3,658 research outputs found
Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout
Heart-rate estimation is a fundamental feature of modern wearable devices. In
this paper we propose a machine intelligent approach for heart-rate estimation
from electrocardiogram (ECG) data collected using wearable devices. The novelty
of our approach lies in (1) encoding spatio-temporal properties of ECG signals
directly into spike train and using this to excite recurrently connected
spiking neurons in a Liquid State Machine computation model; (2) a novel
learning algorithm; and (3) an intelligently designed unsupervised readout
based on Fuzzy c-Means clustering of spike responses from a subset of neurons
(Liquid states), selected using particle swarm optimization. Our approach
differs from existing works by learning directly from ECG signals (allowing
personalization), without requiring costly data annotations. Additionally, our
approach can be easily implemented on state-of-the-art spiking-based
neuromorphic systems, offering high accuracy, yet significantly low energy
footprint, leading to an extended battery life of wearable devices. We
validated our approach with CARLsim, a GPU accelerated spiking neural network
simulator modeling Izhikevich spiking neurons with Spike Timing Dependent
Plasticity (STDP) and homeostatic scaling. A range of subjects are considered
from in-house clinical trials and public ECG databases. Results show high
accuracy and low energy footprint in heart-rate estimation across subjects with
and without cardiac irregularities, signifying the strong potential of this
approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at
Elsevier Neural Network
Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients
Artificial Neural Networks (ANNs) are currently being used as function
approximators in many state-of-the-art Reinforcement Learning (RL) algorithms.
Spiking Neural Networks (SNNs) have been shown to drastically reduce the energy
consumption of ANNs by encoding information in sparse temporal binary spike
streams, hence emulating the communication mechanism of biological neurons. Due
to their low energy consumption, SNNs are considered to be important candidates
as co-processors to be implemented in mobile devices. In this work, the use of
SNNs as stochastic policies is explored under an energy-efficient
first-to-spike action rule, whereby the action taken by the RL agent is
determined by the occurrence of the first spike among the output neurons. A
policy gradient-based algorithm is derived considering a Generalized Linear
Model (GLM) for spiking neurons. Experimental results demonstrate the
capability of online trained SNNs as stochastic policies to gracefully trade
energy consumption, as measured by the number of spikes, and control
performance. Significant gains are shown as compared to the standard approach
of converting an offline trained ANN into an SNN.Comment: Submitted for conference publicatio
Stochastic Spin-Orbit Torque Devices as Elements for Bayesian Inference
Probabilistic inference from real-time input data is becoming increasingly
popular and may be one of the potential pathways at enabling cognitive
intelligence. As a matter of fact, preliminary research has revealed that
stochastic functionalities also underlie the spiking behavior of neurons in
cortical microcircuits of the human brain. In tune with such observations,
neuromorphic and other unconventional computing platforms have recently started
adopting the usage of computational units that generate outputs
probabilistically, depending on the magnitude of the input stimulus. In this
work, we experimentally demonstrate a spintronic device that offers a direct
mapping to the functionality of such a controllable stochastic switching
element. We show that the probabilistic switching of Ta/CoFeB/MgO
heterostructures in presence of spin-orbit torque and thermal noise can be
harnessed to enable probabilistic inference in a plethora of unconventional
computing scenarios. This work can potentially pave the way for hardware that
directly mimics the computational units of Bayesian inference
Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective
On metrics of density and power efficiency, neuromorphic technologies have
the potential to surpass mainstream computing technologies in tasks where
real-time functionality, adaptability, and autonomy are essential. While
algorithmic advances in neuromorphic computing are proceeding successfully, the
potential of memristors to improve neuromorphic computing have not yet born
fruit, primarily because they are often used as a drop-in replacement to
conventional memory. However, interdisciplinary approaches anchored in machine
learning theory suggest that multifactor plasticity rules matching neural and
synaptic dynamics to the device capabilities can take better advantage of
memristor dynamics and its stochasticity. Furthermore, such plasticity rules
generally show much higher performance than that of classical Spike Time
Dependent Plasticity (STDP) rules. This chapter reviews the recent development
in learning with spiking neural network models and their possible
implementation with memristor-based hardware
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