Memory Consolidation in Memristive Systems
Lead Research Organisation:
University of Southampton
Department Name: Optoelectronics Research Ctr (closed)
Abstract
Key objectives
This research will combine computational neuroscience theory with novel memristive technologies for the development of brain-inspired neural systems that exhibit long-life memory consolidation and recall. Initial interest has risen from identifying the limitations of current machine-learning theory with respect to both inefficiencies of von-Neumann architecture and the static memory methods they employ. The main goal for this project is the realisation of a new technology that exhibits always-on, always-learning capabilities and can be integrated to other physical and programmable systems, in flow with the Internet of Things society.
Engineering Methodology
A bottom-up approach shall be examined to develop neural circuits in-Silico. I aim to build on the "memristive fuse" concept to realise artificial synaptic models that exhibit meta-plasticity and are therefore able to dynamically store information with an intrinsic time reference. Theoretical modelling of such technology will be done in Python and fabricated devices will be tested using Arc Instruments analysis tools.
Successful models shall be integrated in numbers, to develop active memory devices exhibiting consolidation and recall. Theoretical work on quantifying individual memories is an essential task to achieve maximum modular hierarchy in storage. Multiple memories consisting partially of the same information should be associated to corresponding pre-existing synapses, whose efficacies should be re-enforced.
The topology of memory consolidation in such devices is of most interest since it can create new bridges between information input and reasoning.
Mathematical models such as Bayesian statistics will be used to study the mentioned hardware memory recall methods. System performance can be designed to exhibit new "creative" methods by stochastically combining synaptic information to create and store new memories. Such improvement can be a significantly leap in the multi-concept problem-solving abilities of artificial automated systems.
Applications
This research will be in full sync with the trend towards a smart society and more general artificial intelligence. The potential impact is vast since successful results can open new paths for computation, finally overcoming the static limitations of current memory manipulation.
This research will combine computational neuroscience theory with novel memristive technologies for the development of brain-inspired neural systems that exhibit long-life memory consolidation and recall. Initial interest has risen from identifying the limitations of current machine-learning theory with respect to both inefficiencies of von-Neumann architecture and the static memory methods they employ. The main goal for this project is the realisation of a new technology that exhibits always-on, always-learning capabilities and can be integrated to other physical and programmable systems, in flow with the Internet of Things society.
Engineering Methodology
A bottom-up approach shall be examined to develop neural circuits in-Silico. I aim to build on the "memristive fuse" concept to realise artificial synaptic models that exhibit meta-plasticity and are therefore able to dynamically store information with an intrinsic time reference. Theoretical modelling of such technology will be done in Python and fabricated devices will be tested using Arc Instruments analysis tools.
Successful models shall be integrated in numbers, to develop active memory devices exhibiting consolidation and recall. Theoretical work on quantifying individual memories is an essential task to achieve maximum modular hierarchy in storage. Multiple memories consisting partially of the same information should be associated to corresponding pre-existing synapses, whose efficacies should be re-enforced.
The topology of memory consolidation in such devices is of most interest since it can create new bridges between information input and reasoning.
Mathematical models such as Bayesian statistics will be used to study the mentioned hardware memory recall methods. System performance can be designed to exhibit new "creative" methods by stochastically combining synaptic information to create and store new memories. Such improvement can be a significantly leap in the multi-concept problem-solving abilities of artificial automated systems.
Applications
This research will be in full sync with the trend towards a smart society and more general artificial intelligence. The potential impact is vast since successful results can open new paths for computation, finally overcoming the static limitations of current memory manipulation.
