The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
On the Dynamics of Small Continuous-Time Recurrent Neural Networks
Abstract
Dynamical neural networks are being increasingly employed in a variety of contexts, including as simple model nervous systems for autonomous agents. For this reason, there is a growing need for a comprehensive understanding of their dynamical properties. Using a combination of elementary analysis and numerical studies, this article begins a systematic examination of the dynamics of continuous-time recurrent neural networks. Specifically, a fairly complete description of the possible dynamical behavior and bifurcations of one- and two-neuron circuits is given, along with a few specific results for larger networks. This analysis provides both qualitative insight and, in many cases, quantitative formulas for predicting the dynamical behavior of particular circuits and how that behavior changes as network parameters are varied. These results demonstrate that even small circuits are capable of a rich variety of dynamical behavior (including chaotic dynamics). An approach to understanding the dynamics of circuits with time-varying inputs is also presented. Finally, based on this analysis, several strategies for focusing evolutionary searches into fruitful regions of network parameter space are suggested.
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Cites
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Cited by
Neuromorphic photonic networks using silicon photonic weight banks
Tait, A. N., de Lima, T. F., Zhou, E., Wu, A. X., Nahmias, M. A., Shastri, B. J., & Prucnal, P. R. (2017) · Scientific Reports · doi:10.1038/s41598-017-07754-z · Cited by 849
Dynamical approaches to cognitive science
Beer, R. D. (2000) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(99)01440-0 · Cited by 639
Self-citationEvolution and Analysis of Minimal Neural Circuits for Klinotaxis in Caenorhabditis elegans
Izquierdo, E. J., & Lockery, S. R. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.2606-10.2010 · Cited by 95
How (not) to model autonomous behaviour
Di Paolo, E. A., & Iizuka, H. (2008) · Biosystems · doi:10.1016/j.biosystems.2007.05.016 · Cited by 76
“This work intended to reproduce previous work by Yamauchi and Beer (1994) on landmark learning in one dimension, but with an agent moving freely in a two dimensional arena. The task is simple: find the location of a goal that cannot be seen from the distance using information provided by a fixed light. In half the cas…”
BackgroundMethodResultInfluentialFrom head to tail: a neuromechanical model of forward locomotion in Caenorhabditis elegans
Izquierdo, E. J., & Beer, R. D. (2018) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2017.0374 · Cited by 56
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Metadata
- Authors
- Beer, R. D.
- Venue
- Adaptive Behavior
- Publisher
- SAGE Publications
- Published
- 1995-03
- Volume
- 3
- Issue
- 4
- Pages
- 469-509
- ISSN
- 1059-7123 (ISSN-L)
- 1741-2633
- OpenAlex
- W2041533382
- Cited by
- 386
- References
- 39
- FWCI
- 5.75
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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