Idea seems to be to train a specialized CNN on a GPU, then use it on a compatible neuromorphic chip for big energy savings.

So Bialystok is refusing a collaboration because a pre-registered protocol is unscientific and she refuses to work with someone so obviously 'biased' that they would damage the results, and besides, publication bias doesn't exist, "there is absolutely no evidence". I see...

See also http://www.unz.com/gnxp/the-cost-of-inbreeding-in-terms-of-health/  & http://www.dailytelegraph.com.au/news/the-case-of-incest-and-depravity-which-came-to-rest-in-the-hills-of-a-quiet-country-town/story-fni0cx4q-1226780679570

Marijuana natural experiment: in 2011, a Dutch university town with many foreign students decided to ban marijuana sales to foreigners; this creates a natural experiment in the effects of marijuana on university grades. The foreigner grades improve, particularly on mathematical topics.

Since education is mostly about signaling, this has clear policy implications: marijuana should be legalized as much as possible so as to maximize the signaling value of grades and the efficiency of the university system. With enough drug legalization, we may even be able to rollback the spread of 'degree-requiring jobs'.

"GWA analyses were performed for verbal–numerical reasoning (N=36 035), memory (N=112 067), reaction time (N=111 483) and for the attainment of a college or a university degree (N=111 114). We report genome-wide significant single-nucleotide polymorphism (SNP)-based associations in 20 genomic regions, and significant gene-based findings in 46 regions. These include findings in the ATXN2, CYP2DG, APBA1 and CADM2 genes. We report replication of these hits in published GWA studies of cognitive function, educational attainment and childhood intelligence. There is also replication, in UK Biobank, of SNP hits reported previously in GWA studies of educational attainment and cognitive function. GCTA-GREML analyses, using common SNPs (minor allele frequency>0.01), indicated significant SNP-based heritabilities of 31% (s.e.m.=1.8%) for verbal–numerical reasoning, 5% (s.e.m.=0.6%) for memory, 11% (s.e.m.=0.6%) for reaction time and 21% (s.e.m.=0.6%) for educational attainment. Polygenic score analyses indicate that up to 5% of the variance in cognitive test scores can be predicted in an independent cohort. The genomic regions identified include several novel loci, some of which have been associated with intracranial volume, neurodegeneration, Alzheimer’s disease and schizophrenia."

"Variants near CHRNA3/5 and APOE have age- and sex-related effects on human lifespan", Joshi et al 2016:

"Lifespan is a trait of enormous personal interest. Research into the biological basis of human lifespan, however, is hampered by the long time to death. Using a novel approach of regressing (272,081) parental lifespans beyond age 40 years on participant genotype in a new large data set (UK Biobank), we here show that common variants near the apolipoprotein E and nicotinic acetylcholine receptor subunit alpha 5 genes are associated with lifespan. The effects are strongly sex and age dependent, with APOE e4 differentially influencing maternal lifespan (P 1⁄4 4.2 Â 10 À 15 , effect À 1.24 years of maternal life per imputed risk allele in parent; sex difference, P 1⁄4 0.011), and a locus near CHRNA3/5 differentially affecting paternal lifespan (P 1⁄4 4.8 Â 10 À 11 , effect À 0.86 years per allele; sex difference P 1⁄4 0.075). Rare homozygous carriers of the risk alleles at both loci are predictedto have 3.3–3.7 years shorter lives."

Plenty of room at the top: "Evolution of the Human Brain: From Matter to Mind", Hofman 2015:

"...Fig. 5.5 The number of connections ( C ), cortical processing units ( U ), and level of interconnectivity ( I ) in the primate neocortex as a function of brain size. Semilogarithmic scale. Values are normalized to one at a brain volume of 100 cm 3 , the size of a monkey brain. Note that the number of myelinated axons increases much faster than the number of cortical processing units (see also Fig. 5.3 ). The human cerebrum, for example, contains six times more myelinated axons than that of a rhesus monkey, whereas the number of cortical processing units is only three times larger. Dashed lines show the potential evolutionary pathway of these neural network elements in primates with very large brains, that is, beyond the hypothetical upper limit of the brain’s processing power (see text and Fig. 5.6 ). Note that a further exponential growth in the number of cortical processing units, without an increase in the number of connections, will lead to a decrease in connectivity between these units and thus to more local wiring (Reprinted with permission from Hofman 2012 )

Fig. 5.6 Relative subcortical volume as a function of brain volume. The predicted subcortical volume (i.e., brain volume—predicted neocortex volume) must be zero at zero brain size. Likewise, the subcortical volume will be zero when the brain is exclusively composed of cortical gray and white matter. At a brain size of 3,575 cm 3 , the subcortical volume has a maximum (see also Fig. 5.5 ). The maximum simulated value for the subcortical volume (366 cm 3 ) is then taken as 100 %. The larger the brain grows beyond this critical size, the less efficient it will become. Assuming constant design, it follows that this model predicts an upper limit to the brain’s processing power. Modern humans are at about two-thirds of that maximum

...Limits to Human Brain Evolution
A progressive enlargement of the hominid brain started by about 2–2.5 million years ago, probably from a bipedal australopithecine form with a brain size comparable to that of a modern chimpanzee (see, e.g., Falk 2004 , 2007 , 2012 ; Robson and Wood 2008 ; De Sousa and Cunha 2012 ). The linear scaling law determined for primates allowed Lent et al. ( 2012 ) to estimate the number of neurons in the brains of hominins, using brain vol73 umes as inferred from fossil cranial endocasts (Klein 2009 ). It shows that ancestral primates living between 35 and 20 million years ago— arboricole and quadruped—did not have more than 20 billion neurons in their brains. In the Pliocene period, between 5.3 and 2.5 million years ago, neuronal numbers may have increased to about 40 billion in Australopithecus , just above the estimated 30 billion neurons of chimpanzees. These hominins became bipedal and produced the first fl aked stone tools. Another increase took place in the early Pleistocene, about 2.5 million years ago, with the appearance of the genus Homo . The number of neurons in the brain grew to about 50 billion in Homo habilis , reaching about 70 billion in Homo erectus , and finally about 90 billion in modern man. With such a large number of neurons, bipedal locomotion consolidated, and handfinger movements acquired sophisticated abilities, which allowed Homo to produce more and more elaborate tools, dominate fire, and improve social interactions.
It means that over the past 2–2.5 million years, more than a doubling in the number of neurons has taken place, leading to one of the most complex and efficient structures in the animated universe, the human brain.
In view of the central importance placed on brain evolution in explaining the success of our species, one may wonder whether there are physical limits that constrain its processing power and evolutionary potential. The human brain has evolved from a set of underlying structures that constrain its size and the amount of information it can store and process. In fact, there are a number of related factors that interact to limit brain size, factors that can be divided into two categories: (1) energetic constraints and (2) neural processing constraints (see, e.g., Wang et al. 2008 ; Herculano-Houzel 2009 ).

...Cochrane and his colleagues ( 1995 ) looked at the different ways in which the brain could evolve to process more information or work more efficiently. They argue that the human brain has (almost) reached the limits of information processing that a neuron-based system allows and that our evolutionary potential is constrained by the delicate balance maintained between conduction speed, pulse width, synaptic processing time, and neuron density. By modeling the informationprocessing capability per unit time of a humantype brain as a function of interconnectivity and axonal conduction speed, they found that the human brain lies about 20–30 % below the optimal, with the optimal processing ability corresponding to a brain about twice the current volume. Any further enhancement of human brainpower would require a simultaneous improvement of neural organization, signal processing, and thermodynamics. Such a scenario, however, is an unrealistic biological option and must be discarded because of the trade-off that exists between these factors.
Of course, extrapolations based on brain models, such as the ones used in the present study, implicitly assume a continuation of brain developments that are on a par with growth rates in the past. One cannot exclude the possibility of new structures evolving in the brain, or a higher degree of specialization of existing brain areas, but within the limits of the existing “Bauplan,” there does not seem to be an incremental improvement path available to the human brain. At a brain size of about 3,500 cm 3 , corresponding to a brain volume two to three times that of modern man, the brain seems to reach its maximum processing capacity. The larger the brain grows beyond this critical size, the less efficient it will become, thus limiting any improvement in cognitive power."

Open: https://www.fortforecast.com/survey/2016