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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. © 2002 Elsevier Science Ltd. All rights reserved. 15 7 891 908 Cited By :27

Nerve ending particles isolated from prenatal and postnatal rats by means of density-gradient centrifugation in a ficoll medium actively incorporated [32P]-orthophosphate into nucleotides, phosphoproteins and phospholipids. This process requires Mg2+; is dependent on malate plus pyruvate but not glucose, and is inhibited by dinitrophenol, gramicidin, oligomycin, azide, p-chloromercuribenzoate, CaCl2 but not iodoacetate. It was concluded that the metabolic energy at the synapse is derived largely from the mitochondria of the synaptic complex rather than from its cytoplasm. Thin-layer chromatography and paper electrophoresis revealed that the metabolically active phospholipids, namely phosphatidic acid, phosphatidyl inositol and the polyphosphoinositides which were found to be tightly bound to the phosphoprotein fraction, contained more than 90% of the total radioactivity but constituted less than 9% of the total phospholipids. The highest phosphoprotein and phospholipid content of the nerve endings (expressed in μmoles P/mg nerve ending protein) as well as maximal 32P-incorporation occurred just prior to and continued into the stage when functional changes are formed. It is suggested that an increase in the amount of enzymatic activity coupled with an increase in the permeability of the synaptosomal membrane to inorganic phosphate and other metabolites could trigger the rapid morpho-biochemical and functional changes observed in rat brain during this period of development. A simple density-gradient withdrawing device for the isolation of the submitochondrial particles after density-gradient centrifugation of the conventional mitochondrial fraction was described.