Inhomogeneous sparseness leads to dynamic instability during sequence memory recall in a recurrent neural network model
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Publication:2251526
DOI10.1186/2190-8567-3-8zbMath1291.92040OpenAlexW2097409179WikidataQ42859653 ScholiaQ42859653MaRDI QIDQ2251526
Daniel Medina, Christian Leibold
Publication date: 14 July 2014
Published in: The Journal of Mathematical Neuroscience (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1186/2190-8567-3-8
Cites Work
- Inhibition enhances memory capacity: optimal feedback, transient replay and oscillations
- The existence of persistent states in the brain
- Precise Capacity Analysis in Binary Networks with Multiple Coding Level Inputs
- Neural networks and physical systems with emergent collective computational abilities.
- Memory Capacity for Sequences in a Recurrent Network with Biological Constraints
- Associative memory: on the (puzzling) sparse coding limit
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