Artificial Neurons with Arbitrarily Complex Internal Structures

Details Artificial Neurons with Arbitrarily Complex Internal Structures Artificial Neurons with Arbitrarily Complex Internal Structures

2001-08-17

arxiv.org/abs/cs/0108009v1

Neurocomputing, vol. 47, pp. 103-118 (2002).

Computer Science

Neural and Evolutionary Computing

22 pages, 2 figures

Scientific paper

Artificial neurons with arbitrarily complex internal structure are introduced. The neurons can be described in terms of a set of internal variables, a set activation functions which describe the time evolution of these variables and a set of characteristic functions which control how the neurons interact with one another. The information capacity of attractor networks composed of these generalized neurons is shown to reach the maximum allowed bound. A simple example taken from the domain of pattern recognition demonstrates the increased computational power of these neurons. Furthermore, a specific class of generalized neurons gives rise to a simple transformation relating attractor networks of generalized neurons to standard three layer feed-forward networks. Given this correspondence, we conjecture that the maximum information capacity of a three layer feed-forward network is 2 bits per weight.

Affiliated with

Also associated with

No associations

Rating

Artificial Neurons with Arbitrarily Complex Internal Structures does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Artificial Neurons with Arbitrarily Complex Internal Structures, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Artificial Neurons with Arbitrarily Complex Internal Structures will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-544111