Biomedical subjects

Priscilla E Greenwood

Publications and source records attributed to Priscilla E Greenwood.

3 recordsLinked to original sources

PubMed2005

Optimum signal in a simple neuronal model with signal-dependent noise.

How does the information about a signal in neural threshold crossings depend on the noise acting upon it? Two models are explored, a binary McCulloch and Pitts (threshold exceedance) model and a model of waiting time to exceedance--a discrete-time version of interspike intervals. If noise grows linearly with the signal, we find the best identification of the signal in terms of the Fisher information is for signals that do not reach the threshold in the absence of noise. Identification attains the same precision under weak and strong signals, but the coding range decreases at both extremes of signal level. We compare the results obtained for Fisher information with those using related first and second moment measures. The maximum obtainable information is plotted as a function of the ratio of noise to signal.

Action Potentials↗

PubMed2005

Optimal signal estimation in neuronal models.

We study optimal estimation of a signal in parametric neuronal models on the basis of interspike interval data. Fisher information is the inverse asymptotic variance of the best estimator. Its dependence on the parameter value indicates accuracy of estimation. Our models assume that the input signal is estimated from neuronal output interspike interval data where the frequency transfer function is sigmoidal. If the coefficient of variation of the interspike interval is constant with respect to the signal, the Fisher information is unimodal, and its maximum for the most estimable signal can be found. We obtain a general result and compare the signal producing maximal Fisher information with the inflection point of the sigmoidal transfer function in several basic neuronal models.

Action Potentials↗

PubMed2004

Soft threshold stochastic resonance.

Soft thresholds are ubiquitous in living organisms, in particular in mechanisms of neurons and of neural networks such as sensory systems. Which soft threshold functions produce (threshold) stochastic resonance remains a question. The answer may depend on the information measure used. We argue that Fisher information about signal parameters is an attractive measure of information transmission across soft thresholds. We illustrate how the pattern of information changes as a signal moves across a soft threshold. For some signals this pattern is much the same whether Fisher information or signal-to-noise ratio is used as a measure of information transmission. Noninvertibility of the threshold function, rather than its steepness, is important for stochastic resonance measured by Fisher information.

Journal Article↗