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Neural Networks with Dynamic Synapses
Open accessAbstract
Transmission across neocortical synapses depends on the frequency of presynaptic activity (Thomson & Deuchars, 1994). Interpyramidal synapses in layer V exhibit fast depression of synaptic transmission, while other types of synapses exhibit facilitation of transmission. To study the role of dynamic synapses in network computation, we propose a unified phenomenological model that allows computation of the postsynaptic current generated by both types of synapses when driven by an arbitrary pattern of action potential (AP) activity in a presynaptic population. Using this formalism, we analyze different regimes of synaptic transmission and demonstrate that dynamic synapses transmit different aspects of the presynaptic activity depending on the average presynaptic frequency. The model also allows for derivation of mean-field equations, which govern the activity of large, interconnected networks. We show that the dynamics of synaptic transmission results in complex sets of regular and irregular regimes of network activity.
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Citations
Cites
Differential signaling via the same axon of neocortical pyramidal neurons
Markram, H., Wang, Y., & Tsodyks, M. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.9.5323 · Cited by 1,102
Self-citation
Cited by
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
The magical number 4 in short-term memory: A reconsideration of mental storage capacity
Cowan, N. (2001) · Behavioral and Brain Sciences · doi:10.1017/s0140525x01003922 · Cited by 6,779
Synaptic theory of working memory
Mongillo, G., Barak, O., & Tsodyks, M. (2008) · Science · doi:10.1126/science.1150769 · Cited by 1,253
Self-citationDisynaptic Inhibition between Neocortical Pyramidal Cells Mediated by Martinotti Cells
Silberberg, G., & Markram, H. (2007) · Neuron · doi:10.1016/j.neuron.2007.02.012 · Cited by 768
Self-citationPhenomenological models of synaptic plasticity based on spike timing
Morrison, A., Diesmann, M., & Gerstner, W. (2008) · Biological Cybernetics · doi:10.1007/s00422-008-0233-1 · Cited by 593
Metadata
- Authors
- Tsodyks, M., Pawelzik, K., & Markram, H.
- Venue
- Neural Computation
- Publisher
- MIT Press - Journals
- Published
- 1998-05-01
- Volume
- 10
- Issue
- 4
- Pages
- 821-835
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2148654448
- Cited by
- 942
- References
- 1
- FWCI
- 5.90
- Type
- journal article
- Language
- en
- Open access
- green
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- Tsodyks, M., Pawelzik, K., & Markram, H. (1998). Neural Networks with Dynamic Synapses. Neural Computation. https://doi.org/10.1162/089976698300017502
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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.
