It seems academic progress is expected to be incremental: small steps forward building on previous research. Although beneficial for many facets of research, this effectively means that assumptions set decades ago are no longer open for change, because that is no longer incremental.
However, there is a major problem using current large-scale neural network computational neuroscience models to understand how the neural circuits responsible for recognition can flexibly incorporate new information. Current models are based on the assumption established 4-5 decades ago that neural recognition is primarily “feedforward”. Although several articles have been written over the years describing parts of this problem, they have been minimized with flimsy theoretical “patches”. I have found a solution that fundamentally addresses the problem, but it does not conform to the feedforward assumption.
The incremental expectation makes publishing (where it will be seen) and finding an independent research position impossible, despite my obtaining an unparalleled multidisciplinary background including degrees in Electrical Engineering Computer Sciences, Neuroscience, and a MD. In effect, instead of facing the reality and asking why little has been revealed about this problem over the last 4-5 decades, academia would rather bury someone like me.
Academia promises to promote new research and through my experience I have come to the realization that such promises are cynical and in many cases simply false. Such false promises inhibit new research, ruin young researchers' lives, puts academia in a bad light, and wastes taxpayer's money.
No comments:
Post a Comment