A new generation of neural network models — called spiking neural networks
For every thought or behavior, the brain erupts in a riot of activity, as thousands of cells communicate via electrical and chemical signals. Each nerve cell influences others within an intricate, interconnected neural network. And connections between brain cells change over time in response to our environment.
Despite supercomputer advances, the human brain remains the most flexible, efficient information processing device in the world. Its exceptional performance inspires researchers to study and imitate it as an ideal of computing power.
Artificial neural networks
Computer models built to replicate how the brain processes, memorizes and/or retrieves information are called artificial neural networks. For decades, engineers and computer scientists have used artificial neural networks as an effective tool in many real-world problems involving tasks such as classification, estimation and control.
However, artificial neural networks do not take into consideration some of the basic characteristics of the human brain such as signal transmission delays between neurons, membrane potentials and synaptic currents.
A new generation of neural network models — called spiking neural networks — are designed to better model the dynamics of the brain, where neurons initiate signals to other neurons in their networks with a rapid spike in cell voltage. In modeling biological neurons, spiking neural networks may have the potential to mimick brain activities in simulations, enabling researchers to investigate neural networks in a biological context.
With funding from the National Science Foundation, Silvia Ferrari of the Laboratory for Intelligent Systems and Controls at Duke University uses a new variation of spiking neural networks to better replicate the behavioral learning processes of mammalian brains.
Behavioral learning involves the use of sensory feedback, such as vision, touch and sound, to improve motor performance and enable people to respond and quickly adapt to their changing environment.
“Although existing engineering systems are very effective at controlling dynamics, they are not yet capable of handling unpredicted damages and failures handled by biological brains,” Ferrari said.
How to teach an artificial brain
Ferrari’s team is applying the spiking neural network model of learning on the fly to complex, critical engineering systems, such as aircraft and power plants, with the goal of making them safer, more cost-efficient and easier to operate.
The team has constructed an algorithm that teaches spiking neural networks which information is relevant and how important each factor is to the overall goal. Using computer simulations, they’ve demonstrated the algorithm on aircraft flight control and robot navigation.
They started, however, with an insect.
The Latest on: Spiking neural network
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The Latest on: Spiking neural network
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- Putting the Neural back into Networkson January 15, 2020 at 7:26 am
In this installment we’ll see how to build and train a feed-forward spiking neural network to solve a temporal signal transformation task. Rockpool is a new open-source Python package for ...
- Putting the Neural back into Networkson January 15, 2020 at 7:21 am
In the final slide of his ISSCC 2019 keynote , Yann LeCun [2, 3, 4] (that’s “Mr CNN” to you) said he was skeptical about the usefulness of spiking neural networks, as almost a throwaway ...
- Brian2GeNN: accelerating spiking neural network simulations with graphics hardwareon January 14, 2020 at 4:00 pm
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- BrainChip and Tata Consultancy Services (TCS) jointly Present a Demonstration Featuring Its Akida Neuromorphic Technology Platform at NeurIPS 2019on December 5, 2019 at 2:44 am
This will showcase the fast and lightweight unsupervised live learning capability of the spiking neural network (SNN) and the Akida neuromorphic chip, which takes much less data than a traditional ...
- BrainChip Introduces a Powerful Neural Network Converteron June 11, 2019 at 11:24 am
Landmark innovation in Neural Network design enables the next generation of AI Edge devices Convert existing Convolutional Neural Networks to high performance, low power event-based Spiking Neural ...
- 'Human brain' supercomputer with 1 million processors switched on for first timeon February 16, 2019 at 2:34 am
The newly formed million-processor-core ‘Spiking Neural Network Architecture’ or ‘SpiNNaker’ machine is capable of completing more than 200 million million actions per second, with each of its chips ...
- An attempt to fit all parameters of a dynamical recurrent neural network from sensory neural spiking dataon June 28, 2018 at 5:00 pm
The employed model is a continuous time recurrent neural network (CTRNN) which is a member of models with known universal approximation features. This feature of the recurrent dynamical neuron network ...
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