A New Supervised Learning Algorithm for Spiking Neurons

2013 ◽  
Vol 25 (6) ◽  
pp. 1472-1511 ◽  
Author(s):  
Yan Xu ◽  
Xiaoqin Zeng ◽  
Shuiming Zhong
Keyword(s):  
Supervised Learning ◽  
Learning Algorithm ◽  
Learning Rule ◽  
Classification Problem ◽  
Higher Learning ◽  
Spiking Neurons ◽  
Spiking Neuron ◽  
Temporal Encoding ◽  
The Times ◽  
Perceptron Learning

The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.

scholarly journals Improved SpikeProp for Using Particle Swarm Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Falah Y. H. Ahmed ◽  
Siti Mariyam Shamsuddin ◽  
Siti Zaiton Mohd Hashim
Keyword(s):  
Particle Swarm Optimization ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Learning Rule ◽  
Temporal Coding ◽  
Spiking Neurons ◽  
Learning Enhancement ◽  
Swarm Optimization ◽  
Spiking Networks

A spiking neurons network encodes information in the timing of individual spike times. A novel supervised learning rule for SpikeProp is derived to overcome the discontinuities introduced by the spiking thresholding. This algorithm is based on an error-backpropagation learning rule suited for supervised learning of spiking neurons that use exact spike time coding. The SpikeProp is able to demonstrate the spiking neurons that can perform complex nonlinear classification in fast temporal coding. This study proposes enhancements of SpikeProp learning algorithm for supervised training of spiking networks which can deal with complex patterns. The proposed methods include the SpikeProp particle swarm optimization (PSO) and angle driven dependency learning rate. These methods are presented to SpikeProp network for multilayer learning enhancement and weights optimization. Input and output patterns are encoded as spike trains of precisely timed spikes, and the network learns to transform the input trains into target output trains. With these enhancements, our proposed methods outperformed other conventional neural network architectures.

scholarly journals An Auto-Adjustable Semi-Supervised Self-Training Algorithm

Algorithms ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 139 ◽  
Author(s):  
Ioannis Livieris ◽  
Andreas Kanavos ◽  
Vassilis Tampakas ◽  
Panagiotis Pintelas
Keyword(s):  
Supervised Learning ◽  
Predictive Models ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Classification Problem ◽  
Classification Methods ◽  
Training Algorithm ◽  
Traditional Classification ◽  
Supervised Learning Algorithms ◽  
Significant Research

Semi-supervised learning algorithms have become a topic of significant research as an alternative to traditional classification methods which exhibit remarkable performance over labeled data but lack the ability to be applied on large amounts of unlabeled data. In this work, we propose a new semi-supervised learning algorithm that dynamically selects the most promising learner for a classification problem from a pool of classifiers based on a self-training philosophy. Our experimental results illustrate that the proposed algorithm outperforms its component semi-supervised learning algorithms in terms of accuracy, leading to more efficient, stable and robust predictive models.

A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy

2017 ◽  
Vol 27 (03) ◽  
pp. 1750002 ◽  
Author(s):  
Lilin Guo ◽  
Zhenzhong Wang ◽  
Mercedes Cabrerizo ◽  
Malek Adjouadi
Keyword(s):  
Adaptive Learning ◽  
Learning Algorithm ◽  
Recognition Performance ◽  
Learning Rule ◽  
Classification Performance ◽  
Spiking Neurons ◽  
Learning Performance ◽  
Learning Method ◽  
Neuron Models ◽  
Interictal Spike

This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timing of spikes. Unlike the Remote Supervised Method (ReSuMe), synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. The CCDS rule is both biologically plausible and computationally efficient. The properties of this learning rule are investigated extensively through experimental evaluations in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. Results presented show that the CCDS learning method achieves learning accuracy and learning speed comparable with ReSuMe, but improves classification accuracy when compared to both the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. The merit of CCDS rule is further validated on a practical example involving the automated detection of interictal spikes in EEG records of patients with epilepsy. Results again show that with proper encoding, the CCDS rule achieves good recognition performance.

Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting

2010 ◽  
Vol 22 (2) ◽  
pp. 467-510 ◽  
Author(s):  
Filip Ponulak ◽  
Andrzej Kasiński
Keyword(s):  
Supervised Learning ◽  
Fundamental Property ◽  
Learning Rule ◽  
Relevant Information ◽  
Spiking Neurons ◽  
Time Shift ◽  
Surprising Result ◽  
Daily Routines ◽  
Input Signals ◽  
Target Templates

Learning from instructions or demonstrations is a fundamental property of our brain necessary to acquire new knowledge and develop novel skills or behavioral patterns. This type of learning is thought to be involved in most of our daily routines. Although the concept of instruction-based learning has been studied for several decades, the exact neural mechanisms implementing this process remain unrevealed. One of the central questions in this regard is, How do neurons learn to reproduce template signals (instructions) encoded in precisely timed sequences of spikes? Here we present a model of supervised learning for biologically plausible neurons that addresses this question. In a set of experiments, we demonstrate that our approach enables us to train spiking neurons to reproduce arbitrary template spike patterns in response to given synaptic stimuli even in the presence of various sources of noise. We show that the learning rule can also be used for decision-making tasks. Neurons can be trained to classify categories of input signals based on only a temporal configuration of spikes. The decision is communicated by emitting precisely timed spike trains associated with given input categories. Trained neurons can perform the classification task correctly even if stimuli and corresponding decision times are temporally separated and the relevant information is consequently highly overlapped by the ongoing neural activity. Finally, we demonstrate that neurons can be trained to reproduce sequences of spikes with a controllable time shift with respect to target templates. A reproduced signal can follow or even precede the targets. This surprising result points out that spiking neurons can potentially be applied to forecast the behavior (firing times) of other reference neurons or networks.

Semi-Supervised Learning for Classification with Uncertainty

2012 ◽  
Vol 433-440 ◽  
pp. 3584-3590 ◽  
Author(s):  
Rui Zhang ◽  
Tong Bo Liu ◽  
Ming Wen Zheng
Keyword(s):  
Supervised Learning ◽  
Classification Problem ◽  
Support Vector ◽  
Training Set ◽  
Powerful Learning ◽  
Svm Model ◽  
Working Set ◽  
Training Examples ◽  
Learning Machine ◽  
The Times

Support vector machine (SVM) is a general and powerful learning machine, which adopts supervised manner. However, for many practical machine learning and data mining applications, unlabeled training examples are readily available but labeled ones are very expensive to be obtained. Therefore, semi-supervised learning emerges as the times require. At present, the combination of SVM and semi-supervised learning (S3VM) has attracted more and more attentions. In general, S3VM deals with problems with small training sets and large working sets. When the training set is large relative to the working set, We propose a new SVM model to solve the above classification problem by introducing the fuzzy memberships to each unlabeled point. Simulation results demonstrate that the proposed method can exploit unlabeled data to yield good performance effectively.

From Winner-Takes-All to Winners-Share-All: Exploiting the Information Capacity in Temporal Codes

2018 ◽  
Vol 30 (3) ◽  
pp. 761-791 ◽  
Author(s):  
Melika Payvand ◽  
Luke Theogarajan
Keyword(s):  
Neural Coding ◽  
Learning Rule ◽  
Classification Problem ◽  
Spike Timing ◽  
Spiking Neurons ◽  
Information Capacity ◽  
Image Size ◽  
English Alphabet ◽  
Output Neurons ◽  
Winner Takes All

In this letter, we have implemented and compared two neural coding algorithms in the networks of spiking neurons: Winner-takes-all (WTA) and winners-share-all (WSA). Winners-Share-All exploits the code space provided by the temporal code by training a different combination of [Formula: see text] out of [Formula: see text] neurons to fire together in response to different patterns, while WTA uses a one-hot-coding to respond to distinguished patterns. Using WSA, the maximum value of [Formula: see text] in order to maximize information capacity using [Formula: see text] output neurons was theoretically determined and utilized. A small proof-of-concept classification problem was applied to a spiking neural network using both algorithms to classify 14 letters of English alphabet with an image size of 15 [Formula: see text] 15 pixels. For both schemes, a modified spike-timing-dependent-plasticity (STDP) learning rule has been used to train the spiking neurons in an unsupervised fashion. The performance and the number of neurons required to perform this computation are compared between the two algorithms. We show that by tolerating a small drop in performance accuracy (84% in WSA versus 91% in WTA), we are able to reduce the number of output neurons by more than a factor of two. We show how the reduction in the number of neurons will increase as the number of patterns increases. The reduction in the number of output neurons would then proportionally reduce the number of training parameters, which requires less memory and hence speeds up the computation, and in the case of neuromorphic implementation on silicon, would take up much less area.

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