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Imitation of Brain Processes by Artificial Brain System

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The following concepts play an important role in the operation of a system, which imitates the brain. It should be mentioned that sometimes the definitions listed below are used in slightly different ways by different investigators.

Spiking

Signals are communicated between neurons through voltage or current spikes. This communication is different from that used in current digital systems, in which the signals are binary, or an analogue implementation, which relies on the manipulation of continuous signals. Spiking signaling systems are time encoded and transmitted via “action potentials”.

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Plasticity

A conventional device has a unique response to a particular stimulus or input. In contrast, the typical neuromorphic architecture relies on changing the properties of an element or device depending on the past history. Plasticity is a key property that allows the complex neuromorphic circuits to be modified (“learn”) as they are exposed to different signals.

Fan-in/fan-out

In conventional computational circuits, the different elements generally are interconnected by a few connections between the individual devices. In the brain, however, the number of dendrites is several orders of magnitude larger (e.g., 10,000). Further research is needed to determine how essential this is to the fundamental computing model of neuromorphic systems.

Hebbian learning/dynamical resistance change

Long-term changes in the synapse resistance after repeated spiking by the presynaptic neuron. This is also sometimes referred to as spike time-dependent plasticity (STDP). An alternative characterization in Hebbian learning is “devices that fire together, wire together”.

Adaptability

Biological brains generally start with multiple connections out of which, through a selection or learning process, some are chosen and others abandoned. This process may be important for improving the fault tolerance of individual devices as well as for selecting the most efficient computational path. In contrast, in conventional computing the system architecture is rigid and fixed from the beginning.

Criticality

The brain typically must operate close to a critical point at which the system is plastic enough that it can be switched from one state to another, neither extremely stable nor very volatile. At the same time, it may be important for the system to be able to explore many closely lying states. In terms of materials science, for example, the system may be close to some critical state such as a phase transition. Neuromorphic Computing: From Materials to Systems Architecture

Accelerators

The ultimate construction of a neuromorphic–based thinking machine requires intermediate steps, working toward small-scale applications based on neuromorphic ideas. Some of these types of applications require combining sensors with some limited computation.

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