Earlier this week, I published my working paper on simulating synthetic neurointerfaces. It’s been quite a journey getting here, and I apologize for the delay in posting about the posting of my paper. I’m going to submit the paper to the 2017 International Conference for Learning Representations (ICLR). What I have posted is a working paper, meaning that there will be more drafts and revisions to come before January. If you have any questions please feel free to contact me. I would also like to give a disclaimer that my work purely comes from a mathematical, and a computer science background. This is a draft, and there are field experts that helped me with the computational neuroscience portion of this project. In the end, my goal was to make the brain itself, a formal system: and I have treated the brain as such throughout.
I’m very excited about this project not only because of its potential but because of what it’s already showed us. We are now able to get some basic neural representations of simple cognitive functions and modulate the functional anatomy of a synthetic neocortical column with ease, a step that we couldn’t achieve otherwise.
In this study, we explore the potential of an unbounded, self-organizing spatial network to simulate translational awareness lent by the brain’s neocortical hypercolumns as a means to better understand the nature of awareness and memory. We modularly examine the prefrontal cortical function, amygdalar responses, and cortical activation complexes to model a synthetic recall system capable of functioning as a compartmentalized and virtual equivalent of the human memory functions. The produced neurointerfaces are able to consistently reproduce the reductive learning quotients of humans in various learning complexities and increase generalizing potentials across all learned behaviors. The cognitive system is validated by examining its persistence under the induction of various mental illnesses and mapping the synthetic changes to their equivalent neuroanatomical mutations. The resultant set of neurointerfaces is a form of artificial general intelligence that produces wave forms empirically similar to that of a patient’s brain. The interfaces also allow us to pinpoint, geometrically and neuroanatomically, the source of any functional behavior.