4,765 research outputs found
Self-Learning Fuzzy Spiking Neural Network as a Nonlinear Pulse-Position Threshold Detection Dynamic System Based on Second-Order Critically Damped Response Units
Architecture and learning algorithm of self-learning spiking neural network in fuzzy clustering task are
outlined. Fuzzy receptive neurons for pulse-position transformation of input data are considered. It is proposed to
treat a spiking neural network in terms of classical automatic control theory apparatus based on the Laplace
transform. It is shown that synapse functioning can be easily modeled by a second order damped response unit.
Spiking neuron soma is presented as a threshold detection unit. Thus, the proposed fuzzy spiking neural network
is an analog-digital nonlinear pulse-position dynamic system. It is demonstrated how fuzzy probabilistic and
possibilistic clustering approaches can be implemented on the base of the presented spiking neural network
Live Demonstration: Neuromorphic Row-by-Row Multi-convolution FPGA Processor-SpiNNaker architecture for Dynamic-Vision Feature Extraction
In this demonstration a spiking neural network
architecture for vision recognition using an FPGA spiking
convolution processor, based on leaky integrate and fire neurons
(LIF) and a SpiNNaker board is presented. The network has
been trained with Poker-DVS dataset in order to classify the
four different card symbols. The spiking convolution processor
extracts features from images in form of spikes, computes by
one layer of 64 convolutions. These features are sent to an
OKAERtool board that converts from AER to 2-7 protocol
to be classified by a spiking neural network deployed on a
SpiNNaker platform
Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor
Neuromorphic computing is a new paradigm for design of both the computing
hardware and algorithms inspired by biological neural networks. The event-based
nature and the inherent parallelism make neuromorphic computing a promising
paradigm for building efficient neural network based architectures for control
of fast and agile robots. In this paper, we present a spiking neural network
architecture that uses sensory feedback to control rotational velocity of a
robotic vehicle. When the velocity reaches the target value, the mapping from
the target velocity of the vehicle to the correct motor command, both
represented in the spiking neural network on the neuromorphic device, is
autonomously stored on the device using on-chip plastic synaptic weights. We
validate the controller using a wheel motor of a miniature mobile vehicle and
inertia measurement unit as the sensory feedback and demonstrate online
learning of a simple 'inverse model' in a two-layer spiking neural network on
the neuromorphic chip. The prototype neuromorphic device that features 256
spiking neurons allows us to realise a simple proof of concept architecture for
the purely neuromorphic motor control and learning. The architecture can be
easily scaled-up if a larger neuromorphic device is available.Comment: 6+1 pages, 4 figures, will appear in one of the Robotics conference
Supervised Associative Learning in Spiking Neural Network
In this paper, we propose a simple supervised associative learning approach for spiking neural networks. In an excitatory-inhibitory network paradigm with Izhikevich spiking neurons, synaptic plasticity is implemented on excitatory to excitatory synapses dependent on both spike emission rates and spike timings. As results of learning, the network is able to associate not just familiar stimuli but also novel stimuli observed through synchronised activity within the same subpopulation and between two associated subpopulations
Combinatorial optimization solving by coherent Ising machines based on spiking neural networks
Spiking neural network is a kind of neuromorphic computing that is believed
to improve the level of intelligence and provide advantages for quantum
computing. In this work, we address this issue by designing an optical spiking
neural network and find that it can be used to accelerate the speed of
computation, especially on combinatorial optimization problems. Here the
spiking neural network is constructed by the antisymmetrically coupled
degenerate optical parametric oscillator pulses and dissipative pulses. A
nonlinear transfer function is chosen to mitigate amplitude inhomogeneities and
destabilize the resulting local minima according to the dynamical behavior of
spiking neurons. It is numerically shown that the spiking neural
network-coherent Ising machines have excellent performance on combinatorial
optimization problems, which is expected to offer new applications for neural
computing and optical computing.Comment: 10 pages, 5 figures, accepted by Quantu
Stimulus sensitivity of a spiking neural network model
Some recent papers relate the criticality of complex systems to their maximal
capacity of information processing. In the present paper, we consider high
dimensional point processes, known as age-dependent Hawkes processes, which
have been used to model spiking neural networks. Using mean-field
approximation, the response of the network to a stimulus is computed and we
provide a notion of stimulus sensitivity. It appears that the maximal
sensitivity is achieved in the sub-critical regime, yet almost critical for a
range of biologically relevant parameters
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