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A model of vental cochlear nucleus units based on first order intervals
No full textSignificant parallel processing occurs at the level of the ventral cochlear nucleus (VCN). It is believed that underlying this processing are discrete cellular response types which have a unique signature in their temporal adaptation patterns (TAPs). However, in reality it is difficult to define these TAPS as discrete. Here we demonstrate that a large sample (n=874) of VCN neurons can be accurately modelled as belonging to a continuous parameter space, even if they were not classifiable using traditional methods (Blackburn and Sachs, 1988).
We constructed first order interspike interval distributions (FOIDs) from the responses to 50ms best-frequency tone bursts. FOIDs were then plotted on a logarithmic time axis and fitted with an “extreme value distribution” (EVD). The EVD is a variation of the statistical “central limit theorem” that states that the distribution of the extreme values of arbitrary populations has a defined EVD. EVDs predict the probability of the occurrence of rare events independently of the underlying probability function. The validity of the fit was checked by a Kolgomorov-Smirnov test. EVDs fitted more units than other models (e.g. normal, gamma, weibull, exponential). Therefore the probability for spike generation can be described statistically without knowing the exact underlying mechanism. The two parameters of the fitted FOID (mean and variance) uniformly fill a well defined area of the parameter space. Single unit types recorded from the VCN occupy different, slightly overlapping areas of this parameter space. An “automatic classification” scheme (measuring the distance to population means) closely matches the classification schemes traditionally used by physiologists
Can first-order spike statistics from single cells in the cochlear nucleus be used for a Bayesian filter?
No full textThe magnitude of forward masking and the time course of its recovery as a function of unit type in the ventral cochlear nucleus
No full textUsing genetic algorithms to find the most effective stimulus for sensory Neurons.
No full textGenetic algorithms (GAs) can be used to find maxima in large search spaces in a relatively short period of time. We have used GAs in electrophysiological experiments to find the most effective stimulus (MES) for sensory neurons in the cochlear nucleus and inferior colliculus of anaesthetised guinea pigs. The MES is the stimulus that elicits the greatest number of spikes from a unit. We show that GAs provide an effective means of determining the best combination of up to four parameters for sinusoids with amplitude modulation. Using GAs, we have found tuning to modulation frequencies as a function of carrier frequency, sound level and temporal asymmetry. These results demonstrate the suitability of GAs in electrophysical experiments for estimating the position of the most effective stimulus in a specified parameter space. <br/
Using genetic algorithms to find the most effective stimulus for neurons in the auditory pathway.
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Adaptation in the ventral cochlear nucleus in the presence of constant-amplitude tone bursts is determined by ordered inter-spike interval statistics
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