Behind the buzz and beyond the hype:
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Posted: Apr 04, 2014
(Nanowerk Spotlight) The human brain is often likened to a computer, but it differs from everyday computers in three important ways: it consumes very little power (only about 10-25 Watts), it works well even if components fail, and it seems to work without any software.
It consists of hundreds of billions of cells that form approximately one quadrillion connections and contains multiple, interacting levels of complexity. There are tens of thousands of different types of nerve cells, or neurons, each characterized by the unique pattern of genes they express, their shape and the connections they make with other cells.
Understanding how activity within neuronal circuits gives rise to higher cognitive processes such as language, emotions and consciousness is not understood yet and fully understanding how the brain works will require major technological breakthroughs.
Even 'only' simulating the brain will require massive computing power. Each simulated neuron requires the equivalent of a laptop computer. A model of the whole brain would have billions. Supercomputing technology is rapidly approaching a level where simulating the whole brain becomes a concrete possibility.
Constructing realistic simulations of the human brain is a key goal of the Human Brain Project, a massive European-led research project that commenced in 2013.
The Human Brain Project is a large-scale, scientific collaborative project, which aims to gather all existing knowledge about the human brain, build multi-scale models of the brain that integrate this knowledge and use these models to simulate the brain on supercomputers. The resulting "virtual brain" offers the prospect of a fundamentally new and improved understanding of the human brain, opening the way for better treatments for brain diseases and for novel, brain-like computing technologies.
Several years ago, another European project named FACETS (Fast Analog Computing with Emergent Transient States) completed an exhaustive study of neurons to find out exactly how they work, how they connect to each other and how the network can ‘learn’ to do new things. One of the outcomes of the project was PyNN, a simulator-independent language for building neuronal network models.
Scientists have great expectations that nanotechnologies will bring them closer to the goal of creating computer systems that can simulate and emulate the brain's abilities for sensation, perception, action, interaction and cognition while rivaling its low power consumption and compact size – basically a brain-on-a-chip. Already, scientists are working hard on laying the foundations for what is called neuromorphic engineering – a new interdisciplinary discipline that includes nanotechnologies and whose goal is to design artificial neural systems with physical architectures similar to biological nervous systems.
Several research projects funded with millions of dollars are at work with the goal of developing brain-inspired computer architectures or virtual brains: DARPA's SyNAPSE, the EU's BrainScaleS (a successor to FACETS), or the Blue Brain project (one of the predecessors of the Human Brain Project) at Switzerland's EPFL.
DARPA for instance, the U.S. military's research outfit known for projects that are pushing the envelope on what is technologically possible, since 2008 has a program called SyNAPSE that is trying to develop electronic neuromorphic machine technology that scales to biological levels.
Developing electronic neuromorphic machine technology that scales to biological levels - the yellow star depicts the goal of the SyNAPSE program. (Image: DARPA)
Programmable machines are limited not only by their computational capacity, but also by an architecture requiring (human-derived) algorithms to both describe and process information from their environment. In contrast, biological neural systems (e.g., brains) autonomously process information in complex environments by automatically learning relevant and probabilistically stable features and associations. Since real world systems are always many body problems with infinite combinatorial complexity, neuromorphic electronic machines would be preferable in a host of applications – but useful and practical implementations do not yet exist.
The initial phase of SyNAPSE developed nanometer-scale electronic synaptic components capable of adapting connection strength between two neurons in a manner analogous to that seen in biological systems and simulated the utility of these synaptic components in core microcircuits that support the overall system architecture.
Continuing efforts will focus on hardware development through microcircuit development, fabrication process development, single chip system development, and multi-chip system development.
Most approaches in neuroinformatics are limited to the development of neural network models on conventional computers or aim to simulate complex nerve networks on supercomputers. Few pursue an approach like researchers at Ecole Polytechnique Fédérale de Lausanne to develop electronic circuits that are comparable to a real brain in terms of size, speed, and energy consumption (read more: "Chips that mimic the brain").
Independent from military-inspired research like DARPA's, nanotechnology researchers in France have developed a hybrid nanoparticle-organic transistor that can mimic the main functionalities of a synapse. This organic transistor, based on pentacene and gold nanoparticles and termed NOMFET (Nanoparticle Organic Memory Field-Effect Transistor), has opened the way to new generations of neuro-inspired computers, capable of responding in a manner similar to the nervous system (read more: "Scientists use nanotechnology to try building computers modeled after the brain").
One of the key components of any neuromorphic effort, and its starting point, is the design of artificial synapses. Synapses dominate the architecture of the brain and are responsible for massive parallelism, structural plasticity, and robustness of the brain. They are also crucial to biological computations that underlie perception and learning. Therefore, a compact nanoelectronic device emulating the functions and plasticity of biological synapses will be the most important building block of brain-inspired computational systems.
The human brain contains vastly more synapses than neurons – by a factor of about 10,000 – and therefore it is necessary to develop a nanoscale, low power, synapse-like device if scientists want to scale neuromorphic circuits towards the human brain level.
In 2011, a team at Stanford University demonstrates a new single element nanoscale device, based on the successfully commercialized phase change material technology, emulating the functionality and the plasticity of biological synapses.
In their work, the Stanford team demonstrated a single element electronic synapse with the capability of both the modulation of the time constant and the realization of the different synaptic plasticity forms while consuming picojoule level energy for its operation (read more: "Brain-inspired computing with nanoelectronic programmable synapses").
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Researchers have also suggested that memristor devices are capable of emulating the biological synapses with properly designed CMOS neuron components. A memristor is a two-terminal electronic device whose conductance can be precisely modulated by charge or flux through it. It has the special property that its resistance can be programmed (resistor) and subsequently remains stored (memory).
One research project already demonstrated that a memristor can connect conventional circuits and support a process that is the basis for memory and learning in biological systems (read more: "Nanotechnology's road to artificial brains").
A completely different – and revolutionary – human brain model has been designed by researchers in Japan who introduced the concept of a new class of computer which does not use any circuit or logic gate. This artificial brain-building project differs from all others in the world. It does not use logic-gate based computing within the framework of Turing. The decision-making protocol is not a logical reduction of decision rather projection of frequency fractal operations in a real space, it is an engineering perspective of Gödel’s incompleteness theorem.