期刊
FRONTIERS IN NEUROSCIENCE
卷 11, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fnins.2017.00693
关键词
reservoir model; non-Hebbian learning; Hebbian plasticity; sequence generation; attractor dynamics; eigenvalue spectra
资金
- C-SPIN
- MARCO
- DARPA
- Semiconductor Research Corporation
- National Science Foundation
- Intel Corporation
- DoD Vannevar Bush Fellowship
Synaptic Plasticity, the foundation for learning and memory formation in the human brain, manifests in various forms. Here, we combine the standard spike timing correlation based Hebbian plasticity with a non-Hebbian synaptic decay mechanism for training a recurrent spiking neural model to generate sequences. We show that inclusion of the adaptive decay of synaptic weights with standard STDP helps learn stable contextual dependencies between temporal sequences, while reducing the strong attractor states that emerge in recurrent models due to feedback loops. Furthermore, we show that the combined learning scheme suppresses the chaotic activity in the recurrent model substantially, thereby enhancing its' ability to generate sequences consistently even in the presence of perturbations.
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