Guido Bugmann, Ph.D.
|Centre for Robotics and Neural Systems||University of Plymouth, Plymouth, England, United Kingdom|
Guido Bugmann was born in 1953. He studied Physics at the University of Geneva in Switzerland. In 1986 he completed his PhD on “Fabrication of photovoltaic solar cells with a-Si : H produced by anodic deposition in a DC plasma”. He then worked at the Swiss Federal Institute of Technology in Lausanne on the development of a measurement system using an ultra-sound beam and neural networks to measure the size of air bubbles in bacterial cultures. In 1989, he joined the Fundamental Research Laboratories of NEC in Japan and modelled the function of biological neurons in the visual system. In 1992 he joined Prof. John G. Taylor at King's College London to develop applications of the pRAM neuron model and develop a theory of visual latencies. In 1993 he joined the University of Plymouth (UK).
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|Koutsou A, Bugmann G, Christodoulou C. (2015) On learning time delays between the spikes from different input neurons in a biophysical model of a pyramidal neuron. Bio Systems. 136: 80-9|
|Bugmann G, Goslin J, Duchamp-Viret P. (2013) The speed of learning instructed stimulus-response association rules in human: experimental data and model. Brain Research. 1536: 2-15|
|Koutsou A, Christodoulou C, Bugmann G, et al. (2012) Distinguishing the causes of firing with the membrane potential slope. Neural Computation. 24: 2318-45|
|Bugmann G. (2011) Modeling fast stimulus-response association learning along the occipito-parieto-frontal pathway following rule instructions. Brain Research. 1434: 73-89|
|Bugmann G. (2006) Determination of the fraction of active inputs required by a neuron to fire. Bio Systems. 89: 154-9|
|Christodoulou C, Bugmann G, Clarkson TG. (2004) A spiking neuron model: applications and learning. Neural Networks : the Official Journal of the International Neural Network Society. 15: 891-908|
|Bugmann G. (2003) Synaptic depression increases the selectivity of a neuron to its preferred pattern and binarizes the neural code. Bio Systems. 67: 17-25|