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A lossy compression paradigm for sensory neural coding

Title
A lossy compression paradigm for sensory neural coding
Funding
ARC | Discovery Projects
Contract (GA) number
DP0770747
Start Date
2007/01/01
End Date
2009/12/31
Open Access mandate
no
Organizations
-
More information
http://purl.org/au-research/grants/arc/DP0770747

 

  • What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology

    McDonnell, Mark D.; Abbott, Derek (2009)
    Projects: ARC | A lossy compression paradigm for sensory neural coding (DP0770747)
    Stochastic resonance is said to be observed when increases in levels of unpredictable fluctuations--e.g., random noise--cause an increase in a metric of the quality of signal transmission or detection performance, rather than a decrease. This counterintuitive effect relies on system nonlinearities and on some parameter ranges being "suboptimal". Stochastic resonance has been observed, quantified, and described in a plethora of physical and biological systems, including neurons. Being a topic ...

    Signal acquisition via polarization modulation in single photon sources

    McDonnell, Mark D.; Flitney, Adrian P. (2009)
    Projects: ARC | A lossy compression paradigm for sensory neural coding (DP0770747)
    A simple model system is introduced for demonstrating how a single photon source might be used to transduce classical analog information. The theoretical scheme results in measurements of analog source samples that are (i) quantized in the sense of analog-to-digital conversion and (ii) corrupted by random noise that is solely due to the quantum uncertainty in detecting the polarization state of each photon. This noise is unavoidable if more than one bit per sample is to be transmitted, and we...

    Stochastic Pooling Networks

    McDonnell, Mark D.; Amblard, Pierre-Olivier; Stocks, Nigel G. (2009)
    Projects: ARC | A lossy compression paradigm for sensory neural coding (DP0770747)
    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 randomness -- interact in surprising ways. Our approach to analyzing SPNs is information theoretic. We define an SPN as a network with multiple nodes that each produce noisy and compressed measurements of the same information. An SPN must combine all these measurements into a single further compressed network output, in a w...

    Neural Population Coding is Optimized by Discrete Tuning Curves

    Nikitin, Alexander P.; Stocks, Nigel G.; Morse, Robert P.; McDonnell, Mark D. (2008)
    Projects: ARC | A lossy compression paradigm for sensory neural coding (DP0770747)
    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 discrete structure that results in a quantization of the input signal. The number of quantization levels undergoes a hierarchy of phase transitions as the length of the coding window is varied. We postulate, using the mammalian auditory system as an example, that the presence of a subpopulation structure within a neur...

    Maximally Informative Stimuli and Tuning Curves for Sigmoidal Rate-Coding Neurons and Populations

    McDonnell, Mark D.; Stocks, Nigel G. (2008)
    Projects: ARC | A lossy compression paradigm for sensory neural coding (DP0770747)
    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 nonlinear function of the cumulative distribution function of the stimulus and depends on the mean-variance relationship of the neural system. The derivation is based on a known relationship between Shannon's mutual information and Fisher information, and the optimality of Jeffrey's prior. It relies on the existence of clo...

    Optimal sigmoidal tuning curves for intensity encoding sensory neurons with quasi-Poisson variability

    McDonnell, Mark D; Stocks, Nigel G (2008)
    Projects: ARC | A lossy compression paradigm for sensory neural coding (DP0770747)
    Rate-coding neurons are often characterized by their tuning\ud curve, that is, the average firing rate, T(x), as a function\ud of stimulus intensity, x. However the substantial natural\ud variability in firing rate that often occurs for a fixed stimulus\ud provides a limitation on the fidelity of firing rate\ud encoding of stimuli. Consequently, stimulus-dependent\ud variance in firing rate, V(x), is crucial in studies of tuning\ud curve optimality. Information theory can be used to quantify\...
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