scholarly journals Approximating Back-propagation for a Biologically Plausible Local Learning Rule in Spiking Neural Networks

2019 ◽  
Author(s):  
Amar Shrestha ◽  
Haowen Fang ◽  
Qing Wu ◽  
Qinru Qiu
Keyword(s):  
Neural Networks ◽  
Back Propagation ◽  
Learning Rule ◽  
Spiking Neural Networks ◽  
Local Learning

scholarly journals Toward One-Shot Learning in Neuroscience-Inspired Deep Spiking Neural Networks

2019 ◽  
Author(s):  
Faramarz Faghihi ◽  
Hossein Molhem ◽  
Ahmed A. Moustafa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Back Propagation ◽  
Learning Rule ◽  
Spiking Neural Networks ◽  
Spiking Neural Network ◽  
Essential Information ◽  
Information Channels ◽  
Training Examples

AbstractConventional deep neural networks capture essential information processing stages in perception. Deep neural networks often require very large volume of training examples, whereas children can learn concepts such as hand-written digits with few examples. The goal of this project is to develop a deep spiking neural network that can learn from few training trials. Using known neuronal mechanisms, a spiking neural network model is developed and trained to recognize hand-written digits with presenting one to four training examples for each digit taken from the MNIST database. The model detects and learns geometric features of the images from MNIST database. In this work, a novel biological back-propagation based learning rule is developed and used to a train the network to detect basic features of different digits. For this purpose, randomly initialized synaptic weights between the layers are being updated. By using a neuroscience inspired mechanism named ‘synaptic pruning’ and a predefined threshold, some of the synapses through the training are deleted. Hence, information channels are constructed that are highly specific for each digit as matrix of synaptic connections between two layers of spiking neural networks. These connection matrixes named ‘information channels’ are used in the test phase to assign a digit class to each test image. As similar to humans’ abilities to learn from small training trials, the developed spiking neural network needs a very small dataset for training, compared to conventional deep learning methods checked on MNIST dataset.

Chapter 9. Spike-Based Symbolic Computations on Bit Strings and Numbers

2021 ◽  
Author(s):  
Ceca Kraišniković ◽  
Wolfgang Maass ◽  
Robert Legenstein
Keyword(s):  
Neural Networks ◽  
Energy Efficient ◽  
Learning Rule ◽  
Spiking Neural Networks ◽  
New Paradigm ◽  
Symbolic Computations ◽  
Neuromorphic Hardware ◽  
State Of Research ◽  
Higher Cognitive Functions ◽  
The Brain

The brain uses recurrent spiking neural networks for higher cognitive functions such as symbolic computations, in particular, mathematical computations. We review the current state of research on spike-based symbolic computations of this type. In addition, we present new results which show that surprisingly small spiking neural networks can perform symbolic computations on bit sequences and numbers and even learn such computations using a biologically plausible learning rule. The resulting networks operate in a rather low firing rate regime, where they could not simply emulate artificial neural networks by encoding continuous values through firing rates. Thus, we propose here a new paradigm for symbolic computation in neural networks that provides concrete hypotheses about the organization of symbolic computations in the brain. The employed spike-based network models are the basis for drastically more energy-efficient computer hardware – neuromorphic hardware. Hence, our results can be seen as creating a bridge from symbolic artificial intelligence to energy-efficient implementation in spike-based neuromorphic hardware.

scholarly journals Biologically Plausible Sequence Learning with Spiking Neural Networks

2020 ◽  
Vol 34 (02) ◽  
pp. 1316-1323
Author(s):  
Zuozhu Liu ◽  
Thiparat Chotibut ◽  
Christopher Hillar ◽  
Shaowei Lin
Keyword(s):  
Neural Networks ◽  
Sequence Learning ◽  
Nervous Activity ◽  
Learning Rule ◽  
Generative Models ◽  
Spike Timing ◽  
Hopfield Network ◽  
Spiking Neural Networks ◽  
Theoretical Ground ◽  
Time Model

Motivated by the celebrated discrete-time model of nervous activity outlined by McCulloch and Pitts in 1943, we propose a novel continuous-time model, the McCulloch-Pitts network (MPN), for sequence learning in spiking neural networks. Our model has a local learning rule, such that the synaptic weight updates depend only on the information directly accessible by the synapse. By exploiting asymmetry in the connections between binary neurons, we show that MPN can be trained to robustly memorize multiple spatiotemporal patterns of binary vectors, generalizing the ability of the symmetric Hopfield network to memorize static spatial patterns. In addition, we demonstrate that the model can efficiently learn sequences of binary pictures as well as generative models for experimental neural spike-train data. Our learning rule is consistent with spike-timing-dependent plasticity (STDP), thus providing a theoretical ground for the systematic design of biologically inspired networks with large and robust long-range sequence storage capacity.

HOW TO MAKE GOOD USE OF MULTILAYER NEURAL NETWORKS

1995 ◽  
Vol 03 (04) ◽  
pp. 1177-1191 ◽  
Author(s):  
HÉLÈNE PAUGAM-MOISY
Keyword(s):  
Neural Networks ◽  
Open Problem ◽  
Back Propagation ◽  
Learning Rule ◽  
Multilayer Network ◽  
Odor Recognition ◽  
Short Summary ◽  
Multilayer Neural Networks ◽  
Hidden Layer ◽  
Universal Approximators

This article is a survey of recent advances on multilayer neural networks. The first section is a short summary on multilayer neural networks, their history, their architecture and their learning rule, the well-known back-propagation. In the following section, several theorems are cited, which present one-hidden-layer neural networks as universal approximators. The next section points out that two hidden layers are often required for exactly realizing d-dimensional dichotomies. Defining the frontier between one-hidden-layer and two-hidden-layer networks is still an open problem. Several bounds on the size of a multilayer network which learns from examples are presented and we enhance the fact that, even if all can be done with only one hidden layer, more often, things can be done better with two or more hidden layers. Finally, this assertion 'is supported by the behaviour of multilayer neural networks in two applications: prediction of pollution and odor recognition modelling.

scholarly journals Back-propagation learning in deep Spike-By-Spike networks

2019 ◽  
Author(s):  
David Rotermund ◽  
Klaus R. Pawelzik
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Action Potentials ◽  
Back Propagation ◽  
Learning Rule ◽  
Building Blocks ◽  
Classification Performance ◽  
Generative Models ◽  
Learning Method ◽  
Discrete Action

ABSTRACTNeural networks are important building blocks in technical applications. These artificial neural networks (ANNs) rely on noiseless continuous signals in stark contrast to the discrete action potentials stochastically exchanged among the neurons in real brains. A promising approach towards bridging this gap are the Spike-by-Spike (SbS) networks which represent a compromise between non-spiking and spiking versions of generative models that perform inference on their inputs. What is still missing are algorithms for finding weight sets that would optimize the output performances of deep SbS networks with many layers.Here, a learning rule for hierarchically organized SbS networks is derived. The properties of this approach are investigated and its functionality demonstrated by simulations. In particular, a Deep Convolutional SbS network for classifying handwritten digits (MNIST) is presented. When applied together with an optimizer this learning method achieves a classification performance of roughly 99.3% on the MNIST test data. Thereby it approaches the benchmark results of ANNs without extensive parameter optimization. We envision that with this learning rule SBS networks will provide a new basis for research in neuroscience and for technical applications, especially when they become implemented on specialized computational hardware.

scholarly journals Modelling an Adaptive Learning System Using Artificial Intelligence

Webology ◽  
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.

Compact Hardware Synthesis of Stochastic Spiking Neural Networks

2019 ◽  
Vol 29 (08) ◽  
pp. 1950004 ◽  
Author(s):  
Fabio Galán-Prado ◽  
Alejandro Morán ◽  
Joan Font ◽  
Miquel Roca ◽  
Josep L. Rosselló
Keyword(s):  
Neural Networks ◽  
Processing Speed ◽  
Back Propagation ◽  
High Volume ◽  
High Density ◽  
Digital Design ◽  
Spiking Neural Networks ◽  
Biologically Inspired ◽  
Proposed Model ◽  
Technical Interest

Spiking neural networks (SNN) are able to emulate real neural behavior with high confidence due to their bio-inspired nature. Many designs have been proposed for the implementation of SNN in hardware, although the realization of high-density and biologically-inspired SNN is currently a complex challenge of high scientific and technical interest. In this work, we propose a compact digital design for the implementation of high-volume SNN that considers the intrinsic stochastic processes present in biological neurons and enables high-density hardware implementation. The proposed stochastic SNN model (SSNN) is compared with previous SSNN models, achieving a higher processing speed. We also show how the proposed model can be scaled to high-volume neural networks trained by using back propagation and applied to a pattern classification task. The proposed model achieves better results compared with other recently-published SNN models configured with unsupervised STDP learning.

Sign in / Sign up

Export Citation Format

Share Document