The neuron can be called the main cell of a nervous system that can transmit messages from one neuron to another neuron or another cell through electrical signals. In neuromorphic engineering, the hardware realization and simulation of these neurons are crucial. To accomplish a proper digital implementation (i.e. reducing hardware resources and increasing speed and accuracy) of three important neuron models including Hodgkin-Huxley, Morris-Lecar, and Izhikevich, this study proposes a set of multiplier-less mathematical equations based on converting nonlinear functions to$2^{x}$functions. Then optimizes the proposed equations based on reducing the number of different$2^{x}$terms. The suggested model can accurately recreate the behavioral characteristics of the original neuron models. The suggested model was synthesized and implemented on the Zynq XC7Z010 (3CLG400) reconfigurable board (FPGA) to validate the mathematical simulation findings. The results of hardware synthesizing and implementations of the proposed model show that different biological behavior can be replicated with greater efficiency and at substantially reduced implementation costs. This method (implemented on the zynq board) can raise the frequency of the proposed models at least by up to 3.5 times that of the original model and reduce power consumption between 20% and 60% for different proposed models. Also, due to the reduction of hardware resources in the proposed model, it is possible to implement a much larger number of neurons (between 4 and 12 times) relative to the original model on a single zynq board.

In recent years, hardware modeling for various parts of the body’s sensitive organs, including the brain and nervous system, heart and eyes, has been considered for the treatment of diseases and rehabilitation, as well as for moving towards the construction of artificial prostheses. The retina is a thin layer that is the innermost layer of the human eye. In this paper, low-cost hardware implementation for retinal cone cells is performed. Existing mathematical models for implementing the behavior of these cells include a series of nonlinear functions that, if implemented directly, would require a large amount of hardware and, in addition, would not have the desired speed. The proposed model uses multi-linear functions to approximate the nonlinear terms and eliminate the multiplication expressions. The simulation results show that the proposed model tracks the behavior of the original model with high precision. There is also a good match between the main model and the proposed model in terms of dynamic behaviors. The results of hardware implementation using the virtex5 XC5VLX20T (2FF323) reconfigurable board (FPGA) show that the proposed model is fully valid and has a lower hardware volume as well as a 4 times higher frequency, and 22% less power consumption than the original model.

Neurons are the basic blocks in the Central Nervous System (CNS). Simulation and hardware realization of these blocks are vital in neuromorphic engineering. This paper presents a set of multiplierless mathematical equations based on 2Xterms to achieve a low-cost, high-speed, and high-accuracy digital implementation of Hodgkin-Huxley (HH) neuron model. The HH model is the most complicated and high-accuracy among the mathematical neuron models. The proposed model can reproduce spiking behaviors of the original HH model with high precision. To validate the mathematical simulation results, the proposed model has been synthesized and implemented on Field-Programmable Gate Array (FPGA) development board. Hardware synthesis and physical implementations reveal that the biological behavior of different spiking patterns can be reproduced with higher performance and significantly lower implementation costs compared with the original HH model. Also, in this approach the maximum frequency of 200 MHz is achievable which is valuable in comparison with other similar works.

In this paper, we describe the impact of square and non-square implementations of Current Source Arrays (CSAs) on the integral non-linearity (INL) of the thermometer-decoded current steering Digital to Analog Converters (DACs). The characteristics of several well-known switching sequences have been modeled and simulated using MATLAB in presence of different gradient error profiles. The simulation results show a significant correlation between the efficiency and performance of the employed switching strategy and the physical dimensions of the CSA. Based on the analysis of the obtained results, a strategy is introduced which includes a recursive approach towards the achievement of an optimum switching sequence for distributed current source-based DAC topologies.

This paper proposes tracking forecast memories (TFMs) as a novel method for implementing re-randomization and decorrelation of stochastic bit streams in stochastic channel decoders. We show that TFMs are able to achieve decoding performance similar to that of the previous methods in the literature (i.e., edge memories or EMs), but they exhibit much lower hardware complexity. TFMs significantly reduce the area requirements of ASIC implementations of stochastic decoders.

This paper describes a sub-sampling delta modulator operating at giga Hertz range to capture radio frequency signals. Down-conversion to low-IF is achieved by sub-sampling with a 1-bit quantizer. It presents higher bandwidth and SNR than those of the state-of-the-art sub-sampling modulators. Input carrier frequency can be followed over a wide range by controlling the sampling rate. Center frequency of the band-pass filters, which is placed at IF, is independent of input carrier frequency. A SNR higher than 55 dB is expected for a 2 MHz bandwidth signal modulated at 2-GHz frequency when the sampling rate is set to 990 mega samples per second

MOTIVATION: Elucidating the molecular taxonomy of cancers and finding biological and clinical markers from microarray experiments is problematic due to the large number of variables being measured. Feature selection methods that can identify relevant classifiers or that can remove likely false positives prior to supervised analysis are therefore desirable. RESULTS: We present a novel feature selection procedure based on a mixture model and a non-gaussianity measure of a gene's expression profile. The method can be used to find genes that define either small outlier subgroups or major subdivisions, depending on the sign of kurtosis. The method can also be used as a filtering step, prior to supervised analysis, in order to reduce the false discovery rate. We validate our methodology using six independent datasets by rediscovering major classifiers in ER negative and ER positive breast cancer and in prostate cancer. Furthermore, our method finds two novel subtypes within the basal subgroup of ER negative breast tumours, associated with apoptotic and immune response functions respectively, and with statistically different clinical outcome. AVAILABILITY: An R-function pack that implements the methods used here has been added to vabayelMix, available from (www.cran.r-project.org). CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.