2,358 research outputs found
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
Estimating the number of neurons in multi-neuronal spike trains
A common way of studying the relationship between neural activity and
behavior is through the analysis of neuronal spike trains that are recorded
using one or more electrodes implanted in the brain. Each spike train typically
contains spikes generated by multiple neurons. A natural question that arises
is "what is the number of neurons generating the spike train?"; This
article proposes a method-of-moments technique for estimating . This
technique estimates the noise nonparametrically using data from the silent
region of the spike train and it applies to isolated spikes with a possibly
small, but nonnegligible, presence of overlapping spikes. Conditions are
established in which the resulting estimator for is shown to be strongly
consistent. To gauge its finite sample performance, the technique is applied to
simulated spike trains as well as to actual neuronal spike train data.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS371 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Estimating the number of neurons in multi-neuronal spike trains
A common way of studying the relationship between neural activity and
behavior is through the analysis of neuronal spike trains that are recorded
using one or more electrodes implanted in the brain. Each spike train typically
contains spikes generated by multiple neurons. A natural question that arises
is "what is the number of neurons generating the spike train?"; This
article proposes a method-of-moments technique for estimating . This
technique estimates the noise nonparametrically using data from the silent
region of the spike train and it applies to isolated spikes with a possibly
small, but nonnegligible, presence of overlapping spikes. Conditions are
established in which the resulting estimator for is shown to be strongly
consistent. To gauge its finite sample performance, the technique is applied to
simulated spike trains as well as to actual neuronal spike train data.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS371 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines
Information in neural networks is represented as weighted connections, or
synapses, between neurons. This poses a problem as the primary computational
bottleneck for neural networks is the vector-matrix multiply when inputs are
multiplied by the neural network weights. Conventional processing architectures
are not well suited for simulating neural networks, often requiring large
amounts of energy and time. Additionally, synapses in biological neural
networks are not binary connections, but exhibit a nonlinear response function
as neurotransmitters are emitted and diffuse between neurons. Inspired by
neuroscience principles, we present a digital neuromorphic architecture, the
Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex
synaptic response functions without requiring additional hardware components.
We consider the paradigm of spiking neurons with temporally coded information
as opposed to non-spiking rate coded neurons used in most neural networks. In
this paradigm we examine liquid state machines applied to speech recognition
and show how a liquid state machine with temporal dynamics maps onto the
STPU-demonstrating the flexibility and efficiency of the STPU for instantiating
neural algorithms.Comment: 8 pages, 4 Figures, Preprint of 2017 IJCN
Ensemble Inhibition and Excitation in the Human Cortex: an Ising Model Analysis with Uncertainties
The pairwise maximum entropy model, also known as the Ising model, has been
widely used to analyze the collective activity of neurons. However, controversy
persists in the literature about seemingly inconsistent findings, whose
significance is unclear due to lack of reliable error estimates. We therefore
develop a method for accurately estimating parameter uncertainty based on
random walks in parameter space using adaptive Markov Chain Monte Carlo after
the convergence of the main optimization algorithm. We apply our method to the
spiking patterns of excitatory and inhibitory neurons recorded with
multielectrode arrays in the human temporal cortex during the wake-sleep cycle.
Our analysis shows that the Ising model captures neuronal collective behavior
much better than the independent model during wakefulness, light sleep, and
deep sleep when both excitatory (E) and inhibitory (I) neurons are modeled;
ignoring the inhibitory effects of I-neurons dramatically overestimates
synchrony among E-neurons. Furthermore, information-theoretic measures reveal
that the Ising model explains about 80%-95% of the correlations, depending on
sleep state and neuron type. Thermodynamic measures show signatures of
criticality, although we take this with a grain of salt as it may be merely a
reflection of long-range neural correlations.Comment: 17 pages, 8 figure
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