A 64-channel inductively-powered neural recording sensor array

This paper reports a 64-channel inductively powered neural recording sensor array. Neural signals are acquired, filtered, digitized and compressed in the channels. Additionally, each channel implements a local auto-calibration mechanism which configures the transfer characteristics of the recording site. The system has two operation modes; in one case the information captured by the channels is sent as uncompressed raw data; in the other, feature vectors extracted from the detected neural spikes are transmitted. Data streams coming from the channels are serialized by an embedded digital processor and transferred to the outside by means of the same inductive link used for powering the system. Simulation results show that the power consumption of the complete system is 377μW.Ministerio de Ciencia e Innovación TEC2009-0844

The McShane integral in weakly compactly generated spaces

Di Piazza and Preiss asked whether every Pettis integrable function defined on [0,1] and taking values in a weakly compactly generated Banach space is McShane integrable. In this paper we answer this question in the negative.Comment: Revised version. To appear in Journal of Functional Analysi

A power efficient neural spike recording channel with data bandwidth reduction

This paper presents a mixed-signal neural spike recording channel which features, as an added value, a simple and low-power data compression mechanism. The channel uses a band-limited differential low noise amplifier and a binary search data converter, together with other digital and analog blocks for control, programming and spike characterization. The channel offers a self-calibration operation mode and it can be configured both for signal tracking (to raw digitize the acquired neural waveform) and feature extraction (to build a first-order PWL approximation of the spikes). The prototype has been fabricated in a standard CMOS 0.13μm and occupies 400μm×400μm. The overall power consumption of the channel during signal tracking is 2.8μW and increases to 3.0μW average when the feature extraction operation mode is programmed.Ministerio de Ciencia e Innovación TEC2009-08447Junta de Andalucía TIC-0281

Rational characteristic functions and markov chains

Abstract 1 We investigate in this paper how to estimate the density function of a random variable using a parametric ARMA model for its characteristic function. The choice of this model is motivated by the fact that this type of density characterizes the duration of staying at an N-states Markov chain, but the approach is general enough to be applied to many practical problems. Both ML and moment-based linear estimates are derived, the former being based on the optimization of a highly non-linear function. 1.Peer ReviewedPostprint (published version