I had a rare honor and privilege to present Cognitive Computing to The Honorable H. E. Jakaya Mrisho Kikwete, President, United Republic of Tanzania.
AGEA: Anatomic Gene Expression Atlas
Nature Neuroscience recently published a breakthrough paper from Allen Institute for Brain Science and several collaborating institutions. The study was led by Michael Hawrylycz who is a mathematician by training (advisor: late famed Gian-Carlo Rota).
TITLE: An anatomic gene expression atlas of the adult mouse brain
ABSTRACT: Studying gene expression provides a powerful means of understanding structure-function relationships in the nervous system. The availability of genome-scale in situ hybridization datasets enables new possibilities for understanding brain organization based on gene expression patterns. The Anatomic Gene Expression Atlas (AGEA) is a new relational atlas revealing the genetic architecture of the adult C57Bl/6J mouse brain based on spatial correlations across expression data for thousands of genes in the Allen Brain Atlas (ABA). The AGEA includes three discovery tools for examining neuroanatomical relationships and boundaries: (1) three dimensional expression-based correlation maps, (2) a hierarchical transcriptome-based parcellation of the brain and (3) a facility to retrieve from the ABA specific genes showing enriched expression in local correlated domains. The utility of this atlas is illustrated by analysis of genetic organization in the thalamus, striatum and cerebral cortex. The AGEA is a publicly accessible online computational tool integrated with the ABA (http://mouse.brain-map.org/agea).
The paper brings structure-function together at previously unprecendented scale of 200 micron x 200 micron x 200 micron grid cells.
Biomedical Computation Review: Reverse Engineering the Brain
Biomedical Computation Review published by Simbios (funded by NIH) carried a cover story by Roberta Friedman on Reverse Engineering the Brain. You can see it here. It is thoroghly researched, and covers work of Gerald Edelman, Kwabena Boahen, Tomaso Poggio, Thomas Serre, Eric Knudsen, myself, amongst others.
Humor: Theory of Intelligence from Cheers!
"Well, you see, Norm, it’s like this. A herd of buffalo can only move as fast as the slowest buffalo. And when the herd is hunted, it’s the slowest and weakest ones at the back that are killed first. This natural selection is good for the herd as a whole, because the general speed and health of the whole group keeps improving by the regular killing of the weakest members.
In much the same way, the human brain can only operate as fast as the slowest brain cells. Now, as we know, excessive intake of alcohol kills brain cells. But naturally, it attacks the slowest and weakest brain cells first. In this way, regular consumption of beer eliminates the weaker brain cells, making the brain a faster and more efficient machine.
And that, Norm, is why you always feel smarter after a few beers."
Vitaly Feldman
Vitaly Feldman is a theoretical computer scientist at IBM Almaden. Today, we had a great intellectual pleasure of listening to a wonderful and stimulating white-board talk from Vitaly on his upcoming paper in Neural Computation.
Title:
Experience-Induced Neural Circuits That Achieve High Capacity
Abstract:
Over a lifetime cortex performs a vast number of di®erent cognitive actions, mostly dependent on past experience. Previously it has not been known how such capabilities can be reconciled, even in principle, with the known resource constraints on cortex, such as low connectivity and low average synaptic strength. Here we describe neural circuits and associated algorithms that respect the brain’s most basic resource constraints and support the execution of high numbers of cognitive actions when presented with natural inputs. Our circuits simultaneously support a suite of four basic kinds of task that each require some circuit modi¯cation: hierarchical memory formation, pairwise association, supervised memorization, and inductive learning of threshold functions. The capacity of our circuits is established via experiments in which sequences of several thousands of such actions are simulated by computer and the circuits created tested for subsequent e±cacy. Our underlying theory is apparently the only biologically plausible systems-level theory of learning and memory in cortex for which such a demonstration has been performed, and we argue that no general theory of information processing in the brain can be considered viable without such a demonstration.