ArticleFrontiers in computational neuroscience2013
Learning and prospective recall of noisy spike pattern episodes.
Article in Frontiers in computational neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A model of working memory for encoding multiple items and ordered sequences exploiting the theta-gamma code.Cognitive neurodynamics · 2023Article
- A neural mass model of place cell activity: theta phase precession, replay and imagination of never experienced paths.Journal of computational neuroscience · 2015Article
- Synaptic scaling enables dynamically distinct short- and long-term memory formation.PLoS computational biology · 2013Article
- Spiking neural network connectivity and its potential for temporal sensory processing and variable binding.Frontiers in computational neuroscience · 2013Article
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2 authors.
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Abstract
Spike patterns in vivo are often incomplete or corrupted with noise that makes inputs to neuronal networks appear to vary although they may, in fact, be samples of a single underlying pattern or repeated presentation. Here we present a recurrent spiking neural network (SNN) model that learns noisy pattern sequences through the use of homeostasis and spike-timing dependent plasticity (STDP). We find that the changes in the synaptic weight vector during learning of patterns of random ensembles are approximately orthogonal in a reduced dimension space when the patterns are constructed to minimize overlap in representations. Using this model, representations of sparse patterns maybe associated through co-activated firing and integrated into ensemble representations. While the model is tolerant to noise, prospective activity, and pattern completion differ in their ability to adapt in the presence of noise. One version of the model is able to demonstrate the recently discovered phenomena of preplay and replay reminiscent of hippocampal-like behaviors.
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