Abstract.Starting from the idea that neural group activity as such is unlikely to be immediately relevant for neural synchronization, we investigate mechanisms that act at the level of individual nerve impulses (spikes). Hence, we consider populations of formal spike-emitting 'leaky integrate and fire' neurons instead of networks built from non-spiking oscillators. After outlining the principle of synchronization for basic forms of recurrent impulse coupling by using a pair of simplified formal neurons, we show that local lateral inhibition results in robust impulse synchronization in networks with nonvanishing transmission delays.

We present rules for the unsupervised learning of coincidence between excitatory postsynaptic potentials (EPSPs) by the adjustment of postsynaptic delays between the transmitter binding and the opening of ion channels. Starting from a gradient descent scheme, we develop a robust and more biological threshold rule by which EPSPs from different synapses can be gradually pulled into coincidence. The synaptic delay changes are determined from the summed potential--at the site where the coincidence is to be established--and from postulated synaptic learning functions that accompany the individual EPSPs. According to our scheme, templates for the detection of spatiotemporal patterns of synaptic activation can be learned, which is demonstrated by computer simulation. Finally, we discuss possible relations to biological mechanisms.

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The visual estimation of object velocity in systems of tuned bilocal detector units (simplified Hassenstein-Reichardt detectors) is investigated. The units contain delay filters of an arbitrary low-pass characteristic. Arrays of such detector units with identical delay filters are assumed to cover the plane of analysis. The global evaluation of the output signals of suitably arranged detector units is exemplified by the analysis of frontoparallel translations of rigid objects. The correlative method permits the estimation of the instantaneous object velocity, independently of object form. The time course of the resulting estimate is shown to be the convolution of the true velocity profile with a time-invariant kernel that depends solely on the impulse response of the delay filters and thus characterizes the analyzer system. The mathematical analysis of the processing principle is illustrated by considering idealized detector systems. The response of correlative motion analyzers to compound motion and to motion of nonrigid objects is discussed.

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