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View article: Adaptive Central Frequencies Locally Competitive Algorithm for Speech
Adaptive Central Frequencies Locally Competitive Algorithm for Speech Open
Neuromorphic computing, inspired by nervous systems, revolutionizes information processing with its focus on efficiency and low power consumption. Using sparse coding, this paradigm enhances processing efficiency, which is crucial for edge…
View article: Maximizing information in neuron populations for neuromorphic spike encoding
Maximizing information in neuron populations for neuromorphic spike encoding Open
One of the ways neuromorphic applications emulate the processing performed by the brain is by using spikes as inputs instead of time-varying analog stimuli. Therefore, these time-varying stimuli have to be encoded into spikes, which can in…
View article: Maximizing Information in Neuron Populations for Neuromorphic Spike Encoding
Maximizing Information in Neuron Populations for Neuromorphic Spike Encoding Open
Neuromorphic applications emulate the processing performed by the brain by using spikes as inputs instead of time-varying analog stimuli. Therefore, these time-varying stimuli have to be encoded into spikes, which can induce important info…
View article: Efficient Sparse Coding with the Adaptive Locally Competitive Algorithm for Speech Classification
Efficient Sparse Coding with the Adaptive Locally Competitive Algorithm for Speech Classification Open
Researchers are exploring novel computational paradigms such as sparse coding and neuromorphic computing to bridge the efficiency gap between the human brain and conventional computers in complex tasks. A key area of focus is neuromorphic …
View article: Learning to see via epiretinal implant stimulation in silico with model-based deep reinforcement learning
Learning to see via epiretinal implant stimulation in silico with model-based deep reinforcement learning Open
Objective: Diseases such as age-related macular degeneration and retinitis pigmentosa cause the degradation of the photoreceptor layer. One approach to restore vision is to electrically stimulate the surviving retinal ganglion cells with a…
View article: Single Channel Speech Enhancement Using U-Net Spiking Neural Networks
Single Channel Speech Enhancement Using U-Net Spiking Neural Networks Open
Speech enhancement (SE) is crucial for reliable communication devices or robust speech recognition systems. Although conventional artificial neural networks (ANN) have demonstrated remarkable performance in SE, they require significant com…
View article: Efficiency metrics for auditory neuromorphic spike encoding techniques using information theory
Efficiency metrics for auditory neuromorphic spike encoding techniques using information theory Open
Spike encoding of sound consists in converting a sound waveform into spikes. It is of interest in many domains, including the development of audio-based spiking neural network applications, where it is the first and a crucial stage of proc…
View article: Evaluation of Neuromorphic Spike Encoding of Sound Using Information Theory
Evaluation of Neuromorphic Spike Encoding of Sound Using Information Theory Open
The problem of spike encoding of sound consists in transforming a sound waveform into spikes. It is of interest in many domains, including the development of audio-based spiking neural networks, where it is the first and most crucial stage…
View article: Adaptive Approach for Sparse Representations Using the Locally Competitive Algorithm for Audio
Adaptive Approach for Sparse Representations Using the Locally Competitive Algorithm for Audio Open
Gammachirp filterbank has been used to approximate the cochlea in sparse coding algorithms. An oriented grid search optimization was applied to adapt the gammachirp's parameters and improve the Matching Pursuit (MP) algorithm's sparsity al…
View article: Speaker-Independent Speech Enhancement with Brain Signals
Speaker-Independent Speech Enhancement with Brain Signals Open
Single-channel speech enhancement algorithms have seen great improvements over the past few years. Despite these improvements, they still lack the efficiency of the auditory system in extracting attended auditory information in the presenc…
View article: Speaker-Independent Speech Enhancement with Brain Signals
Speaker-Independent Speech Enhancement with Brain Signals Open
Single-channel speech enhancement algorithms have seen great improvements over the past few years. Despite these improvements, they still lack the efficiency of the auditory system in extracting attended auditory information in the presenc…
View article: Noise Invariance in Inferior Colliculus Neurons is Dependant on the Input Noisy Conditions
Noise Invariance in Inferior Colliculus Neurons is Dependant on the Input Noisy Conditions Open
The auditory system is extremely efficient to extract audio information in the presence of background noise. However, the neural mechanisms related to this efficiency is still greatly misunderstood, especially in the inferior colliculus (I…
View article: A Comparison of ECG Waveform Features for the Classification of Normal and Abnormal Heartbeats
A Comparison of ECG Waveform Features for the Classification of Normal and Abnormal Heartbeats Open
This work investigates technics that allow for the automatic classification of normal vs abnormal heartbeats with the goal of assisting general practitioners.In fact, many different ECG waveform features have been proposed over the years a…
View article: Classification of auditory stimuli from EEG signals with a regulated recurrent neural network reservoir
Classification of auditory stimuli from EEG signals with a regulated recurrent neural network reservoir Open
The use of electroencephalogram (EEG) as the main input signal in brain-machine interfaces has been widely proposed due to the non-invasive nature of the EEG. Here we are specifically interested in interfaces that extract information from …
View article: Classification of auditory stimuli from EEG signals with a regulated\n recurrent neural network reservoir
Classification of auditory stimuli from EEG signals with a regulated\n recurrent neural network reservoir Open
The use of electroencephalogram (EEG) as the main input signal in\nbrain-machine interfaces has been widely proposed due to the non-invasive\nnature of the EEG. Here we are specifically interested in interfaces that\nextract information fr…
View article: A Flexible Bio-Inspired Hierarchical Model for Analyzing Musical Timbre
A Flexible Bio-Inspired Hierarchical Model for Analyzing Musical Timbre Open
A flexible and multipurpose bio-inspired hierarchical model for analyzing musical timbre is presented in this paper. Inspired by findings in the fields of neuroscience, computational neuroscience, and psychoacoustics, not only does the mod…