Biomedical subjects

Guido Bugmann

Publications and source records attributed to Guido Bugmann.

3 recordsLinked to original sources

PubMed2002

A spiking neuron model: applications and learning.

This paper presents a biologically inspired, hardware-realisable spiking neuron model, which we call the Temporal Noisy-Leaky Integrator (TNLI). The dynamic applications of the model as well as its applications in Computational Neuroscience are demonstrated and a learning algorithm based on postsynaptic delays is proposed. The TNLI incorporates temporal dynamics at the neuron level by modelling both the temporal summation of dendritic postsynaptic currents which have controlled delay and duration and the decay of the somatic potential due to its membrane leak. Moreover, the TNLI models the stochastic neurotransmitter release by real neuron synapses (with probabilistic RAMs at each input) and the firing times including the refractory period and action potential repolarisation. The temporal features of the TNLI make it suitable for use in dynamic time-dependent tasks like its application as a motion and velocity detector system presented in this paper. This is done by modelling the experimental velocity selectivity curve of the motion sensitive H1 neuron of the visual system of the fly. This application of the TNLI indicates its potential applications in artificial vision systems for robots. It is also demonstrated that Hebbian-based learning can be applied in the TNLI for postsynaptic delay training based on coincidence detection, in such a way that an arbitrary temporal pattern can be detected and recognised. The paper also demonstrates that the TNLI can be used to control the firing variability through inhibition; with 80% inhibition to concurrent excitation, firing at high rates is nearly consistent with a Poisson-type firing variability observed in cortical neurons. It is also shown with the TNLI, that the gain of the neuron (slope of its transfer function) can be controlled by the balance between inhibition and excitation, the gain being a decreasing function of the proportion of inhibitory inputs. Finally, in the case of perfect balance between inhibition and excitation, i.e. where the average input current is zero, the neuron can still fire as a result of membrane potential fluctuations. The firing rate is then determined by the average input firing rate. Overall this work illustrates how a hardware-realisable neuron model can capitalise on the unique computational capabilities of biological neurons.

Analog-Digital Conversion↗

PubMedDate not supplied

A model of visual backward masking.

When two successive stimuli are presented within 0-200 ms intervals, the recognition of the first stimulus (the target) can be impaired by the second (the mask). This backward masking phenomenon has a form called metacontrast masking where the target and the mask are in close spatial proximity but not overlapping. In that case, the masking effect is strongest for interval of 60-100 ms. To understand this behaviour, activity propagation in a feedforward network of leaky integrate and fire neurons is investigated. It is found that, if neurons have a selectivity similar to that of V1 simple cells, activity decays layer after layer and ceases to propagate. To combat this, a local amplification mechanism is included in the model, using excitatory lateral connections, which turn out to support prolonged self-sustained activity. Masking is assumed to arise from local competition between representations recruited by the target and the mask. This tends to interrupt sustained firing, while prolonged retinal input tends to re-initiate it. Thus, masking causes a maximal reduction of the duration of the cortical response to the target towards the end of the retinal response. This duration exhibits the typical U-shape of the masking curve. In this model, masking does not alter the propagation of the onset of the response to the target, thus preserving response reaction times and enabling unconscious priming phenomena.

Models, Neurological↗

PubMedDate not supplied

Synaptic depression increases the selectivity of a neuron to its preferred pattern and binarizes the neural code.

The preferred pattern of a neuron is defined here by the set of features detected by its excitatory inputs. It is shown that the Leaky integrate-and-fire (LIF) model of a neuron has a poor selectivity to its preferred pattern. Its response is determined by the total current injected by input spike trains. Thus, a few inputs with a high activity (an incomplete pattern) can elicit the same response as many inputs (a complete pattern) with a weak activity. A theoretical model of depressing synapse with linear recovery is proposed which eliminates this problem. Using this model, the time-averaged current injected in the soma by a spike train becomes independent on its frequency. The neural code thus becomes binary, and the response strength of the target neuron depends only on the number of active inputs. Simulations show that a biological model of strong synaptic depression has effects similar to those of the ideal linear model. The best selectivity is obtained with long somatic decay time constants (>50 ms) and with depression recovery time constants larger or equal to the somatic decay time constant. Thus, by eliminating information carried in the input firing rate, a neuron can improve its pattern recognition performance.

Action Potentials↗