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An unresolved paradox exists in auditory and electrosensory neural systems {Carr93,Heiligenberg91}: they encode behaviourally relevant signals in the range of a few microseconds with neurons that are at least one order of magnitude slower. We take the barn ...
An integral equation describing the time evolution of the population activity in a homogeneous pool of spiking neurons of the integrate-and-fire type is discussed. It is analytically shown that transients from a state of incoherent firing can be immediate. ...
A model of an associative network of spiking neurons with stationary states, globally locked oscillations, and weakly locked oscillatory states is presented and analyzed. The network is close to biology in the following sense. First, the neurons spike and ...
This thesis gives an overview of my work over the last four years on the development of analogue electronic building blocks for the auditory pathway, and their application to some models of processing in the auditory brainstem. The anatomy and physiology o ...
The domain of artificial neural networks has evolved rapidly during the last decade, and many research groups are presently working on new neuronal algorithms and investigating their potential for technological applications. The idea to use biologically in ...
Progress in the instrumentation and, in particular, in the photon sources makes it possible to implement a number of established X-ray spectroscopies in a high-lateral-resolution mode. We discuss the general trends of this field, and then we present a deta ...
A correlation-based (Hebbian'') learning rule at the spike level is formulated, mathematically analyzed, and compared with learning in a firing-rate description. As for spike coding, we take advantage of a learning window'' that describes the effect of ...
A good approximation to the integrate-and-fire model with diffusive noise can be obtained using a noisy threshold model. This allows the response of a population of noisy neurons to a current transient to be described using a linear filter. Here we apply t ...