Name
Abstract | ||
|---|---|---|
Research involving artificial neural networks has tended to be driven towards efficient computation, especially in the domain of pattern recognition, or towards elucidating biological processes in the brain. Models have become more detailed as our understanding of the biology of the brain has increased, incorporating real-time behaviour of individual neurons interacting within complex system structures and dynamics. There are few examples of abstract and fully formal models of biologically plausible neural networks: in the neural networks literature models are often presented as a mixture of mathematical equations and natural language, supported by simulation code and associated experimental results. The informality often hides or obscures important aspects of a particular model, and leaves a large conceptual gap between the model descriptions and the usually low-level programming code used to simulate them. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.tcs.2015.08.019 | Theoretical Computer Science |
Keywords | Field | DocType |
Neural networks,Process algebra,Hybrid systems | Operational semantics,Computer science,Recurrent neural network,Natural language,Artificial intelligence,Backpropagation,Artificial neural network,Hybrid system,Process calculus | Journal |
Volume | Issue | ISSN |
623 | C | 0304-3975 |
Citations | PageRank | References |
0 | 0.34 | 54 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert Colvin | 1 | 68 | 8.67 |