People
Kwabena Boahen
Principal Investigator
John Aguayo
Tools: Software
Ben Varkey Benjamin
Circuits: Neurons
Tatiana Engel
Modeling: Attention and Learning
Sam Fok
Compilers: Neural Mapping
Peiran Gao
Theory: Neural Computation
Samir Menon
Motor Control
Alex Neckar
Architecture: Connectivity
Nick Oza
Tools: Calibration
Alumni
Rodrigo Alvarez-Icaza
Learning: Cerebellum
John Arthur
Learning: Hippocampus
Jean-Marie Bussat
Tools: Hardware
Anand Chandrasekaran
Learning: Axon guidance
Sridhar Devarajan
Attention: Tectum
Kai Hynna
Attention: Thalamus
Paul A. Merolla
Vision: Cortex
Nick Steinmetz
Attention: Cortical Circuits
Brian Taba
Learning: Axion Guidance
Bo Wen
Audition: Cochlea
John H. Wittig Jr.
Audition: Cochlear Nucleus
Kareem Zaghloul
Vision: Retina
Roll your mouse over a lab member's name for an overview of that person's research. Click for an expanded profile, including personal background, chip data, and publications.
Kwabena Boahen, PhD
boahen@stanford.eduBeing a scientist at heart, I want to understand how cognition arises from neuronal properties. Being an engineer by training, I am using silicon integrated circuits to emulate the way neurons compute, linking the seemingly disparate fields of electronics and computer science with neurobiology and medicine.
Bo Wen, PhD
bwen@mit.eduThe human inner ear, namely the cochlea, functions as the front-end of the auditory pathway, turning sound into neural impulses that travel up to the cortex. My goal is to mimic the cochlea's nonlinear active behavior in silicon, while satisfying the engineering constraints of minimizing silicon area and power consumption.
Brian Taba, PhD
brian.taba@alumni.upenn.eduThe ultimate goal is to make an artificial brain. The lab has had success devising the first stage, the silicon retina, which includes thirteen cell types. The cortex is more daunting, with billions of neurons, divided into an undetermined taxonomy. Rather than trace every circuit out by hand, I would like to devise a simple rule that can self-organize neural circuits automatically.
John V. Arthur, PhD
jarthur@stanford.eduIt is my goal to understand how the hippocampus functions as an episodic memory, and to apply this knowledge to build a hippocampal model in silicon. All three hippocampal regions—dentate gyrus, CA1, and CA3—are important in episodic memory and receive highly processed sensory data from the cortical hierarchy.
John H. Wittig Jr., PhD
john_wittig@yahoo.comI am examining how anatomical and physiological specializations enable the mammalian nervous system to encode and enhance acoustic information. In particular, I am interested in early acoustic processing: encoding sound signals at the inner hair cell afferent synapse and enhancing sound features one synapse downstream at the cochlear nucleus.
Kai Michael Hynna, PhD
kai.hynna@gmail.comMy thesis topic is the design of a silicon LGN (lateral geniculate nucleus). The LGN is the area within the thalamus through which retinal signals flow. It was originally thought to simply relay sensory information to cortex. However, the retinal input makes up only 1/4 of all afferents, which is quite low for "just a relay point".
Kareem Zagloul, MD, PhD
zaghloul@alum.mit.eduMy research involves quantifying some of the computations realized by the mammalian retina in order to model this first stage of visual processing in silicon. A study of the retina's seemingly simple architecture reveals several layers of complexity that underly its ability to convey visual information to higher cortical structures.
Paul A Merolla, PhD
pmerolla@stanford.eduI have always been impressed with the brain's ability to seamlessly integrate large numbers of input (sensory information) and output variables (motor output). What is it about the brain that makes it better at real-world applications? Can we understand its technique for efficiently coordinating massive amounts of data, and build similar systems in hardware?
Rodrigo Alvarez-Icaza, PhD
rodrigoa@stanford.eduMy interest is to populate the world with self-sufficient artificial agents that "live" and work among us. However, given the present state of technology, this will probably not occur during my lifetime and thus, for the time being, I have chosen to focus on removing a major obstacle by advancing the real-world interaction capabilities of robotic systems.
Anand Chandrasekaran, PhD
The brain wires itself up and a significant fraction of those connections are highly specific. There is strong evidence that these specific connections are a result of activity dependent changes to the arbor structure of axons that innervate them. I am currently focused on implementing dynamic routing of axons based on activity dependent cues on chip.
Sridhar Devarajan, PhD
Exploring the inner workings of the human mind is one of the few questions that gives me emotional and intellectual satisfaction. I am currently unraveling the puzzle of how computations performed by networks of neurons in the brain give rise to mental phenomena by combining experiments with modeling.
Jean-Marie Bussat , PhD
Being an engineer, I've always been amazed by the way nature finds solutions to complex problems. I believe a robot brain has to model the biological brain to be efficient. I want to contribute to the design of such an artificial brain and I am currently focusing on chip design tools for the Neurogrid project.
Peiran Gao
Understanding neural computation is a scientifically challenging problem that can also be applied to engineer revolutionary computer architectures. I take a theoretical and computational approach to define and study properties of networks' structure and dynamics underlying their computational functions.
Samir Menon
It is my goal to understand how the brain controls the musculoskeletal system by combining theoretical and experimental approaches. I am building controllers for human musculoskeletal models, which reveal what neural control is necessary and sufficient, and am applying these theoretical insights to design functional MRI experiments that will elucidate how the brain's motor regions control the musculoskeletal system.
Nick Steinmetz, PhD
I am investigating the cortical mechanisms of selective attention using a combination of advanced electrode array recordings from awake, behaving animals (in the lab of Dr. Tirin Moore) and computational modeling of circuit and network dynamics using Neurogrid.
Ben Varkey Benjamin
I believe that neuromorphic systems hold great potential as low-power computational platforms because they operate at extremely low current levels that are theoretically limited only by shot noise. I am taking on the challenge of designing such systems in deep submicron processes and with newer disruptive devices.
Alex Neckar
Computers have always fascinated me, and the brain is the ultimate computer. My goal is to develop computer architectures that are inspired by the brain’s functional paradigms. Since these paradigms give rise to an incredibly efficient and feature-rich construct in our own biology, I believe that architectures derived from this biology will share the same qualities.
Sam Fok
The human brain offers unmatched power efficiency in computation. I use low-power, subthreshold circuits that mimic spiking neurons and theory that maps computations onto spiking neural networks for applications such as cortical decoders and robot controllers.
John Aguayo
My interests lie at the intersection of computer science, neuroscience and software engineering. In particular, I'm interested in how distributed systems (whether they are software, hardware or neural) are designed and developed to carry out specific goals and tasks. My goal in the Brains in Silicon lab is to develop software that will enhance the experience of the neuromorphic systems being researched and developed in the lab.
Nick Oza
Being an active, lifelong learner, I choose to work in this lab as a hardware engineer because there is no shortage of learning opportunities. Working with neuromorphic systems offers me many opportunities, including designing a low-power chip using nanoscale transistors operating in the subthreshold region.
Tatiana Engel
My goal is to understand how cognition emerges from dynamics in neuronal circuits and how these circuits are rewired in the process of learning. I use a combination of theory, biophysical modeling and model-driven data analysis and work in close collaboration with neurophysiologists.
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