Guy Billings PhD

Guy Billings


Publications:
2009
  State Based Model of Long-Term Potentiation and Synaptic Tagging and Capture
Barrett, AB, Billings, G, Morris, RGM & van Rossum, MCW 2009, 'State Based Model of Long-Term Potentiation and Synaptic Tagging and Capture' PLoS Computational Biology, vol 5, no. 1, e1000259, pp. 1-12. DOI: 10.1371/journal.pcbi.1000259
Recent data indicate that plasticity protocols have not only synapse-specific but also more widespread effects. In particular, in synaptic tagging and capture (STC), tagged synapses can capture plasticity-related proteins, synthesized in response to strong stimulation of other synapses. This leads to long-lasting modification of only weakly stimulated synapses. Here we present a biophysical model of synaptic plasticity in the hippocampus that incorporates several key results from experiments on STC. The model specifies a set of physical states in which a synapse can exist, together with transition rates that are affected by high-and low-frequency stimulation protocols. In contrast to most standard plasticity models, the model exhibits both early-and late-phase LTP/D, de-potentiation, and STC. As such, it provides a useful starting point for further theoretical work on the role of STC in learning and memory.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Barrett, Adam B, Billings, Guy, Morris, Richard G M & van Rossum, Mark C W.
Keywords: (, , . )
Number of pages: 12
Pages: 1-12
Publication Date: Jan 2009
Publication Information
Category: Article
Journal: PLoS Computational Biology
Volume: 5
Issue number: 1
ISSN: 1553-734X
Original Language: English
DOIs: 10.1371/journal.pcbi.1000259
  Memory Retention and Spike-Timing-Dependent Plasticity
Billings, G & van Rossum, MCW 2009, 'Memory Retention and Spike-Timing-Dependent Plasticity' Journal of Neurophysiology, vol 101, no. 6, pp. 2775-2788. DOI: 10.1152/jn.91007.2008
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.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Billings, Guy & van Rossum, M. C. W..
Keywords: (, , . )
Number of pages: 14
Pages: 2775-2788
Publication Date: Jun 2009
Publication Information
Category: Article
Journal: Journal of Neurophysiology
Volume: 101
Issue number: 6
ISSN: 0022-3077
Original Language: English
DOIs: 10.1152/jn.91007.2008
2007
  A Biophysical model of long-term potentiation and synaptic tagging
Barrett, A, Billings, G, Morris, RGM & van Rossum, MCW 2007, 'A Biophysical model of long-term potentiation and synaptic tagging' Society for Neuroscience Annual Meeting, 2007, San Diego, California, United States, 3/11/07 - 7/11/07, .
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Barrett, Adam, Billings, Guy, Morris, Richard G M & van Rossum, Mark C. W..
Publication Date: 2007
Publication Information
Category: Poster
Original Language: English

Projects:
Modelling synaptic plasticity and the contribution of noise to early visual processing (PhD)