The understanding of neuronal function under the action of a certain stimulus can be facilitated using techniques to distinguish the potential action from different neurons. Thus, from simultaneous recording of multip...
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ISBN:
(纸本)9780819486578
The understanding of neuronal function under the action of a certain stimulus can be facilitated using techniques to distinguish the potential action from different neurons. Thus, from simultaneous recording of multiple neurons one can determine the firing patterns of each of them. Usually these techniques are implemented in three stages. From raw electrical potentials recorded using an intracranial electrode, spikes are detected, then parameterized and finally sorted, attributing every single spike observed to a particular neuron. Recently, it was proposed an on-line sorting method based on the noise level. Nevertheless, sorting is done directly based on the raw samples. In this paper we introduce an alternative way using the modified leastsquaresalgorithm based on the priori error with error feedback to parameterize the raw signals before classification. Preliminary simulations results show that using parameters provides performance near to results where the sorting is done directly based on the raw samples.
An error feedback LSL algorithm is presented which has a reduced computational complexity and makes use of both a priori and a posteriori prediction errors. A simple numerical convention is proposed to assure numerica...
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An error feedback LSL algorithm is presented which has a reduced computational complexity and makes use of both a priori and a posteriori prediction errors. A simple numerical convention is proposed to assure numerical stability for poor persistent excitation. Simulation results illustrate that divisions can be implemented with reduced wordlength without leading to a significant decrease in the overall accuracy even in floating-point implementations. This fact allows the use of lookup tables for implementing divisions. Moreover, the proposed algorithm has an inherent parallelism that can be advantageously exploited for fast implementations.
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