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SyNAPSE

SyNAPSE is a biology topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand SyNAPSE rather than just read about it. In short: SyNAPSE is a DARPA program that aims to develop electronic neuromorphic machine technology, an attempt to build a new kind of cognitive computer with form, function, and architecture similar to the mammalian brain. Such artificial brains would be used in robots whose intelligence would scale with the size of the neural system in terms of the total number of neurons and synapses and their connectivity.

SyNAPSE — main illustration
SyNAPSE — illustration

Key takeaways

  • SyNAPSE belongs to biology; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect SyNAPSE to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of SyNAPSE from memory before moving on to harder problems.

Reference excerpt

SyNAPSE is a DARPA program that aims to develop electronic neuromorphic machine technology, an attempt to build a new kind of cognitive computer with form, function, and architecture similar to the mammalian brain. Such artificial brains would be used in robots whose intelligence would scale with the size of the neural system in terms of the total number of neurons and synapses and their connectivity. SyNAPSE is a backronym standing for Systems of Neuromorphic Adaptive Plastic Scalable Electronics. The name alludes to synapses, the junctions between biological neurons. The program is being undertaken by HRL Laboratories (HRL), Hewlett-Packard, and IBM Research. In November 2008, IBM and its collaborators were awarded $4.9 million in funding from DARPA while HRL and its collaborators were awarded $5.9 million in funding from DARPA. For the next phase of the project, DARPA added $16.1 million more to the IBM effort while HRL received an additional $10.7 million. In 2011, DARPA added $21 million more to the IBM project. and an additional $17.9 million to the HRL project. The SyNAPSE team for IBM is led by Dharmendra Modha, manager of IBM's cognitive computing initiative. The SyNAPSE team for HRL is led by Narayan Srinivasa, manager of HRL's Center for Neural and Emergent Systems. The initial phase of the SyNAPSE program developed nanometer scale electronic synaptic components capable of adapting the connection strength between two neurons in a manner analogous to that seen in biological systems (Hebbian learning), 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 the stages of microcircuit development, fabrication process development, single chip system development, and multi-chip system development. In support of these hardware developments, the program seeks to develop increasingly capable architecture and design tools, very large-scale computer simulations of the neuromorphic electronic systems to inform the designers and validate the hardware prior to fabrication, and virtual environments for training and testing the simulated and hardware neuromorphic systems.

Published product highlights clockless operation (event-driven), consumes 70 mW during real-time operation, power density of 20 mW/cm2 manufactured in Samsung's 28 nm process technology, 5.4 billion transistors one million neurons and 256 million synapses networked into 4096 neurosynaptic cores by a 2D array, all programmable each core module integrates memory, computation, and communication, and operates in an event-driven, parallel, and fault-tolerant fashion

Participants The following people and institutions are participating in the DARPA SyNAPSE program: IBM team, led by Dharmendra Modha

Stanford University: Brian A. Wandell, H.-S. Philip Wong Cornell University: Rajit Manohar Columbia University Medical Center: Stefano Fusi University of Wisconsin–Madison: Giulio Tononi University of California, Merced: Christopher Kello iniLabs GmbH: Tobi Delbruck IBM Research: Rajagopal Ananthanarayanan, Leland Chang, Daniel Friedman, Christoph Hagleitner, Bulent Kurdi, Chung Lam, Paul Maglio, Dharmendra Modha, Stuart Parkin, Bipin Rajendran, Raghavendra Singh HRL Team led by Narayan Srinivasa

HRL Laboratories: Narayan Srinivasa, Jose Cruz-Albrecht, Dana Wheeler, Tahir Hussain, Sri Satyanarayana, Tim Derosier, Aleksey Nogin, Youngkwan Cho, Corey Thibeault, Michael O' Brien, Michael Yung, Karl Dockendorf, Vincent De Sapio, Qin Jiang, Suhas Chelian Boston University: Massimiliano Versace, Stephen Grossberg, Gail Carpenter, Yongqiang Cao, Praveen Pilly Neurosciences Institute: Gerald Edelman, Einar Gall, Jason Fleischer University of Michigan: Wei Lu Georgia Institute of Technology: Jennifer Hasler University of California, Irvine: Jeff Krichmar George Mason University: Giorgio Ascoli, Alexei Samsonovich Portland State University: Christof Teuscher Stanford University: Mark Schnitzer Set Corporation: Chris Long

See also TrueNorth – IBM chip (introduced mid 2014) boasts of 1 million neurons and 256 million synapses (computing sense); 5.4 billion transistors and 4,096 neurosynaptic cores (hardware). Computational RAM is another approach bypassing the von Neumann bottleneck

References

External links Systems of Neuromorphic Adaptive Plastic Scalable Electronics Neuromorphonics Lab, Boston University Center for Neural and Emergent Systems Homepage HRL Labs Homepage

Illustrations

SyNAPSE: A circuit board with a 4×4 array of SyNAPSE-developed chips. Each chip has one million electronic “neurons” and 256 million electronic synapses between neurons. Built on 28nm process technology, the 5.4 billion transistor chip has one of the highest transistor counts of any chip ever produced as of 2014[update].
A circuit board with a 4×4 array of SyNAPSE-developed chips. Each chip has one million electronic “neurons” and 256 million electronic synapses between neurons. Built on 28nm process technology, the 5.4 billion transistor chip has one of the highest transistor counts of any chip ever produced as of 2014[update].

Worked examples

Example 1 — a first encounter with SyNAPSE

Start with the simplest possible case. Write down what SyNAPSE claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In biology, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to SyNAPSE before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about SyNAPSE ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of SyNAPSE

In research
SyNAPSE appears in biology research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses SyNAPSE in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
SyNAPSE is common in secondary-school and first-year university syllabi. It links to neighbouring topics DARPA projects, Neurotechnology, so understanding it makes those chapters shorter.
In everyday life
Look for SyNAPSE outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study SyNAPSE in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what SyNAPSE means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain SyNAPSE out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is SyNAPSE in simple terms?

SyNAPSE is a DARPA program that aims to develop electronic neuromorphic machine technology, an attempt to build a new kind of cognitive computer with form, function, and architecture similar to the mammalian brain. Such artificial brains would be used in robots whose intelligence would scale with t…

Why does SyNAPSE matter?

Because it connects several biology ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study SyNAPSE?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on SyNAPSE.

Tags

  • DARPA projects
  • Neurotechnology

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