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Learning in Networks of Cortical Neurons
Open accessAbstract
The results presented here demonstrate selective learning in a network of real cortical neurons. We focally stimulate the network at a low frequency (0.3–1 Hz) until a desired predefined response is observed 50 ± 10 msec after a stimulus, at which point the stimulus is stopped for 5 min. Repeated cycles of this procedure ultimately lead to the desired response being directly elicited by the stimulus. By plotting the number of stimuli required to achieve the target response in each cycle, we are able to generate learning curves. Presumably, the repetitive stimulation is driving changes in the circuit, and we are selecting for changes consistent with the predefined desired response. To the best of our knowledge, this is the first time learning of arbitrarily chosen tasks, in networks composed of real cortical neurons, is demonstrated outside of the body.
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Cites
Activity-dependent scaling of quantal amplitude in neocortical neurons
Turrigiano, G. G., Leslie, K. R., Desai, N. S., Rutherford, L. C., & Nelson, S. B. (1998) · Nature · doi:10.1038/36103 · Cited by 2,226
Cited by
Beggs, J. M., & Plenz, D. (2004) · The Journal of Neuroscience · doi:10.1523/jneurosci.0540-04.2004 · Cited by 587
Chiappalone, M., Bove, M., Vato, A., Tedesco, M., & Martinoia, S. (2006) · Brain Research · doi:10.1016/j.brainres.2006.03.049 · Cited by 403
Mjalli, F. S., Al-Asheh, S., & Alfadala, H. E. (2007) · Journal of Environmental Management · doi:10.1016/j.jenvman.2006.03.004 · Cited by 324
Selective Electrical Interfaces with the Nervous System
Rutten, W. L. C. (2002) · Annual Review of Biomedical Engineering · doi:10.1146/annurev.bioeng.4.020702.153427 · Cited by 276
Learning probabilistic neural representations with randomly connected circuits
Maoz, O., Tkačik, G., Esteki, M. S., Kiani, R., & Schneidman, E. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912804117 · Cited by 44
Metadata
- Authors
- Shahaf, G., & Marom, S.
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2001-11-15
- Volume
- 21
- Issue
- 22
- Pages
- 8782-8788
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- OpenAlex
- W2114079854
- Cited by
- 399
- References
- 1
- FWCI
- 5.35
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
