58 research outputs found
A neural circuit for navigation inspired by C. elegans Chemotaxis
We develop an artificial neural circuit for contour tracking and navigation
inspired by the chemotaxis of the nematode Caenorhabditis elegans. In order to
harness the computational advantages spiking neural networks promise over their
non-spiking counterparts, we develop a network comprising 7-spiking neurons
with non-plastic synapses which we show is extremely robust in tracking a range
of concentrations. Our worm uses information regarding local temporal gradients
in sodium chloride concentration to decide the instantaneous path for foraging,
exploration and tracking. A key neuron pair in the C. elegans chemotaxis
network is the ASEL & ASER neuron pair, which capture the gradient of
concentration sensed by the worm in their graded membrane potentials. The
primary sensory neurons for our network are a pair of artificial spiking
neurons that function as gradient detectors whose design is adapted from a
computational model of the ASE neuron pair in C. elegans. Simulations show that
our worm is able to detect the set-point with approximately four times higher
probability than the optimal memoryless Levy foraging model. We also show that
our spiking neural network is much more efficient and noise-resilient while
navigating and tracking a contour, as compared to an equivalent non-spiking
network. We demonstrate that our model is extremely robust to noise and with
slight modifications can be used for other practical applications such as
obstacle avoidance. Our network model could also be extended for use in
three-dimensional contour tracking or obstacle avoidance
Training Multi-layer Spiking Neural Networks using NormAD based Spatio-Temporal Error Backpropagation
Spiking neural networks (SNNs) have garnered a great amount of interest for
supervised and unsupervised learning applications. This paper deals with the
problem of training multi-layer feedforward SNNs. The non-linear
integrate-and-fire dynamics employed by spiking neurons make it difficult to
train SNNs to generate desired spike trains in response to a given input. To
tackle this, first the problem of training a multi-layer SNN is formulated as
an optimization problem such that its objective function is based on the
deviation in membrane potential rather than the spike arrival instants. Then,
an optimization method named Normalized Approximate Descent (NormAD),
hand-crafted for such non-convex optimization problems, is employed to derive
the iterative synaptic weight update rule. Next, it is reformulated to
efficiently train multi-layer SNNs, and is shown to be effectively performing
spatio-temporal error backpropagation. The learning rule is validated by
training -layer SNNs to solve a spike based formulation of the XOR problem
as well as training -layer SNNs for generic spike based training problems.
Thus, the new algorithm is a key step towards building deep spiking neural
networks capable of efficient event-triggered learning.Comment: 19 pages, 10 figure
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
Training Probabilistic Spiking Neural Networks with First-to-spike Decoding
Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at
harnessing the energy efficiency of spike-domain processing by building on
computing elements that operate on, and exchange, spikes. In this paper, the
problem of training a two-layer SNN is studied for the purpose of
classification, under a Generalized Linear Model (GLM) probabilistic neural
model that was previously considered within the computational neuroscience
literature. Conventional classification rules for SNNs operate offline based on
the number of output spikes at each output neuron. In contrast, a novel
training method is proposed here for a first-to-spike decoding rule, whereby
the SNN can perform an early classification decision once spike firing is
detected at an output neuron. Numerical results bring insights into the optimal
parameter selection for the GLM neuron and on the accuracy-complexity trade-off
performance of conventional and first-to-spike decoding.Comment: A shorter version will be published on Proc. IEEE ICASSP 201
A Convolutional Spiking Network for Gesture Recognition in Brain-Computer Interfaces
Brain-computer interfaces are being explored for a wide variety of
therapeutic applications. Typically, this involves measuring and analyzing
continuous-time electrical brain activity via techniques such as
electrocorticogram (ECoG) or electroencephalography (EEG) to drive external
devices. However, due to the inherent noise and variability in the
measurements, the analysis of these signals is challenging and requires offline
processing with significant computational resources. In this paper, we propose
a simple yet efficient machine learning-based approach for the exemplary
problem of hand gesture classification based on brain signals. We use a hybrid
machine learning approach that uses a convolutional spiking neural network
employing a bio-inspired event-driven synaptic plasticity rule for unsupervised
feature learning of the measured analog signals encoded in the spike domain. We
demonstrate that this approach generalizes to different subjects with both EEG
and ECoG data and achieves superior accuracy in the range of 92.74-97.07% in
identifying different hand gesture classes and motor imagery tasks
Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learning
Neuromorphic data carries information in spatio-temporal patterns encoded by
spikes. Accordingly, a central problem in neuromorphic computing is training
spiking neural networks (SNNs) to reproduce spatio-temporal spiking patterns in
response to given spiking stimuli. Most existing approaches model the
input-output behavior of an SNN in a deterministic fashion by assigning each
input to a specific desired output spiking sequence. In contrast, in order to
fully leverage the time-encoding capacity of spikes, this work proposes to
train SNNs so as to match distributions of spiking signals rather than
individual spiking signals. To this end, the paper introduces a novel hybrid
architecture comprising a conditional generator, implemented via an SNN, and a
discriminator, implemented by a conventional artificial neural network (ANN).
The role of the ANN is to provide feedback during training to the SNN within an
adversarial iterative learning strategy that follows the principle of
generative adversarial network (GANs). In order to better capture multi-modal
spatio-temporal distribution, the proposed approach -- termed SpikeGAN -- is
further extended to support Bayesian learning of the generator's weight.
Finally, settings with time-varying statistics are addressed by proposing an
online meta-learning variant of SpikeGAN. Experiments bring insights into the
merits of the proposed approach as compared to existing solutions based on
(static) belief networks and maximum likelihood (or empirical risk
minimization)
Ultra-Low Power Neuromorphic Obstacle Detection Using a Two-Dimensional Materials-Based Subthreshold Transistor
Accurate, timely and selective detection of moving obstacles is crucial for
reliable collision avoidance in autonomous robots. The area- and
energy-inefficiency of CMOS-based spiking neurons for obstacle detection can be
addressed through the reconfigurable, tunable and low-power operation
capabilities of emerging two-dimensional (2D) materials-based devices. We
present an ultra-low power spiking neuron built using an electrostatically
tuned dual-gate transistor with an ultra-thin and generic 2D material channel.
The 2D subthreshold transistor (2D-ST) is carefully designed to operate under
low-current subthreshold regime. Carrier transport has been modelled via
over-the-barrier thermionic and Fowler-Nordheim contact barrier tunnelling
currents over a wide range of gate and drain biases. Simulation of a neuron
circuit designed using the 2D-ST with 45 nm CMOS technology components shows
high energy efficiency of ~3.5 pJ/spike and biomimetic class-I as well as
oscillatory spiking. It also demonstrates complex neuronal behaviors such as
spike-frequency adaptation and post-inhibitory rebound that are crucial for
dynamic visual systems. Lobula giant movement detector (LGMD) is a
collision-detecting biological neuron found in locusts. Our neuron circuit can
generate LGMD-like spiking behavior and detect obstacles at an energy cost of
<100 pJ. Further, it can be reconfigured to distinguish between looming and
receding objects with high selectivity.Comment: Main text along with supporting information. 4 figure
Adversarial Training for Probabilistic Spiking Neural Networks
Classifiers trained using conventional empirical risk minimization or maximum
likelihood methods are known to suffer dramatic performance degradations when
tested over examples adversarially selected based on knowledge of the
classifier's decision rule. Due to the prominence of Artificial Neural Networks
(ANNs) as classifiers, their sensitivity to adversarial examples, as well as
robust training schemes, have been recently the subject of intense
investigation. In this paper, for the first time, the sensitivity of spiking
neural networks (SNNs), or third-generation neural networks, to adversarial
examples is studied. The study considers rate and time encoding, as well as
rate and first-to-spike decoding. Furthermore, a robust training mechanism is
proposed that is demonstrated to enhance the performance of SNNs under
white-box attacks.Comment: Submitted for possible publication. arXiv admin note: text overlap
with arXiv:1710.1070
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