Kwabena Boahen

Last updated
Kwabena Boahen
Born
Kwabena Adu Boahen

22 September 1964 (1964-09-22) (age 59)
Accra, Ghana
NationalityGhanaian
CitizenshipGhana / United States
Alma mater Johns Hopkins University
Caltech
Known for Bioengineering
Parent
Scientific career
Fields Electronic Engineer
Institutions University of Pennsylvania
Stanford University
Doctoral advisor Carver Mead
Website profiles.stanford.edu/kwabena-boahen

Kwabena Adu Boahen (born 22 September 1964) is a Ghanaian-born Professor of Bioengineering and Electrical Engineering at Stanford University. [1] He previously taught at the University of Pennsylvania.

Contents

Education and early life

Kwabena Boahen was born on 22 September 1964, in Accra, Ghana. [2] He attended secondary school at Mfantsipim School in Cape Coast, Ghana, and at the Presbyterian Boys' Senior High School in Accra. While at Mfantsipim, he invented the corn-planting machine that won the national science competition and graduated as the valedictorian of the Class of 1981.

He received his B.S. and M.S. in electrical engineering in 1989 from Johns Hopkins University and his PhD in computation and neural systems in 1997 from the California Institute of Technology, where he was advised by Carver Mead. For his PhD thesis, Boahen designed and fabricated a silicon chip emulating the functioning of the retina. [3] Boahen's father, Albert Adu Boahen, was a professor of history at the University of Ghana and an advocate for democracy in Ghana.

Career

After completing his PhD, Boahen joined the faculty of University of Pennsylvania, where he held the Skirkanich Term Junior Chair. In 2005, he moved to Stanford University and is currently the director of the Brains in Silicon Lab. [4]

Research

Boahen is widely regarded as one of the pioneers of neuromorphic engineering, a field founded by Carver Mead in the 1980s. In contrast to the field of artificial intelligence, which merely takes inspiration from the brain, neuromorphic engineers seek to develop a new computing paradigm based on the brain's organizing principles. The brain employs a computing paradigm that is fundamentally different from digital computers. Instead of using digital signals for computation as well as communication, the brain uses analog signals (i.e., graded dendritic potentials) for computation and digital signals (i.e., all-or-none axonal potentials) for communication. Having explored this unique hybrid of digital and analogue techniques over the past three decades, neuromorphic engineers are now beginning to understand and exploit its advantages. Their potential work applications include brain-machine interfaces, autonomous robots, and machine intelligence.

Boahen often speaks of the promise of efficient computing as an inspiration for his work, writing: "A typical room-size supercomputer weighs approximately 1,000 times more, occupies 10,000 times more space and consumes a millionfold more power than does the cantaloupe-size lump of neural tissue that makes up the brain." [5]

With contributions in circuit design, chip architecture, and neuroscience, Boahen has brought together ideas from many disciplines to build novel computer chips that emulate the brain. Widely renowned for his engineering accomplishments, Boahen was named an IEEE fellow in 2016. Specific contributions throughout his career include the development of the current-mode subthreshold CMOS circuit design paradigm, the address-event approach to communicating spikes between neuromorphic chips, and the scalable design of multi-chip systems. Boahen's chips are mixed-mode: they employ analog circuits for computation and digital circuits for communication.

Boahen's work has demonstrated that neuromorphic computer chips are capable of reproducing many types of brain phenomena across a large range of scales. Examples include ion-channel dynamics [6] (individual molecules), excitable membrane behavior (individual neurons), the orientation tuning of neurons in Visual Cortex [7] (individual cortical columns), and neural synchrony [8] (individual cortical areas). Utilizing these breakthroughs, Boahen's Stanford lab built the first neuromorphic system with one million spiking neurons (and billions of synapses). [9] This system, Neurogrid, emulates networks of cortical neurons in real time while consuming only a few watts of power. In contrast, simulating one million interconnected cortical neurons in real-time using traditional super-computers requires as much power as several thousand households.

Boahen popularized the word retinomorphic, in reference to optical sensors inspired by biological retinae. [10]

Honours

Related Research Articles

<span class="mw-page-title-main">Neuroscience</span> Scientific study of the nervous system

Neuroscience is the scientific study of the nervous system, its functions and disorders. It is a multidisciplinary science that combines physiology, anatomy, molecular biology, developmental biology, cytology, psychology, physics, computer science, chemistry, medicine, statistics, and mathematical modeling to understand the fundamental and emergent properties of neurons, glia and neural circuits. The understanding of the biological basis of learning, memory, behavior, perception, and consciousness has been described by Eric Kandel as the "epic challenge" of the biological sciences.

Computational neuroscience is a branch of neuroscience which employs mathematics, computer science, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system.

An artificial neuron is a mathematical function conceived as a model of biological neurons in a neural network. Artificial neurons are the elementary units of artificial neural networks. The artificial neuron is a function that receives one or more inputs, applies weights to these inputs, and sums them to produce an output.

Bio-inspired computing, short for biologically inspired computing, is a field of study which seeks to solve computer science problems using models of biology. It relates to connectionism, social behavior, and emergence. Within computer science, bio-inspired computing relates to artificial intelligence and machine learning. Bio-inspired computing is a major subset of natural computation.

Neuromorphic computing is an approach to computing that is inspired by the structure and function of the human brain. A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations. In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems. The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, spintronic memories, threshold switches, transistors, among others. Training software-based neuromorphic systems of spiking neural networks can be achieved using error backpropagation, e.g., using Python based frameworks such as snnTorch, or using canonical learning rules from the biological learning literature, e.g., using BindsNet.

<span class="mw-page-title-main">Carver Mead</span> American scientist and engineer

Carver Andress Mead is an American scientist and engineer. He currently holds the position of Gordon and Betty Moore Professor Emeritus of Engineering and Applied Science at the California Institute of Technology (Caltech), having taught there for over 40 years.

<span class="mw-page-title-main">Wetware computer</span> Computer composed of organic material

A wetware computer is an organic computer composed of organic material "wetware" such as "living" neurons. Wetware computers composed of neurons are different than conventional computers because they use biological materials, and offer the possibility of substantially more energy-efficient computing. While a wetware computer is still largely conceptual, there has been limited success with construction and prototyping, which has acted as a proof of the concept's realistic application to computing in the future. The most notable prototypes have stemmed from the research completed by biological engineer William Ditto during his time at the Georgia Institute of Technology. His work constructing a simple neurocomputer capable of basic addition from leech neurons in 1999 was a significant discovery for the concept. This research was a primary example driving interest in creating these artificially constructed, but still organic brains.

<span class="mw-page-title-main">Stephen Grossberg</span> American scientist (born 1939)

Stephen Grossberg is a cognitive scientist, theoretical and computational psychologist, neuroscientist, mathematician, biomedical engineer, and neuromorphic technologist. He is the Wang Professor of Cognitive and Neural Systems and a Professor Emeritus of Mathematics & Statistics, Psychological & Brain Sciences, and Biomedical Engineering at Boston University.

<span class="mw-page-title-main">Spiking neural network</span> Artificial neural network that mimics neurons

Spiking neural networks (SNNs) are artificial neural networks (ANN) that more closely mimic natural neural networks. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not transmit information at each propagation cycle, but rather transmit information only when a membrane potential—an intrinsic quality of the neuron related to its membrane electrical charge—reaches a specific value, called the threshold. When the membrane potential reaches the threshold, the neuron fires, and generates a signal that travels to other neurons which, in turn, increase or decrease their potentials in response to this signal. A neuron model that fires at the moment of threshold crossing is also called a spiking neuron model.

Neurorobotics is the combined study of neuroscience, robotics, and artificial intelligence. It is the science and technology of embodied autonomous neural systems. Neural systems include brain-inspired algorithms, computational models of biological neural networks and actual biological systems. Such neural systems can be embodied in machines with mechanic or any other forms of physical actuation. This includes robots, prosthetic or wearable systems but also, at smaller scale, micro-machines and, at the larger scales, furniture and infrastructures.

<span class="mw-page-title-main">Misha Mahowald</span> American computational neuroscientist

Michelle Anne Mahowald was an American computational neuroscientist in the emerging field of neuromorphic engineering. In 1996 she was inducted into the Women in Technology International Hall of Fame for her development of the Silicon Eye and other computational systems. She died by suicide at age 33.

A physical neural network is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse or a higher-order (dendritic) neuron model. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based approaches. More generally the term is applicable to other artificial neural networks in which a memristor or other electrically adjustable resistance material is used to emulate a neural synapse.

Models of neural computation are attempts to elucidate, in an abstract and mathematical fashion, the core principles that underlie information processing in biological nervous systems, or functional components thereof. This article aims to provide an overview of the most definitive models of neuro-biological computation as well as the tools commonly used to construct and analyze them.

<span class="mw-page-title-main">Dharmendra Modha</span> American computer scientist

Dharmendra S. Modha is an Indian American manager and lead researcher of the Cognitive Computing group at IBM Almaden Research Center. He is known for his pioneering works in Artificial Intelligence and Mind Simulation. In November 2009, Modha announced at a supercomputing conference that his team had written a program that simulated a cat brain. He is the recipient of multiple honors, including the Gordon Bell Prize, given each year to recognize outstanding achievement in high-performance computing applications. In November 2012, Modha announced on his blog that using 96 Blue Gene/Q racks of the Lawrence Livermore National Laboratory Sequoia supercomputer, a combined IBM and LBNL team achieved an unprecedented scale of 2.084 billion neurosynaptic cores containing 530 billion neurons and 137 trillion synapses running only 1542× slower than real time. In August 2014 a paper describing the TrueNorth Architecture, "the first-ever production-scale 'neuromorphic' computer chip designed to work more like a mammalian brain than" a processor was published in the journal Science. TrueNorth project culminated in a 64 million neuron system for running deep neural network applications.

<span class="mw-page-title-main">Neurogrid</span>

Neurogrid is a piece of computer hardware that is designed specifically for simulation of biological brains. It uses analog computation to emulate ion channel activity, and digital communication to softwire structured connectivity patterns. Neurogrid simulates one million neurons and six billion synapses in real time. The neurons spike at a rate of ten times a second. In terms of the total number of simulated neurons, it rivals simulations done by the Blue Brain Project. However, by running the simulation of whole neurons, instead of simulation on molecular level, it needs only one millionth of Blue Brain's power. The entire board consumes less than two watts of electrical energy.

<span class="mw-page-title-main">Massimiliano Versace</span>

Massimiliano Versace is the co-founder and the CEO of Neurala Inc, a Boston-based company building Artificial Intelligence emulating brain function in software and used in automating the process of visual inspection in manufacturing. He is also the founding Director of the Boston University Neuromorphics Lab. Massimiliano Versace is a Fulbright scholar and holds two PhD in Experimental Psychology from the University of Trieste, Italy and Cognitive and Neural Systems from Boston University, USA. He obtained his BSc from the University of Trieste, Italy.

The Computation and Neural Systems (CNS) program was established at the California Institute of Technology in 1986 with the goal of training Ph.D. students interested in exploring the relationship between the structure of neuron-like circuits/networks and the computations performed in such systems, whether natural or synthetic. The program was designed to foster the exchange of ideas and collaboration among engineers, neuroscientists, and theoreticians.

<span class="mw-page-title-main">SpiNNaker</span>

SpiNNaker is a massively parallel, manycore supercomputer architecture designed by the Advanced Processor Technologies Research Group (APT) at the Department of Computer Science, University of Manchester. It is composed of 57,600 processing nodes, each with 18 ARM9 processors and 128 MB of mobile DDR SDRAM, totalling 1,036,800 cores and over 7 TB of RAM. The computing platform is based on spiking neural networks, useful in simulating the human brain.

A cognitive computer is a computer that hardwires artificial intelligence and machine learning algorithms into an integrated circuit that closely reproduces the behavior of the human brain. It generally adopts a neuromorphic engineering approach. Synonyms include neuromorphic chip and cognitive chip.

An event camera, also known as a neuromorphic camera, silicon retina or dynamic vision sensor, is an imaging sensor that responds to local changes in brightness. Event cameras do not capture images using a shutter as conventional (frame) cameras do. Instead, each pixel inside an event camera operates independently and asynchronously, reporting changes in brightness as they occur, and staying silent otherwise.

References

  1. ↑ Kwabena Boahen, PhD, Professor of Bioengineering and Electrical Engineering
  2. ↑ Duodu, Cameron (4 June 2016). "From 'konkaka' Child's Workshop In Accra To The Forefront Of Bio-engineering (1)". Modern Ghana. Retrieved 29 December 2023.
  3. ↑ K. A. Boahen, "A retinomorphic vision system", IEEE Micro, Vol. 16, issue 5, pp. 30–39, 1996.
  4. ↑ Brains in Silicon.
  5. ↑ K Boahen, "Neuromorphic Microchips", Scientific American, vol. 292, no. 5, pp. 56–63, May 2005.
  6. ↑ K. M. Hynna and K. Boahen, "Thermodynamically-Equivalent Silicon Models of Ion Channels", Neural Computation, vol. 19, no. 2, pp. 327–350, February 2007.
  7. ↑ P Merolla and K Boahen, "A Recurrent Model of Orientation Maps with Simple and Complex Cells", Advances in Neural Information Processing Systems 16, S Thrun and L Saul, eds, MIT Press, pp. 995–1002, 2004.
  8. ↑ J V Arthur and K Boahen, "Synchrony in Silicon: The Gamma Rhythm", IEEE Transactions on Neural Networks, vol. PP, issue 99, 2007.
  9. ↑ B V Benjamin, P Gao, E McQuinn, S Choudhary, A R Chandrasekaran, J-M Bussat, R Alvarez-Icaza, J V Arthur, P A Merolla, and K Boahen, "Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations",Proceedings of the IEEE, vol. 102, no. 5, pp. 699–716, 2014.
  10. ↑ Boahen, K. (1996). "Retinomorphic vision systems". Proceedings of Fifth International Conference on Microelectronics for Neural Networks. pp. 2–14. doi:10.1109/MNNFS.1996.493766. ISBN   0-8186-7373-7. S2CID   62609792.
  11. ↑ "NIH Director's Pioneer Award Program – 2006 Award Recipients". commonfund.nih.gov. 2018-09-18. Retrieved 2023-07-20.
  12. ↑ "Boahen, Kwabena A." The David and Lucile Packard Foundation. Retrieved 2023-07-20.
  13. 1 2 "Kwabena Boahen's Profile | Stanford Profiles". profiles.stanford.edu. Retrieved 2023-07-20.


This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.