Mimicking the Brain -- In Silicon: New Computer Chip Models How Neurons Communicate With Each Other at Synapses
ScienceDaily (Nov. 15, 2011) — For decades, scientists have dreamed of building computer systems that could replicate the human brain's talent for learning new tasks.
Researchers have taken a major step toward that goal by designing a computer chip that mimics how the brain's neurons adapt in response to new information. (Credit: MIT)
MIT researchers have now taken a major step toward that goal by designing a computer chip that mimics how the brain's neurons adapt in response to new information. This phenomenon, known as plasticity, is believed to underlie many brain functions, including learning and memory.
With about 400 transistors, the silicon chip can simulate the activity of a single brain synapse -- a connection between two neurons that allows information to flow from one to the other. The researchers anticipate this chip will help neuroscientists learn much more about how the brain works, and could also be used in neural prosthetic devices such as artificial retinas, says Chi-Sang Poon, a principal research scientist in the Harvard-MIT Division of Health Sciences and Technology.
Poon is the senior author of a paper describing the chip in theProceedings of the National Academy of Sciences the week of Nov. 14. Guy Rachmuth, a former postdoc in Poon's lab, is lead author of the paper. Other authors are Mark Bear, the Picower Professor of Neuroscience at MIT, and Harel Shouval of the University of Texas Medical School.
Modeling synapses
There are about 100 billion neurons in the brain, each of which forms synapses with many other neurons. A synapse is the gap between two neurons (known as the presynaptic and postsynaptic neurons). The presynaptic neuron releases neurotransmitters, such as glutamate and GABA, which bind to receptors on the postsynaptic cell membrane, activating ion channels. Opening and closing those channels changes the cell's electrical potential. If the potential changes dramatically enough, the cell fires an electrical impulse called an action potential.
All of this synaptic activity depends on the ion channels, which control the flow of charged atoms such as sodium, potassium and calcium. Those channels are also key to two processes known as long-term potentiation (LTP) and long-term depression (LTD), which strengthen and weaken synapses, respectively.
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A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity
Mark F. Beard, and
-Author Affiliations
aHarvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139;
bDivision of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138;
cDepartment of Neurobiology and Anatomy, University of Texas Medical School at Houston, Houston, TX 77030; and
dThe Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
Edited by* Leon N. Cooper, Brown University, Providence, RI, and approved October 3, 2011 (received for review May 24, 2011)
Abstract
Current advances in neuromorphic engineering have made it possible to emulate complex neuronal ion channel and intracellular ionic dynamics in real time using highly compact and power-efficient complementary metal-oxide-semiconductor (CMOS) analog very-large-scale-integrated circuit technology. Recently, there has been growing interest in the neuromorphic emulation of the spike-timing-dependent plasticity (STDP) Hebbian learning rule by phenomenological modeling using CMOS, memristor or other analog devices. Here, we propose a CMOS circuit implementation of a biophysically grounded neuromorphic (iono-neuromorphic) model of synaptic plasticity that is capable of capturing both the spike rate-dependent plasticity (SRDP, of the Bienenstock-Cooper-Munro or BCM type) and STDP rules. The iono-neuromorphic model reproduces bidirectional synaptic changes with NMDA receptor-dependent and intracellular calcium-mediated long-term potentiation or long-term depression assuming retrograde endocannabinoid signaling as a second coincidence detector. Changes in excitatory or inhibitory synaptic weights are registered and stored in a nonvolatile and compact digital format analogous to the discrete insertion and removal of AMPA or GABA receptor channels. The versatile Hebbian synapse device is applicable to a variety of neuroprosthesis, brain-machine interface, neurorobotics, neuromimetic computation, machine learning, and neural-inspired adaptive control problems.
iono-neuromorphic modeling, rate-based synaptic plasticity, silicon neuron, subthreshold microelectronics, VLSI circuit
Footnotes
1To whom correspondence should be addressed. E-mail:cpoon@mit.edu.
Author contributions: G.R., H.Z.S., M.F.B., and C.-S.P. designed research; G.R. and C.-S.P. performed research; G.R. and C.-S.P. analyzed data; and G.R., H.Z.S., and C.-S.P. wrote the paper.
The authors declare no conflict of interest.
*This Direct Submission article had a prearranged editor.
This article contains supporting information online at
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Professores, pesquisadores e alunos de universidades públicas e privadas com acesso ao site CAPES/Periódicos podem ler gratuitamente este artigo do PNAS e de mais 22.440 publicações científicas.
