- Hybrid neural network
The term hybrid neural network can have two meanings:
biological neural networksinteracting with artificial neuronal models, and
Artificial neural networkswith a symbolic part (or, conversely, symbolic computations with a connectionist part).
As for the first meaning, the
artificial neurons and synapses in hybrid networks can be digitalor analog. For the digital variant voltage clamps are used to monitor the membrane potentialof neurons, to computationally simulate artificial neurons and synapses and to stimulate biological neurons by inducing synaptic. For the analog variant, specially designed electronic circuits connect to a network of living neurons through electrodes.
As for the second meaning, incorporating elements of symbolic computation and artificial neural networks into one model was an attempt to combine the advantages of both paradigms while avoid the shortcomings. Symbolic representations have advantages with respect to explicitly, direct control, fast initial coding, dynamic variable binding and knowledge abstraction. Representations of artificial neural networks, on the other hand, show advantages for biological plausibility, learning, robustness (fault-tolerant processing and graceful decay), and generalization to similar input. Since the early 1990s many attempts have been made to reconcile the two approaches.
* [http://scienceweek.com/2004/sc041015-3.htm Biological and artificial neurons]
* [http://www.cogsci.rpi.edu/~rsun/wsp98.html Connecting symbolic and connectionist approaches]
* Connectionism vs. Computationalism debate
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