The Ersatz Brain Project: A Brain-Like Computer Architecture for Cognition

The Ersatz Brain Project: A Brain-Like Computer Architecture for Cognition

James A. Anderson (Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, RI, USA), Paul Allopenna (Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, RI, USA), Gerald S. Guralnik (Department of Physics, Brown University, Providence, RI, USA), Daniel Ferrente (Department of Physics, Brown University, Providence, RI, USA) and John A. Santini (SRI International, Sarnoff, Princeton, NJ, USA)
DOI: 10.4018/jcini.2012100102
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Abstract

The Ersatz Brain Project develops programming techniques and software applications for a brain-like computing system. Its brain-like hardware architecture design is based on a select set of ideas taken from the anatomy of mammalian neo-cortex. In common with other such attempts it is based on a massively parallel, two-dimensional array of CPUs and their associated memory. The design used in this project: 1) Uses an approximation to cortical computation called the network of networks which holds that the basic computing unit in the cortex is not a single neuron but groups of neurons working together in attractor networks; 2) Assumes connections and data representations in cortex are sparse; 3) Makes extensive use of local lateral connections and topographic data representations, and 4) Scales in a natural way from small groups of neurons to the entire cortical regions. The resulting system computes effectively using techniques such as local data movement, sparse data representation, sparse connectivity, temporal coincidence, and the formation of discrete “module assemblies.” The authors discuss recent neuroscience in relation to their physiological assumptions and a set of experiments displaying what appear to be “concept-like” ensemble based cells in human cortex.
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Essentials Of The Ersatz Approach

The human brain is composed of on the order of 1010 neurons, connected together with at least 1014 connections between neurons. These numbers are likely to be underestimates. Biological neurons and their connections are extremely complex electrochemical structures that require substantial computer power to model even in poor approximations. The more realistic the neuron approximation, the smaller is the network that can be modeled. Worse, there is very strong evidence that a bigger brain is a better brain, thereby increasing greatly computational demands if biology is followed closely. We need good approximations to build a practical brain-like computer. Here we discuss one possible way to do it.

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