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The use of neural hardware supports the real time simulation of many more neurons, providing a platform for exploration of more complex simulated neural systems.
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Thus, segregation of neurons into monocular regions within a optic lobe might advocate efficient usage of constrained neural hardware [48], [58] and improve discriminative capabilities [52], [53], [56].
When no one is looking, drop to the ground in a wood: sniff, feel, and start to use a bit more of your neural hardware and software.
Those weights are then used by a second set of neural network hardware to implement pattern recognition - this is referred to as the inference stage.
After presenting the issues and context involved in the design and use of hardware-based spiking neural networks, we describe the analog neuromimetic integrated circuits which form the core of the platform.
The state of the art in hardware design is the use of hardware description languages such as VHDL.
The Brain investigates ways we might "hack" our neural hardware to substitute and add senses.
Google's secret for the speed of instant translations, is its use of convolutional neural networks.
Two objectives: performance isolation and efficient use of hardware.
The use of hardware acceleration has shown promise in literature [ 52, 53].
Some use a combination of neural network architectures.
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