Memory retention and spike-timing-dependent plasticity.

By Guy O. Billings, Mark C. W. van Rossum. 2009

78 citations recorded by the source corpus.

Memory systems should be plastic to allow for learning; however, they should also retain earlier memories. Here we explore how synaptic weights and memories are retained in models of single neurons and networks equipped with spike-timing-dependent plasticity. We show that for single neuron models, the precise learning rule has a strong effect on the memory retention time. In particular, a soft-bound, weight-dependent learning rule has a very short retention time as compared with a learning rule that is independent of the synaptic weights. Next, we explore how the retention time is reflected in receptive field stability in networks. As in the single neuron case, the weight-dependent learning rule yields less stable receptive fields than a weight-independent rule. However, receptive fields stabilize in the presence of sufficient lateral inhibition, demonstrating that plasticity in networks can be regulated by inhibition and suggesting a novel role for inhibition in neural circuits.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

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In Cellular and Molecular Neuroscience

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Citations in the corpus

Models of Metaplasticity: A Review of Concepts (2015, background): “Although this issue has been addressed theoretically for various models (Clopath et al., 2008; Billings and van Rossum, 2009; Gilson and Fukai, 2011; Tetzlaff et al., 2013; Zenke et al., 2013), it suggests that additional timescales are necessary to properly combine short-term and long-term…”
A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses (2015, background): “…account for all of the phenomenology observed neurophysiological experiments (Lisman and Spruston, 2010), have poor memory retention performance (Billings and van Rossum, 2009), and require additional mechanisms to learn both spike-time correlations and mean ﬁring rates in the input patterns…”