neuromorphic technology seminars ppt
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neuromorphic technology seminar ppt

Neuromorphic engineering, also known as neuromorphic computing,[1][2][3] is a concept developed by Carver Mead,[citation needed] in the late 1980s, describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system. In recent times the term neuromorphic has been used to describe analog, digital, and mixed-mode analog/digital VLSI and software systems that implement models of neural systems (for perception, motor control, or multisensory integration). The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, threshold switches and transistors.[4][5]

A key aspect of neuromorphic engineering is understanding how the morphology of individual neurons, circuits, applications, and overall architectures creates desirable computations, affects how information is represented, influences robustness to damage, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change.

Neuromorphic engineering is an interdisciplinary subject that takes inspiration from biology, physics, mathematics, computer science and electronic engineering to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous systems.

As we begin to move towards a unified understanding of the brain, one approach that will lead to additional benefits beyond this understanding is to build computing systems that function on the same principles as those of the human brain, i.e. neuromorphic computing. Emulating how neural systems and microcircuits work will ultimately lead to more efficient computer systems with greater processing capability and lower power consumption as well as contributing to our understanding of the brain itself [following Feynman's maxim: "What I cannot create I do not understand"].

The Neuromorphic Computing Platform (NCP) consists of two complementary and configurable neuromorphic computing systems, based on custom hardware designs and designed to emulate neural microcircuits and apply brain-like principles in machine learning and cognitive computing, i.e. principles that will allow the machine to learn in the way that the brain does rather than be programmed like a normal computer. To do this, the NCP uses state-of-the-art electronic component and circuit technologies and incorporates new knowledge gained from other areas of neuroscience research, e.g. experimental neuroscience, theoretical neuroscience and brain modelling.

The NCP provides remote access to large-scale neuromorphic computing systems based in Manchester, UK and Heidelberg, Germany. These constitute the only neuromorphic systems in the world capable of running simulations of neural circuit systems with state-of-the-art models of neurons, synapses and plasticity in either real time or accelerated time (i.e. 10,000 times faster than real time). The Manchester system is the Many-Core Neuromorphic System, built on the work of the UK SpiNNaker project. This system is based on individual chips, each with 18 ARM cores, consisting of 500,000 processors in total. A single chip can simulate up to 16,000 neurons with 8 million plastic synapses running in real time with an energy budget of 1W. The Heidelberg system is the Physical Model Neuromorphic System, which builds on capabilities developed in the European FACETS and BrainScaleS projects. This system has 20 silicon wafers in 180nm process technology, with circuit models of 4 million neurons and 1 billion synaptic connections. Rather than using pre-programmed code, the system evolves according to the physical properties of the electronics. Both systems use the PyNN network description language, developed to build neuronal network models for software simulators and neuromorphic systems. In addition to these two large-scale systems, small-scale portable evaluation systems are available for educational or research purposes, giving users the advantage of a fully autonomous system that can be used offline.

These powerful neuromorphic systems have unparalleled capabilities to allow the study of key features of brain characteristics, such as learning, plasticity and development. The NCP will therefore allow academic researchers, industry developers and students to perform experiments with configurable, customised systems in computational neuroscience, machine learning and cognitive computing. The systems are optimised for spike-based communication, i.e. spiking neural networks (SNNs), which are models of neural networks that incorporate the concept of time into the system as well as neural and synaptic states. Users can examine SNNs of their choice, including simplified versions of models developed in the Brain Simulation Platform, or can run generic circuit models based on theoretical work. In addition to the main fields of computational neuroscience and cognitive computing, the NCP will also be particularly useful in any area involving large volumes of data where causal relationships or structures need to be identified. This can include industry research such as telecommunications or finance, or the automotive or robotics industries. The use of the Platform in educational institutions is also of special interest, as it can provide students with additional skills beyond traditional computing.
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