Ssm Winter 2007 Engtech Brainchip

engineering + technology

National Institute on Aging

Brain on a Chip Computer Architecture Branches Out by Camille Sindhu

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he next revolution in computer architecture may not come from research in computer science, but rather from the bench of a biology laboratory. Researchers working at the intersection of neurobiology and electrical engineering are already capable of building artificial analogs of biological sensory systems in silicon, but difficulties arise in emulating the more complex neural networks of brain tissue. Photo Credit: Dr. Boahen Dr. Kwabena Boahen, Associate Professor in the Department of Bioengineering, believes these difficulties are rooted in the lack of extensive knowledge about brain function. Overcoming this hurdle has catalyzed a gradual shift in his research goals to design techniques that enable the study of brain function on a much deeper level than ever before. Dr. Kwabena Boahen

A medical illustration showing the structre of a typical neuron.

making practical and informative simulations nearly impossible. The startling difficulty in simulation of neural activity is due to fundamental differences between the von Neumann model of computer architecture, the design upon which most computers today are built, and the neural architecture of an animal brain. Von Neumann machines operate in a sequential manner, executing step-by-step a programmed

Fortunately, biological evolution has already created a computing paradigm whose design is dictated by energy efficiency: the brain.

Neural vs. Von Neumann Computation To tease apart the intricate puzzle of brain function, set of instructions, in marked contrast to the brain’s highly experimentation is required at the molecular and parallel and interconnected architecture. These differences biochemical levels, a nearly impossible task to carry out in are reconciled by the Boahen lab’s Neurogrid computer: a vivo. Instead, experiments are conducted and analyzed using neurobiology lab on a chip born from combining knowledge a computational model of the brain. Since all the computing of neuroscience, computer architecture and electrical done by the brain is via action potentials triggered by the flow engineering. This device provides previously unattainable of ions through selective channels, understanding higher insights into brain function by modeling the underlying brain function necessitates a thorough grasp of ion channel architecture of an animal brain. dynamics. An ideal experiment would Photo Credit: Dr. Boahen Right: Ionic currents measured in neural cells therefore manipulate Below: Ionic current modeled in a three-transistor circuit variables such as ion type, channel characteristics and cell type, then observe the changes in activity or capability of the neural network as a whole. Unfortunately, simulation of one second of neural activity at the level of molecular detail required by a biologist takes a modern Above: The voltage pulse characteristic of the action potential in a neuron (left) can be mimicked by the capacitor/transistor supercomputer one circuit of the silicon neuron (right). hour and 20 minutes,

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engineering + technology

neurobiologists with a brain on a chip, they are actually part of a much more profound shift in computing paradigms. Today we know how to build machines that compute precisely and with blazing speed. The challenge is to do it efficiently in terms of power and space. Fortunately, biological evolution has already created a computing paradigm whose design is dictated by energy efficiency: the brain. When comparing the power used per computation by a brain versus that of a modern computer, one finds that computers use up to a staggering billion times more energy.

Photo Credit: Dr. Boahen

Six-transistor circuit with the primary characteristics of silicon neurons, consisting of a membrane capacitance (center) supplied by input current, a positive-feedback loop delivering sodium current, and a leak current returning membrane to resting potential. Iin is the supplied input current, Ina is the positive-feedback loop that delivers sodium current to the membrane voltage, and Vmem is the resting membrane potential.

Mimicking the Brain The fundamental component of the Neurogrid computer is not a logic gate like in most computing devices, but a “silicon neuron” -- an electrical circuit of transistors and capacitors arranged in a pattern that mimics the voltage pulse of a real neuron’s action potential. These voltage spikes are read and processed by an external computer running a software application that assembles and analyzes the generated data. This arrangement enables an incredible versatility in experimental analysis, from selecting a single cell in the network and plotting its firing pattern to examining the activity of the whole array or any level of complexity in between. Any change in connectivity patterns or cell types can easily be programmed and the experiment can be conducted again. In essence, Neurogrid models the layered organization of the cortex, with each cortical layer corresponding to another

“Say you’re a biologist and you come and tell me you’ve figured out how the brain works—well I’m going to ask you, how does it do it with just 10 watts?” -Boahen chip. This type of parallel architecture facilitates studying large systems of neural cells while taking into account the characteristics of individual neurons, thereby elucidating the connection between molecular events at the ion channel level to macroscopic changes in brain function.

The Need for a New Computational Paradigm While the applications of neuromorphic processor technologies like Neurogrid may appear limited to providing

layout design: Jessica Chia-Rong Lee

When comparing the power used per computation by a brain versus that of a modern computer, one finds that computers use up to a staggering billion times more energy. Boahen is convinced that the key to reducing this massive energy consumption is to approach computer design in a way that emulates the brain’s organization. “This question is really a scientific one, not just an engineering one,” Boahen explains. “Say you’re a biologist and you come and tell me you’ve figured out how the brain works—well I’m going to ask you, how does it do it with just 10 watts?” Any system seeking to mediate human-machine, real world, real time interactions must possess the same computing power as the brain, and in a similarly compact package running on comparable amounts of energy.

Neurogrid’s Future Boahen insists that computers like the Neurogrid won’t replace our current von Neumann machines, which are already very efficient for current purposes. Neuromorphic processors would be complementary, specialized modules called upon in situations where adaptability, not precision, is required. The concept of a computer that can react to its surroundings rather than execute a sequence of programmed steps, as well as last longer on a small, portable energy supply, is dictating evolution of computation. Surprising as it may be, the solution to our computational challenges today is looking more and more like what biology has already designed. S CAMILLE SINDHU is a fourth-year Biological Sciences major interested in research at the intersection of molecular biology and engineering. To Learn More Visit Dr. Boahen’s lab page: http://www.stanford.edu/ group/brainsinsilicon/index.html Read Scientific American (May 2005) “Mimic the nervous system with neuromorphic chips.”

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