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. 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. Researchers
at the Laboratory for Intelligent Systems and Controls at Duke University use a
new variation of spiking neural networks to better replicate the behavioral
learning processes of mammalian brains.
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