Indirect Training Algorithms for Spiking Neural Networks based on Spiking Timing Dependent Plasticity and Their Applications

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2017

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Abstract

Spiking neural networks have been used to investigate the mechanisms of processing

in biological neural circuits or to propose hypotheses that can be tested in exper-

iments. Because of their biological plausibility and event-based information trans-

mission, Spiking Neural Networks (SNNs) have suggested as alternatives to Articial

Neural Networks for pattern recognition, classication and function approximation

problems with fewer neurons. In machine learning, SNNs has been shown to be able

to solve pattern and robotic control. For SNNs to be used for such problems, they

must incorporate some mechanism for learning. Current methods to train SNNs use

learning algorithms which adjust the synaptic weights according to an update rule.

In most cases the weights are modied directly. In potential applications such as

driving plasticity in neural culture (in-vitro) and training neuromorphic chips the

directly manipulation of synaptic weights is not possible. Therefore, indirect algo-

rithms, which cause the SNNs to learn based on some biological learning mechanisms

using stimulation of neurons oer signicant advantages over the existing algorithm

for these real world applications.

Indirect algorithms train the neural network by using external stimuli to modulate

the synaptic strengths of a neural network according to synapses intrinsic mechanisms

for plasticity. The training algorithms have been demonstrated in both Integrate and

Fire neurons and more biologically realistic neural networks. In this thesis, four indi-

rect methods to drive the synaptic weights to its desired value in a network through Spike Time Dependent Plasticity (STDP) are developed: Indirect Perturbation, In-

direct Stochastic Gradient, Indirect ReSuMe, and Indirect Training with Supervised

Teaching Signals. These algorithms are used to solve the temporal and spatial input-

output mapping problem using temporal coding. The other type of problem is to

mapping input output ring rates using rate coding.

To test the algorithms, SNNs are used to control both virtual and real world

robots. For the real world robots with SNNs, known and Neurorobots, two types

of robot localization techniques are used: Optitrack, using ceiling mounted cameras

and onboard markers, and embedded cameras. Both small and large SNNs with

biologically realistic neurons are used to drive the neurorobots are modeled with

input coming from Optitrack or the cameras with GPU accelerated SNN simulator.

The results show that the indirect perturbation and indirect stochastic gradient

algorithms can train an SNN to control the robot to nd targets and avoid obstacles

even in the presence of sensor noise. The results also show that indirect training with

supervised training signals algorithm can train a feedforward network with 1000s of

neurons to process and output the correct movement commands to localize a target

using from real time images captured from an embedded camera. Finally. an indirect

version of the Remote Supervised Method (ReSuMe) algorithm was developed using

a more biologically realistic form of Spike-Timing Dependent Plasticity to produce a

specic temporal pattern of spiking from a group of neurons. The indirect algorithms

developed in this thesis may eventually allow the ability to train in vitro and in

vivo biological circuits to perform specic tasks using patterns of electrical or light

stimulation.

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Citation

Zhang, Xu (2017). Indirect Training Algorithms for Spiking Neural Networks based on Spiking Timing Dependent Plasticity and Their Applications. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/14362.

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