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2.5 million neuron simulation of brain called "Spaun". They taught it to do 8 different tasks without changing any configurations of the network. Spaun takes a 28x28 pixel image as input and controls a simulated arm as output.
"Compression is a natural way to understand much of neural processing." higher-dimensional space in V1 (image-based) lower-dimensional space in IT (feature).
Fig. 1. Anatomical and functional architecture of Spaun. (A) The anatomical architecture of Spaun shows the major brain structures included in the model and their connectivity. Lines terminating in circles indicate GABAergec connections. Lines terminating in open squares indicatemodulatory dopaminergic connections. Box styles and colors indicate the relationship with the functional architecture in (B). PPC, posterior parietal cortex; M1, primary motor cortex; SMA, supplementary motor area; PM, premotor cortex; VLPFC, ventrolateral prefrontal cortex; OFC, orbitofrontal cortex; AIT, anterior inferior temporal cortex; Str, striatum; vStr, ventral striatum; STN, subthalamic nucleus; GPe, globus pallidus externus; GPi, globus pallidus internus; SNr, substantia nigra pars reticulata; SNc, substantia nigra pars compacta; VTA, ventral tegmental area; V2, secondary visual cortex; V4, extrastriate visual cortex. (B) The functional architecture of Spaun. Thick black lines indicate communication between elements of the cortex; thin lines indicate communication between the actionselection mechanism (basal ganglia) and the cortex. Boxes with rounded edges indicate that the actionselection mechanism can use activity changes to manipulate the flow of information into a subsystem. The open-square end of the line connecting reward evaluation and action selection denotes that this connection modulates connection weights. See table S1 for more detailed definitions of abbreviations, a summary of the function to anatomy mapping, and references supporting Spaun’s anatomical and functional assumptions.
The motor output is also hierarchical going from a low-dimensional goal representation to a high-dimensional representation that is in muscle space.
The spiking neurons are implementing a neural representation called "semantic ponters". From Eliasmith's website: Higher-level cognitive functions in biological systems are made possible by semantic pointers. Semantic pointers are neural representations that carry partial semantic content and are composable into the representational structures necessary to support complex cognition.
Eliasmith is also about to publish a book called: "How to build a brain." due out in 2013.
I'm pretty impressed by this. I'm going to spend some time look at his papers.
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