Data Modelling for Analysis of Adaptive Changes in Fly Photoreceptors
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
Population encoding with Hodgkin-Huxley neurons
IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
System identification of Drosophila olfactory sensory neurons
Journal of Computational Neuroscience
Functional identification of spike-processing neural circuits
Neural Computation
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We present a formal methodology for identifying a channel in a system consisting of a communication channel in cascade with an asynchronous sampler. The channel ismodeled as amultidimensional filter, whilemodels of asynchronous samplers are taken from neuroscience and communications and include integrate-and-fire neurons, asynchronous sigma/delta modulators and general oscillators in cascade with zero-crossing detectors. We devise channel identification algorithms that recover a projection of the filter(s) onto a space of input signals loss-free for both scalar and vector-valued test signals. The test signals are modeled as elements of a reproducing kernel Hilbert space (RKHS) with a Dirichlet kernel. Under appropriate limiting conditions on the bandwidth and the order of the test signal space, the filter projection converges to the impulse response of the filter. We show that our results hold for a wide class of RKHSs, including the space of finite-energy bandlimited signals. We also extend our channel identification results to noisy circuits.