http://www.darpa.mil/dso/thrusts/bio/biologically/synapse/index.htmToday's 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 problems generally have many variables and nearly infinite combinatorial complexity, neuromorphic electronic machines would be preferable in a host of applications. However, useful and practical implementations do not yet exist.
Memristors• More unified memory and processing per element – Synapse like• Lower power and high parallelism for some brain-like models• DARPA is funding a lot of this research – Active goal of powerful, cheap, low power adaptive intelligence – Probably deployed on battlefield
Memristor Characteristics• Simpler than transistor• Smaller (3 nm vs 30 nm)• Stateful logic – Computation + memory characteristics – Remembers last voltage • Similar to brain neuron / synapse• Non-volatile – Ultra low power• Sub-nanosecond switching speed
Why are animal brains so efficient?• Human brain runs @ 20 – 30 watts• Blue brain scalable would require own GW power plant• Difference may be fundamental architectural units.• It the mammalian brain storage and processing happen in the same place.• Brain circuits can operated at 100 millivolts. Most CMOS at around 1 volt.• Memristors rival this power efficient, density, and unity of processing and storage.
Blue Brain• Goal is emulated functioning slice of rat brain• Development of generic facility to rapidly model and simulate any brain region• Model of neocortical column containing 200 types of neurons• Attempt to reverse engineer brain – But not to create a brain (!?) – Nope. New goal to build functioning, artificial human brain in next 10 years. Markham claims this is possible.
IBM’s “cat brain”• Cell-by-cell simulation of human visual cortex – 1.6 billion virtual neurons, 9 trillion synapses • Size of cat’s brain• May be key to interpreting biological sense information (sight, hearing, touch)• Seems to have lacked neural patterning – Apparently takes wandering environment • Perhaps embodiment. Experimental data supports this.• Eventual goal – simulating entire human cortex• The approach badly needs memristors or better – Very very power hungry and lots of heat
DARPA Synapse• Systems of Neuromorphic Adaptive Plastic Scalable Electronics• Eventually want full human equivalent artificial brains, starting with cat like intelligence• Many functions of cat brains are very important for robotics bio-equivalent intelligence• Brain body size ratio mysetry
De Garis Brain• Evolving neural network modules for specific task clusters• Integration of these is the “art” part• 3D cellular automata based – Memristors should help – Possible to run on modern GPUs?• “China Brain Project” – 4 year project linking 10,000 – 50,000 NN modules – Brain with thousands of specialized pattern detectors
Numenta – Jef Hawkins• Mathematical model of what he believes is fundamental brain algorithm• Hierchical temporal memory (HTM) – Biomemetic model based on memory-prediction chains – Combines and extends parts of bayesian networks, spatial and temporal clustering algorithms and uses a NN like hierarchy of nodes