A supervised learning rule for classification of spatiotemporal spike patterns

2016 ◽  
Author(s):  
Lilin Guo ◽  
Zhenzhong Wang ◽  
Malek Adjouadi
Keyword(s):  
Supervised Learning ◽  
Learning Rule ◽  
Spike Patterns

A Gradient Learning Rule for the Tempotron

2009 ◽  
Vol 21 (2) ◽  
pp. 340-352 ◽  
Author(s):  
Robert Urbanczik ◽  
Walter Senn
Keyword(s):  
Cost Function ◽  
Supervised Learning ◽  
Binary Classification ◽  
Learning Rule ◽  
Spike Trains ◽  
Integrate And Fire ◽  
Gradient Learning ◽  
Input Spike ◽  
Integrate And Fire Neuron

We introduce a new supervised learning rule for the tempotron task: the binary classification of input spike trains by an integrate-and-fire neuron that encodes its decision by firing or not firing. The rule is based on the gradient of a cost function, is found to have enhanced performance, and does not rely on a specific reset mechanism in the integrate-and-fire neuron.

scholarly journals SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks

2018 ◽  
Vol 30 (6) ◽  
pp. 1514-1541 ◽  
Author(s):  
Friedemann Zenke ◽  
Surya Ganguli
Keyword(s):  
Neural Networks ◽  
Supervised Learning ◽  
Learning Rule ◽  
Nonlinear Problems ◽  
Spiking Neural Networks ◽  
Credit Assignment ◽  
Time Patterns ◽  
Spike Patterns ◽  
The Brain

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.

Supervised learning algorithms in the classification of plant populations with different degrees of kinship

2021 ◽  
Author(s):  
Leandro Skowronski ◽  
Paula Martin de Moraes ◽  
Mario Luiz Teixeira de Moraes ◽  
Wesley Nunes Gonçalves ◽  
Michel Constantino ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Learning Algorithms ◽  
Plant Populations ◽  
Supervised Learning Algorithms

Semi-supervised learning using multiple one-dimensional embedding based adaptive interpolation

2016 ◽  
Vol 14 (02) ◽  
pp. 1640002 ◽  
Author(s):  
Jianzhong Wang
Keyword(s):  
Supervised Learning ◽  
High Dimensional ◽  
One Dimensional ◽  
Large Size ◽  
Adaptive Interpolation

We propose a novel semi-supervised learning (SSL) scheme using adaptive interpolation on multiple one-dimensional (1D) embedded data. For a given high-dimensional dataset, we smoothly map it onto several different 1D sequences, so that the labeled subset is converted to a 1D subset for each of these sequences. Applying the cubic interpolation of the labeled subset, we obtain a subset of unlabeled points, which are assigned to the same label in all interpolations. Selecting a proportion of these points at random and adding them to the current labeled subset, we build a larger labeled subset for the next interpolation. Repeating the embedding and interpolation, we enlarge the labeled subset gradually, and finally reach a labeled set with a reasonable large size, based on which the final classifier is constructed. We explore the use of the proposed scheme in the classification of handwritten digits and show promising results.

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