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Cognitive Computing via Synaptronics and Supercomputing.
"The information that comes from deep in the evolutionary past we call genetics. The information passed along from hundreds of years ago we call culture. The information passed along from decades ago we call family, and the information offered months ago we call education. But it is all information that flows through us. The brain is adapted to the river of knowledge and exists only as a creature in that river. Our thoughts are profoundly molded by this long historic flow, and none of us exists, self-made, in isolation from it."
763 x 106
6.1 x 1012
2 x 109
20 x 1012
16 x 106
128 x 109
56 x 106
448 x 109
22 x 109
220 x 1012
BlueGene Meets Brain
New results for SC09
Latest simulations achieve unprecedented scale of
109 neurons and 1013 synapses
Data from Todd Hylton
Brain can be realized in modern electronics
Digital, synchronous conventional, 5GHz(compare Power 6, 2008)
Digital, semi-synchronous, 5 MHz(compare IBM PC/8088, 1978)
Digital-Analog, asynchronous, clockless(compare the Brain)
Digital, asynchronous, 100 kHz(compare ENIAC, 1946)
Commandment:Do what is necessary, when it is necessary, and only that which is necessary.
Raghavendra SinghIBM Research – India
Network Architecture of the White Matter Pathways in the Macaque BrainPNAS (July 2010)
Rolf Kotter, Klass Stephen, 2000
413 literature reports
7007 brain sites
8003 mapping details
2508 tracer injections
39748 connection details
Divergent Nomenclature and Abundant Conflicts
Complete Cortex, Thalamus, Basal Ganglia
Comprehensive Includes every study in CoCoMac
Consistent Every connection can be tracked back
Concise 6,877 areas to 383
Coherent Unified hierarchical parcellation
Colossal 3 times larger than previous network
wetware to software
SCC: 351 areas, 6,491 connections
Core contains only 32% of vertices yet 88% of all edges originate or terminate in the core
Core contains correlated-anti-correlated networksand may be a key to consciousness
Fox, Snyder, Vincent, Corbetta, Van Essen, and Raichle, 2005
0 ≤ p≤1 is the Rent parameter and k is the Rent coefficient.
Nicolaus Steno, 1669
Owing both to limitations in hardware and architecture, these (convential) machines are of limited utility in complex, real-world environments, which demand an intelligence that has not yet been captured in an algorithmic-computational paradigm. As compared to biological systems for example, today’s programmable machines are less efficient by a factor of one million to one billion in complex, real-world environments. The SyNAPSE program seeks to break the programmable machine paradigm and define a new path forward for creating useful, intelligent machines.
The vision for the anticipated DARPA SyNAPSE program is the enabling of electronic neuromorphic machine technology that is scalable to biological levels. Programmable machines are limited not only by their computational capacity, but also by an architecture requiring (human-derived) algorithms to both describe and process information from their environment. In contrast, biological neural systems (e.g., brains) autonomously process information in complex environments by automatically learning relevant and probabilistically stable features and associations. Since real world systems are always many body problems with infinite combinatorial complexity, neuromorphic electronic machines would be preferable in a host of applications—but useful and practical implementations do not yet exist.