Tsvi Achler MD/PhD/EECS
Sensory recognition is an essential foundation upon which cognition and intelligence is based. Without recognition the brain cannot interact with the world. The internal form in which recognition information is stored dictates how memory and processing is achieved. Moreover, currently diseases and injuries affecting circuits responsible for sensory processing and recognition are incurable (e.g. Alzheimer's, strokes, Schizophrenia, Parkinson's, and so on). Ultimately the goal is to obtain a better understanding in order to dynamically predict, interact with, and provide treatment strategies for diseases affecting processing.
The fundamental question I study is: in what form does the brain store information in order to perform the most flexible recognition? Understanding of the underlying biologically motivated computations responsible for flexible recognition will have a very broad impact in neuroscience, cognitive psychology, computer science, and many other fields.
One insight involves top-down feedback connections (where output units or neurons feed back to their own inputs). This can be found ubiquitously in the brain, however, most conventional artificial neural network theories do not incorporate top-down feedback during their recognition phase. I focus on top-down feedback during recognition where Top-down feedback modifies input activation. The modified input activity is re-distributed to the network which generates feedback on this re-distribution. This is repeated iteratively to recognize inputs.
This paradigm promotes a simpler, easier to modify, symbolic-like form of weights enabling simpler representations. It also inherently displays human cognitive phenomena which traditional classifiers do not such as: a speed-accuracy tradoff and difficulty with similarity. Overall these findings challenge conventional assumptions and offer a biologically plausible, flexible, and dynamic approach to recognition.