27,689 research outputs found
Filtering and thresholding the analytic signal envelope in order to improve peak and spike noise reduction in EEG signals
To remove peak and spike artifacts in biological time series has represented a hard challenge in the last decades. Several methods have been implemented mainly based on adaptive filtering in order to solve this problem. This work presents an algorithm for removing peak and spike artifacts based on a threshold built on the analytic signal envelope. The algorithm was tested on simulated and real EEG signals that contain peak and spike artifacts with random amplitude and frequency occurrence. The performance of the filter was compared with commonly used adaptive filters. Three indexes were used for testing the performance of the filters: Correlation coefficient, mean of coherence function, and rate of absolute error. All these indexes were calculated between filtered signal and original signal without noise. It was found that the new proposed filter was able to reduce the amplitude of peak and spike artifacts with > 0.85, C > 0.8, and RAE 1)
Restoration and enhancement of astronomical images using hybrid adaptive nonlinear complex diffusion-based filter
In this paper, a hybrid and adaptive nonlinear complex diffusion based technique is proposed for restoration and enhancement of astronomical images corrupted with additive noise due to diffractions limit, aberrations in telescope camera lens, atmospheric irregularities which are modelled by Gaussian functions. In addition to additive noise removal, the proposed filter is also capable of removing noisy stars and spike noises due to other numerous stars that may have surrounded an object in the astronomical image such as in nebula images. The proposed scheme is completely adaptive in nature in the sense that it estimates all the required filtering parameters from the observed image itself. The performance of the proposed hybrid scheme is compared with other image restoration techniques such as averaging filter, Gaussian filter, median filter, anisotropic diffusion based filter, local variance based adaptive anisotropic diffusion filter and also the adaptive and hybrid version of anisotropic diffusion filter similar to the proposed one in terms of average SNR and BSNR. The results obtained show the efficacy of the proposed scheme.Defence Science Journal, 2012, 62(6), pp.437-442, DOI:http://dx.doi.org/10.14429/dsj.62.129
Design of a silicon cochlea system with biologically faithful response
This paper presents the design and simulation results of a silicon cochlea system that has closely similar behavior as the real cochlea. A cochlea filter-bank based on the improved three-stage filter cascade structure is used to model the frequency decomposition function of the basilar membrane; a filter tuning block is designed to model the adaptive response of the cochlea; besides, an asynchronous event-triggered spike codec is employed as the system interface with bank-end spiking neural networks. As shown in the simulation results, the system has biologically faithful frequency response, impulse response, and active adaptation behavior; also the system outputs multiple
band-pass channels of spikes from which the original sound input can be recovered. The proposed silicon cochlea is feasible for analog VLSI implementation so that it not only emulates the way that sounds are preprocessed in human ears but also is able match the compact physical size of a real cochlea
Particle-filtering approaches for nonlinear Bayesian decoding of neuronal spike trains
The number of neurons that can be simultaneously recorded doubles every seven
years. This ever increasing number of recorded neurons opens up the possibility
to address new questions and extract higher dimensional stimuli from the
recordings. Modeling neural spike trains as point processes, this task of
extracting dynamical signals from spike trains is commonly set in the context
of nonlinear filtering theory. Particle filter methods relying on importance
weights are generic algorithms that solve the filtering task numerically, but
exhibit a serious drawback when the problem dimensionality is high: they are
known to suffer from the 'curse of dimensionality' (COD), i.e. the number of
particles required for a certain performance scales exponentially with the
observable dimensions. Here, we first briefly review the theory on filtering
with point process observations in continuous time. Based on this theory, we
investigate both analytically and numerically the reason for the COD of
weighted particle filtering approaches: Similarly to particle filtering with
continuous-time observations, the COD with point-process observations is due to
the decay of effective number of particles, an effect that is stronger when the
number of observable dimensions increases. Given the success of unweighted
particle filtering approaches in overcoming the COD for continuous- time
observations, we introduce an unweighted particle filter for point-process
observations, the spike-based Neural Particle Filter (sNPF), and show that it
exhibits a similar favorable scaling as the number of dimensions grows.
Further, we derive rules for the parameters of the sNPF from a maximum
likelihood approach learning. We finally employ a simple decoding task to
illustrate the capabilities of the sNPF and to highlight one possible future
application of our inference and learning algorithm
Adaptive Filtering Enhances Information Transmission in Visual Cortex
Sensory neuroscience seeks to understand how the brain encodes natural
environments. However, neural coding has largely been studied using simplified
stimuli. In order to assess whether the brain's coding strategy depend on the
stimulus ensemble, we apply a new information-theoretic method that allows
unbiased calculation of neural filters (receptive fields) from responses to
natural scenes or other complex signals with strong multipoint correlations. In
the cat primary visual cortex we compare responses to natural inputs with those
to noise inputs matched for luminance and contrast. We find that neural filters
adaptively change with the input ensemble so as to increase the information
carried by the neural response about the filtered stimulus. Adaptation affects
the spatial frequency composition of the filter, enhancing sensitivity to
under-represented frequencies in agreement with optimal encoding arguments.
Adaptation occurs over 40 s to many minutes, longer than most previously
reported forms of adaptation.Comment: 20 pages, 11 figures, includes supplementary informatio
Intrinsic gain modulation and adaptive neural coding
In many cases, the computation of a neural system can be reduced to a
receptive field, or a set of linear filters, and a thresholding function, or
gain curve, which determines the firing probability; this is known as a
linear/nonlinear model. In some forms of sensory adaptation, these linear
filters and gain curve adjust very rapidly to changes in the variance of a
randomly varying driving input. An apparently similar but previously unrelated
issue is the observation of gain control by background noise in cortical
neurons: the slope of the firing rate vs current (f-I) curve changes with the
variance of background random input. Here, we show a direct correspondence
between these two observations by relating variance-dependent changes in the
gain of f-I curves to characteristics of the changing empirical
linear/nonlinear model obtained by sampling. In the case that the underlying
system is fixed, we derive relationships relating the change of the gain with
respect to both mean and variance with the receptive fields derived from
reverse correlation on a white noise stimulus. Using two conductance-based
model neurons that display distinct gain modulation properties through a simple
change in parameters, we show that coding properties of both these models
quantitatively satisfy the predicted relationships. Our results describe how
both variance-dependent gain modulation and adaptive neural computation result
from intrinsic nonlinearity.Comment: 24 pages, 4 figures, 1 supporting informatio
Observations on adaptive vector filters for noise reduction in color images
In a series of papers, Plataniotis et al. proposed a number of filters for noise reduction in color images where the noise type is unknown. In this letter, those filters with a unified notation are summarized, and it is shown that they are essentially variants of the same filtering procedure. It is also shown that the class of adaptive vector filters can be considered as interpolants between the arithmetic mean filter and the vector median filter. Results are presented of numerical computations with the filters on test images corrupted with noise. It is found that the adaptive vector filters perform well with general applicability
Efficient Computation in Adaptive Artificial Spiking Neural Networks
Artificial Neural Networks (ANNs) are bio-inspired models of neural
computation that have proven highly effective. Still, ANNs lack a natural
notion of time, and neural units in ANNs exchange analog values in a
frame-based manner, a computationally and energetically inefficient form of
communication. This contrasts sharply with biological neurons that communicate
sparingly and efficiently using binary spikes. While artificial Spiking Neural
Networks (SNNs) can be constructed by replacing the units of an ANN with
spiking neurons, the current performance is far from that of deep ANNs on hard
benchmarks and these SNNs use much higher firing rates compared to their
biological counterparts, limiting their efficiency. Here we show how spiking
neurons that employ an efficient form of neural coding can be used to construct
SNNs that match high-performance ANNs and exceed state-of-the-art in SNNs on
important benchmarks, while requiring much lower average firing rates. For
this, we use spike-time coding based on the firing rate limiting adaptation
phenomenon observed in biological spiking neurons. This phenomenon can be
captured in adapting spiking neuron models, for which we derive the effective
transfer function. Neural units in ANNs trained with this transfer function can
be substituted directly with adaptive spiking neurons, and the resulting
Adaptive SNNs (AdSNNs) can carry out inference in deep neural networks using up
to an order of magnitude fewer spikes compared to previous SNNs. Adaptive
spike-time coding additionally allows for the dynamic control of neural coding
precision: we show how a simple model of arousal in AdSNNs further halves the
average required firing rate and this notion naturally extends to other forms
of attention. AdSNNs thus hold promise as a novel and efficient model for
neural computation that naturally fits to temporally continuous and
asynchronous applications
Adaptive Neural Coding Dependent on the Time-Varying Statistics of the Somatic Input Current
It is generally assumed that nerve cells optimize their performance to reflect the statistics of their input. Electronic circuit analogs of neurons require similar methods of self-optimization for stable and autonomous operation. We here describe and demonstrate a biologically plausible adaptive algorithm that enables a neuron to adapt the current threshold and the slope (or gain) of its current-frequency relationship to match the mean (or dc offset) and variance (or dynamic range or contrast) of the time-varying somatic input current. The adaptation algorithm estimates the somatic current signal from the spike train by way of the intracellular somatic calcium concentration, thereby continuously adjusting the neuronś firing dynamics. This principle is shown to work in an analog VLSI-designed silicon neuron
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