...

for letter: "S"
Results

Saxitoxin
 
Schrödinger, Erwin
 
Schrödinger's Cat
 
Science
 
The Scientific Method
 
Searle, John
 
Secondary Conditioned Reflex
 
Second-Order Reflex
 
Self Taught
 
Sensitization
 
Serotonin
 
Short Term Potentiation
 
Signal
 
Signal Propagation
 
Signaling
 
Sodium
 
Soft Science
 
Sokal Affair
 
Sokal, Alan
 
Sokal Hoax
 
Sokalism
 
The Stability-Plasticity Dilemma
 
The Stability-Plasticity Problem
 
The Stability-Plasticity Question
 
Static Connection
 
STORM
 
STX
 
SVG
 
Synapse
 
Synapse Weight
 
Synaptic
 



Unless otherwise indicated, all glossary content is:
(C) Copyright 2008-2017
Dominic John Repici
~ ALL RIGHTS RESERVED ~
No part of this Content may be copied without the express written permision of Dominic John Repici.






























 



 
Synapse

 
Synapses are, by definition, connections (etymology). Conventionally, most synapses have been thought of as inputs (unidirectional). They receive stimuli from other neurons and neuron-like processes (senses, etc.) which, in turn, affect the output of the neuron they are part of.

When stimulated, a synapse's effect on its neuron is either to increase the likelihood of a positive output on the neuron's axon (excite), or to decrease the likelihood of a positive output on the neuron's axon (inhibit).

More recent studies (i.e., within the last 30 years) have shown evidence that synapses have some bi-directional properties. That is, that they do have some effect on the pre-synaptic cell. A new learning algorithm, called Influence Learning is based on these observations (background).




. . . . . . .
Related Video

. . . . . . .





. . . . .
Synapses In Neural Networks


In traditional artificial neural networks (ANNs), synapses are implemented with signed weight values. Typically, the weight values are used to modulate the signal value at each synapse. The weighted results from many such calculations, at many synapses, are then summed together to produce an axon level which is passed on to the neuron's output processes.

Netlab™ introduces a new form of synapse (or synaptic connection-point) called a multitemporal synapse. This synapse includes multiple connection-weights for a given connection, where each weight (representing a connection strength) can learn and forget at a different rate. This allows a set of fast-learning weights to quickly form detailed responses to familiar situations, driven and prompted by the blunt beginnings of correct responses maintained in the slower-learning permanent weights.




. . . . .
Further Reading

Also: Pre Synaptic     Multitemporal Synapse     PTP

 
 


































Web-based glossary software: (c) Creativyst, 2001-2017