770 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
Delay Learning Architectures for Memory and Classification
We present a neuromorphic spiking neural network, the DELTRON, that can
remember and store patterns by changing the delays of every connection as
opposed to modifying the weights. The advantage of this architecture over
traditional weight based ones is simpler hardware implementation without
multipliers or digital-analog converters (DACs) as well as being suited to
time-based computing. The name is derived due to similarity in the learning
rule with an earlier architecture called Tempotron. The DELTRON can remember
more patterns than other delay-based networks by modifying a few delays to
remember the most 'salient' or synchronous part of every spike pattern. We
present simulations of memory capacity and classification ability of the
DELTRON for different random spatio-temporal spike patterns. The memory
capacity for noisy spike patterns and missing spikes are also shown. Finally,
we present SPICE simulation results of the core circuits involved in a
reconfigurable mixed signal implementation of this architecture.Comment: 27 pages, 20 figure
Synthesis of neural networks for spatio-temporal spike pattern recognition and processing
The advent of large scale neural computational platforms has highlighted the
lack of algorithms for synthesis of neural structures to perform predefined
cognitive tasks. The Neural Engineering Framework offers one such synthesis,
but it is most effective for a spike rate representation of neural information,
and it requires a large number of neurons to implement simple functions. We
describe a neural network synthesis method that generates synaptic connectivity
for neurons which process time-encoded neural signals, and which makes very
sparse use of neurons. The method allows the user to specify, arbitrarily,
neuronal characteristics such as axonal and dendritic delays, and synaptic
transfer functions, and then solves for the optimal input-output relationship
using computed dendritic weights. The method may be used for batch or online
learning and has an extremely fast optimization process. We demonstrate its use
in generating a network to recognize speech which is sparsely encoded as spike
times.Comment: In submission to Frontiers in Neuromorphic Engineerin
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
Asynchronous spiking neurons, the natural key to exploit temporal sparsity
Inference of Deep Neural Networks for stream signal (Video/Audio) processing in edge devices is still challenging. Unlike the most state of the art inference engines which are efficient for static signals, our brain is optimized for real-time dynamic signal processing. We believe one important feature of the brain (asynchronous state-full processing) is the key to its excellence in this domain. In this work, we show how asynchronous processing with state-full neurons allows exploitation of the existing sparsity in natural signals. This paper explains three different types of sparsity and proposes an inference algorithm which exploits all types of sparsities in the execution of already trained networks. Our experiments in three different applications (Handwritten digit recognition, Autonomous Steering and Hand-Gesture recognition) show that this model of inference reduces the number of required operations for sparse input data by a factor of one to two orders of magnitudes. Additionally, due to fully asynchronous processing this type of inference can be run on fully distributed and scalable neuromorphic hardware platforms
Investigation of Synapto-dendritic Kernel Adapting Neuron models and their use in spiking neuromorphic architectures
The motivation for this thesis is idea that abstract, adaptive, hardware efficient, inter-neuronal transfer functions (or kernels) which carry information in the form of postsynaptic membrane potentials, are the most important (and erstwhile missing) element in neuromorphic implementations of Spiking Neural Networks (SNN). In the absence of such abstract kernels, spiking neuromorphic systems must realize very large numbers of synapses and their associated connectivity. The resultant hardware and bandwidth limitations create difficult tradeoffs which diminish the usefulness of such systems.
In this thesis a novel model of spiking neurons is proposed. The proposed Synapto-dendritic Kernel Adapting Neuron (SKAN) uses the adaptation of their synapto-dendritic kernels in conjunction with an adaptive threshold to perform unsupervised learning and inference on spatio-temporal spike patterns. The hardware and connectivity requirements of the neuron model are minimized through the use of simple accumulator-based kernels as well as through the use of timing information to perform a winner take all operation between the neurons. The learning and inference operations of SKAN are characterized and shown to be robust across a range of noise environments.
Next, the SKAN model is augmented with a simplified hardware-efficient model of Spike Timing Dependent Plasticity (STDP). In biology STDP is the mechanism which allows neurons to learn spatio-temporal spike patterns. However when the proposed SKAN model is augmented with a simplified STDP rule, where the synaptic kernel is used as a binary flag that enable synaptic potentiation, the result is a synaptic encoding of afferent Signal to Noise Ratio (SNR). In this combined model the neuron not only learns the target spatio-temporal spike patterns but also weighs each channel independently according to its signal to noise ratio. Additionally a novel approach is presented to achieving homeostatic plasticity in digital hardware which reduces hardware cost by eliminating the need for multipliers.
Finally the behavior and potential utility of this combined model is investigated in a range of noise conditions and the digital hardware resource utilization of SKAN and SKAN + STDP is detailed using Field Programmable Gate Arrays (FPGA)
- …