Beta Rhythm (Music of the Mind)

As I've learned more about bio-systems, starting from water molecules and working up to synapses and networks of neurons, I've come to appreciate how incredibly powerful and compact the molecular computing substrate that life is built on top of is. Our most powerful supercomputers take days to calculate how one protein molecule folds, when the simplest bacteria can perform millions of these operations in parallel in seconds. What these simulations give us, however, is insight into exactly what special characteristics each of the proteins has in all of the various shapes it can assume. Building up from this low level understanding, hopefully we will be able to understand what the larger-scale purpose is for each of the various signaling chains and genetic transcriptions that are taking place, and perhaps we may one day be able to model these complex molecular interactions using state machines and logic that allows us to achieve a functionally equivalent set of operations without

The more I read about "Cognitive Computing", the more disenchanted I get with most of the work being done under this banner. There is an awful lot of hype going on here: everything from university researchers that claim how simple it is to create a silicon chip that accurately emulates millions of neurons and projects to create silicon prosthetics for some of the major centers in the brain to overly ambitious claims stating how close we are to getting computers to 'think' and thus to the resulting 'singularity'. Most 'cognitive computing' efforts seem to miss the point that there is more happening here than simple electrical signaling over a network. So coming across the following articles and podcast was like a breath of fresh spring air: Complex Synapses Drove Brain Evolution : ScienceDaily (June 9, 2008) — One of the great scientific challenges is to understand the design principles and origins of the human brain. New research has shed light on t

From "Modeling the Mind: From Circuits to Systems: section 1.2 "Sensory Coding" by Suzanna Becker. "Several classes of computational models have been influential in guiding current thinking about self-organization in sensory systems. These models share the general feature of modeling the brain as a communication channel and applying concepts from information theory. The underlying assumption of these models is that the goal of sensory coding is to map the high-dimensional sensory signal into another (usually lower-dimensional) code that is somehow optimal with respect to information content. Four information-theoretic coding principles will be considered here: 1) Linsker's Infomax principle, 2) Barlow's redundancy reduction principle, 3) Becker and Hinton's Imax principle, and 4) Risannen's minimum description length (MDL) principle. Each of these principles has been used to derive models of learning and has inspired further research into relate