3 research outputs found
Feed-forward and recurrent inhibition for compressing and classifying high dynamic range biosignals in spiking neural network architectures
Neuromorphic processors that implement Spiking Neural Networks (SNNs) using
mixed-signal analog/digital circuits represent a promising technology for
closed-loop real-time processing of biosignals. As in biology, to minimize
power consumption, the silicon neurons' circuits are configured to fire with a
limited dynamic range and with maximum firing rates restricted to a few tens or
hundreds of Herz.
However, biosignals can have a very large dynamic range, so encoding them
into spikes without saturating the neuron outputs represents an open challenge.
In this work, we present a biologically-inspired strategy for compressing
this high-dynamic range in SNN architectures, using three adaptation mechanisms
ubiquitous in the brain: spike-frequency adaptation at the single neuron level,
feed-forward inhibitory connections from neurons belonging to the input layer,
and Excitatory-Inhibitory (E-I) balance via recurrent inhibition among neurons
in the output layer.
We apply this strategy to input biosignals encoded using both an asynchronous
delta modulation method and an energy-based pulse-frequency modulation method.
We validate this approach in silico, simulating a simple network applied to a
gesture classification task from surface EMG recordings.Comment: 5 pages, 7 figures, to be published in IEEE BioCAS 2023 Proceeding
ANALYSIS OF NEURAL ACTIVITY OF THE HUMAN BASAL GANGLIA IN DYSTONIA: A REVIEW
Deep brain stimulation of the globus pallidus internus is an efective symptomatic treatment for pharmacoresistant dystonic syndromes, where pathophysiological mechanisms of action are not yet fully understood. The aim of this review article is to provide an overview of the state-of-the-art approaches for processing of microelectrode recordings in dystonia; in order to define biomarkers to identify patients who will benefit from the clinical deep brain stimulation. For this purpose, the essential elements of microelectrode processing are examined. Next, we investigate a real example of spike sorting processing in this field. Herein, we describe baseline elements of microrecordings processing including data collection, preprocessing phase, features computation, spike detection and sorting and finally, advanced spike train data analysis. This study will help readers acquire the necessary information about these elements and their associated techniques. Thus, this study is supposed to assist during identification and proposal of interesting clinical hypotheses in the field of single unit neuronal recordings in dystonia
Neural activity classification with machine learning models trained on interspike interval series data
The flow of information through the brain is reflected by the activity
patterns of neural cells. Indeed, these firing patterns are widely used as
input data to predictive models that relate stimuli and animal behavior to the
activity of a population of neurons. However, relatively little attention was
paid to single neuron spike trains as predictors of cell or network properties
in the brain. In this work, we introduce an approach to neuronal spike train
data mining which enables effective classification and clustering of neuron
types and network activity states based on single-cell spiking patterns. This
approach is centered around applying state-of-the-art time series
classification/clustering methods to sequences of interspike intervals recorded
from single neurons. We demonstrate good performance of these methods in tasks
involving classification of neuron type (e.g. excitatory vs. inhibitory cells)
and/or neural circuit activity state (e.g. awake vs. REM sleep vs. nonREM sleep
states) on an open-access cortical spiking activity dataset