Evolving Spiking Networks with Variable Resistive Memories

Neuromorphic computing is a brainlike information processing paradigm that requires adaptive learning mechanisms. A spiking neuro-evolutionary system is used for this purpose; plastic resistive memories are implemented as synapses in spiking neural networks. The evolutionary design process exploits parameter self-adaptation and allows the topology and synaptic weights to be evolved for each network in an autonomous manner. Variable resistive memories are the focus of this research; each synapse has its own conductance profile which modifies the plastic behaviour of the device and may be altered during evolution. These variable resistive networks are evaluated on a noisy robotic dynamic-reward scenario against two static resistive memories and a system containing standard connections only. The results indicate that the extra behavioural degrees of freedom available to the networks incorporating variable resistive memories enable them to outperform the comparative synapse types.

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The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks

10.1101/2020.06.29.176925 ◽

2020 ◽

Author(s):

Friedemann Zenke ◽

Tim P. Vogels

Keyword(s):

Neural Networks ◽

Complex Function ◽

Spiking Neural Networks ◽

Learning Performance ◽

Design Parameters ◽

Classification Problems ◽

Systematic Account ◽

Practical Algorithms ◽

Spiking Networks ◽

Gradient Learning

AbstractBrains process information in spiking neural networks. Their intricate connections shape the diverse functions these networks perform. In comparison, the functional capabilities of models of spiking networks are still rudimentary. This shortcoming is mainly due to the lack of insight and practical algorithms to construct the necessary connectivity. Any such algorithm typically attempts to build networks by iteratively reducing the error compared to a desired output. But assigning credit to hidden units in multi-layered spiking networks has remained challenging due to the non-differentiable nonlinearity of spikes. To avoid this issue, one can employ surrogate gradients to discover the required connectivity in spiking network models. However, the choice of a surrogate is not unique, raising the question of how its implementation influences the effectiveness of the method. Here, we use numerical simulations to systematically study how essential design parameters of surrogate gradients impact learning performance on a range of classification problems. We show that surrogate gradient learning is robust to different shapes of underlying surrogate derivatives, but the choice of the derivative’s scale can substantially affect learning performance. When we combine surrogate gradients with a suitable activity regularization technique, robust information processing can be achieved in spiking networks even at the sparse activity limit. Our study provides a systematic account of the remarkable robustness of surrogate gradient learning and serves as a practical guide to model functional spiking neural networks.

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Modelling an Adaptive Learning System Using Artificial Intelligence

Webology ◽

10.14704/web/v19i1/web19001 ◽

2021 ◽

Vol 19 (1) ◽

pp. 01-18

Author(s):

Hayder Rahm Dakheel AL-Fayyadh ◽

Salam Abdulabbas Ganim Ali ◽

Dr. Basim Abood

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Adaptive Learning ◽

Back Propagation ◽

Learning System ◽

Spiking Neural Networks ◽

Domain Specific Language ◽

Domain Specific ◽

Adaptive Learning System ◽

Artificial Neural

The goal of this paper is to use artificial intelligence to build and evaluate an adaptive learning system where we adopt the basic approaches of spiking neural networks as well as artificial neural networks. Spiking neural networks receive increasing attention due to their advantages over traditional artificial neural networks. They have proven to be energy efficient, biological plausible, and up to 105 times faster if they are simulated on analogue traditional learning systems. Artificial neural network libraries use computational graphs as a pervasive representation, however, spiking models remain heterogeneous and difficult to train. Using the artificial intelligence deductive method, the paper posits two hypotheses that examines whether 1) there exists a common representation for both neural networks paradigms for tutorial mentoring, and whether 2) spiking and non-spiking models can learn a simple recognition task for learning activities for adaptive learning. The first hypothesis is confirmed by specifying and implementing a domain-specific language that generates semantically similar spiking and non-spiking neural networks for tutorial mentoring. Through three classification experiments, the second hypothesis is shown to hold for non-spiking models, but cannot be proven for the spiking models. The paper contributes three findings: 1) a domain-specific language for modelling neural network topologies in adaptive tutorial mentoring for students, 2) a preliminary model for generalizable learning through back-propagation in spiking neural networks for learning activities for students also represented in results section, and 3) a method for transferring optimised non-spiking parameters to spiking neural networks has also been developed for adaptive learning system. The latter contribution is promising because the vast machine learning literature can spill-over to the emerging field of spiking neural networks and adaptive learning computing. Future work includes improving the back-propagation model, exploring time-dependent models for learning, and adding support for adaptive learning systems.

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Self-Adaptation in Evolutionary Design of Neural Networks

The State of the Art in Computational Intelligence ◽

10.1007/978-3-7908-1844-4_62 ◽

2000 ◽

pp. 382-383

Author(s):

Bohdan Macukow ◽

Maciej Grzenda

Keyword(s):

Neural Networks ◽

Evolutionary Design ◽

Self Adaptation

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Event-based backpropagation can compute exact gradients for spiking neural networks

Scientific Reports ◽

10.1038/s41598-021-91786-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Timo C. Wunderlich ◽

Christian Pehle

Keyword(s):

Neural Networks ◽

Loss Function ◽

Spiking Neural Networks ◽

Backpropagation Algorithm ◽

General Loss Function ◽

Gradient Based ◽

Rigorous Study ◽

Spiking Networks ◽

Event Based ◽

First Time

AbstractSpiking neural networks combine analog computation with event-based communication using discrete spikes. While the impressive advances of deep learning are enabled by training non-spiking artificial neural networks using the backpropagation algorithm, applying this algorithm to spiking networks was previously hindered by the existence of discrete spike events and discontinuities. For the first time, this work derives the backpropagation algorithm for a continuous-time spiking neural network and a general loss function by applying the adjoint method together with the proper partial derivative jumps, allowing for backpropagation through discrete spike events without approximations. This algorithm, EventProp, backpropagates errors at spike times in order to compute the exact gradient in an event-based, temporally and spatially sparse fashion. We use gradients computed via EventProp to train networks on the Yin-Yang and MNIST datasets using either a spike time or voltage based loss function and report competitive performance. Our work supports the rigorous study of gradient-based learning algorithms in spiking neural networks and provides insights toward their implementation in novel brain-inspired hardware.

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The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks

Neural Computation ◽

10.1162/neco_a_01367 ◽

2021 ◽

pp. 1-27

Author(s):

Friedemann Zenke ◽

Tim P. Vogels

Keyword(s):

Neural Networks ◽

Information Processing ◽

Complex Function ◽

Spiking Neural Networks ◽

Learning Performance ◽

Design Parameters ◽

Practical Algorithms ◽

Neuromorphic Hardware ◽

Spiking Networks ◽

Gradient Learning

Brains process information in spiking neural networks. Their intricate connections shape the diverse functions these networks perform. Yet how network connectivity relates to function is poorly understood, and the functional capabilities of models of spiking networks are still rudimentary. The lack of both theoretical insight and practical algorithms to find the necessary connectivity poses a major impediment to both studying information processing in the brain and building efficient neuromorphic hardware systems. The training algorithms that solve this problem for artificial neural networks typically rely on gradient descent. But doing so in spiking networks has remained challenging due to the nondifferentiable nonlinearity of spikes. To avoid this issue, one can employ surrogate gradients to discover the required connectivity. However, the choice of a surrogate is not unique, raising the question of how its implementation influences the effectiveness of the method. Here, we use numerical simulations to systematically study how essential design parameters of surrogate gradients affect learning performance on a range of classification problems. We show that surrogate gradient learning is robust to different shapes of underlying surrogate derivatives, but the choice of the derivative's scale can substantially affect learning performance. When we combine surrogate gradients with suitable activity regularization techniques, spiking networks perform robust information processing at the sparse activity limit. Our study provides a systematic account of the remarkable robustness of surrogate gradient learning and serves as a practical guide to model functional spiking neural networks.

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A Radial Basis Function Spike Model for Indirect Learning via Integrate-and-Fire Sampling and Reconstruction Techniques

Advances in Artificial Neural Systems ◽

10.1155/2012/713581 ◽

2012 ◽

Vol 2012 ◽

pp. 1-16 ◽

Cited By ~ 8

Author(s):

X. Zhang ◽

G. Foderaro ◽

C. Henriquez ◽

A. M. J. VanDongen ◽

S. Ferrari

Keyword(s):

Neural Networks ◽

Synaptic Plasticity ◽

Spike Trains ◽

Spike Timing ◽

Spiking Neural Networks ◽

Learning Mechanisms ◽

Integrate And Fire ◽

Spike Model ◽

Radial Basis ◽

Input Spike

This paper presents a deterministic and adaptive spike model derived from radial basis functions and a leaky integrate-and-fire sampler developed for training spiking neural networks without direct weight manipulation. Several algorithms have been proposed for training spiking neural networks through biologically-plausible learning mechanisms, such as spike-timing-dependent synaptic plasticity and Hebbian plasticity. These algorithms typically rely on the ability to update the synaptic strengths, or weights, directly, through a weight update rule in which the weight increment can be decided and implemented based on the training equations. However, in several potential applications of adaptive spiking neural networks, including neuroprosthetic devices and CMOS/memristor nanoscale neuromorphic chips, the weights cannot be manipulated directly and, instead, tend to change over time by virtue of the pre- and postsynaptic neural activity. This paper presents an indirect learning method that induces changes in the synaptic weights by modulating spike-timing-dependent plasticity by means of controlled input spike trains. In place of the weights, the algorithm manipulates the input spike trains used to stimulate the input neurons by determining a sequence of spike timings that minimize a desired objective function and, indirectly, induce the desired synaptic plasticity in the network.

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