Click on image for link to article. Artificial brains learn to adapt
May 16, 2014
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 mimic brain activities in simulations, enabling researchers to investigate neural networks in a biological context.
May 16, 2014
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 mimic brain activities in simulations, enabling researchers to investigate neural networks in a biological context.
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