77 citations · 406 across the 11 of their papers we have counts for
11 papers
Analysis of Spectrum Occupancy Using Machine Learning Algorithms
Freeha Azmat, Yunfei Chen, Nigel Stocks
In this paper, we analyze the spectrum occupancy using different machine learning techniques. Both supervised techniques (naive Bayesian classifier (NBC), decision trees (DT), supp…
Stochastic Pooling Networks
Mark D. McDonnell, Pierre-Olivier Amblard, Nigel G. Stocks
We introduce and define the concept of a stochastic pooling network (SPN), as a model for sensor systems where redundancy and two forms of 'noise' -- lossy compression and randomne…
Neural Population Coding is Optimized by Discrete Tuning Curves
Alexander P. Nikitin, Nigel G. Stocks, Robert P. Morse +1
The sigmoidal tuning curve that maximizes the mutual information for a Poisson neuron, or population of Poisson neurons, is obtained. The optimal tuning curve is found to have a di…
Maximally Informative Stimuli and Tuning Curves for Sigmoidal Rate-Coding Neurons and Populations
Mark D. McDonnell, Nigel G. Stocks
A general method for deriving maximally informative sigmoidal tuning curves for neural systems with small normalized variability is presented. The optimal tuning curve is a nonline…
Optimal stimulus and noise distributions for information transmission via suprathreshold stochastic resonance
Mark D. McDonnell, Nigel G. Stocks, Derek Abbott
Suprathreshold stochastic resonance (SSR) is a form of noise enhanced signal transmission that occurs in a parallel array of independently noisy identical threshold nonlinearities,…
Point singularities and suprathreshold stochastic resonance in optimal coding
Mark D. McDonnell, Nigel G. Stocks, Charles E. M. Pearce +1
Motivated by recent studies of population coding in theoretical neuroscience, we examine the optimality of a recently described form of stochastic resonance known as suprathreshold…