One of the major themes in the SyNAPSE project is developing chips that can learn meaningful information, and preserve it over time. In other words: memristors can learn, but we need to ensure that they are stably learning something useful for the system they are embedded in.
Some help to solve this technological problem comes from neuroscience. The question of how can the cerebral cortex develop stable memories while at the same time incorporating new information through an organism lifetime has been a central theme in many research groups. The talk posted on Neurdon describes one of these approaches.
This talk addresses a central issue on designing intelligent, stable memory systems: how to coordinate multiple levels of (cortical, or artificial) neural processing steps to rapidly learn, and stably remember, important information about a changing environment.
The Synchronous Matching Adaptive Resonance Theory (SMART) model begins to clarify how this can be done by coordinating bottom-up and top-down processes work. The model links processing of learning, expectation, attention, resonance, and synchrony in laminar circuits of spiking neurons obeying realistic membrane equations. The model also predicts how the generality of learned rcategories may be controlled by neuromodulation, and how the same circuit may explain challenging visual perceptual grouping experiments.
PART 1
PART 2
PART 3
