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
____
Senses
____
Sensitization
____
Sensory Receptor
____
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
____
Strong AI
____
STX
____
SVG
____
Synapse
____
Synapse Weight
____
Synaptic




Unless otherwise indicated, all glossary content is:
(C) Copyright 2008-2022
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 typically 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, 2001-2022