Fate of Duplicated Neural Structures

Freud's 1926 conjecture is confirmed: evidence from the dorsal periaqueductal gray in mice that human psychological defense against internal instinctual threat evolved from animal motor defense against external predatory threat

In 1926, Freud famously conjectured that the human ego defense of repression against an internal instinctual threat evolved from the animal motor defense of flight from an external predatory threat. Studies over the past 50 years mainly in rodents have investigated the neurobiology of the fight-or-flight reflex to external threats, which activates the emergency alarm system in the dorsal periaqueductal gray (dPAG), the malfunction of which appears likely in panic and post-traumatic stress disorders, but perhaps also in some "non-emergent" conditions like social anxiety and "hysterical" conversion disorder. Computational neuroscience studies in mice by Reis and colleagues have revealed unprecedented insights into the dPAG-related neural mechanisms underlying these evolutionarily honed emergency vertebrate defensive functions (e.g., explore, risk assessment, escape, freeze). A psychoanalytic interpretation of the Reis studies demonstrates that Freud's 1926 conjecture is confirmed, and that internal instinctual threats alone can also set off the dPAG emergency alarm system, which is regulated by 5-HT1A and CRF-1 receptors. Consistent with current psychoanalytic and neurobiologic theories of panic, several other of the primitive components of the dPAG alarm system may also have relevance for understanding of the unconscious determinants of impaired object relationships (e.g., avoidance distance). These dPAG findings reveal (1) a process of "evolution in situ," whereby a more sophisticated dPAG ego defense is seen evolving out of a more primitive dPAG motor defense, (2) a dPAG location for the phylogenetically ancient kernel of Freud's Ego and Id, and (3) a Conscious Id theory that has been conclusively invalidated.

Evolution of Brains and Computers: The Roads Not Taken

When computers started to become a dominant part of technology around the 1950s, fundamental questions about reliable designs and robustness were of great relevance. Their development gave rise to the exploration of new questions, such as what made brains reliable (since neurons can die) and how computers could get inspiration from neural systems. In parallel, the first artificial neural networks came to life. Since then, the comparative view between brains and computers has been developed in new, sometimes unexpected directions. With the rise of deep learning and the development of connectomics, an evolutionary look at how both hardware and neural complexity have evolved or designed is required. In this paper, we argue that important similarities have resulted both from convergent evolution (the inevitable outcome of architectural constraints) and inspiration of hardware and software principles guided by toy pictures of neurobiology. Moreover, dissimilarities and gaps originate from the lack of major innovations that have paved the way to biological computing (including brains) that are completely absent within the artificial domain. As it occurs within synthetic biocomputation, we can also ask whether alternative minds can emerge from A.I. designs. Here, we take an evolutionary view of the problem and discuss the remarkable convergences between living and artificial designs and what are the pre-conditions to achieve artificial intelligence.