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Applied Computational Neuroscience Lab Roadmap v. 2016-1.0 General ACN applications Computational Neuroscience applied to Artificial Neural Networks 0 1 1 1 1 Neural membrane detection (Rajeswari) 2% 95% 0 3% 2% 2 Sleep Learning Why is sleep important? Is it doing regularization? How can we use sleep BDP in ANN to improve generalization? Construction monitoring (Darshana) 65% 2 20% 2 Task decomposition E.g.: automatically breaking problems into sub-problems; BFC multiple learners; etc. Tuong ND & PC Neural diversity and parallel circuits. Effect on generalization. 85% Specialized architectures E.g.: parallel circuits, depth, 25% modularity, small-worlds, BDP compositionality, motifs, recurrence. Tomas 2% EDLA E.g.; integrated evolution, development, learning, and adaptation mechanisms; evolvability; BDP learning to learn; evolving learning rules; etc. 2 Tuong 1 2 Cloud Image Processing (Moataz) 35% 3 Large memory capacity Can store large numbers of 0% BFC rules, patterns, etc. High Dim. Low Samp. Size ( 5% (Sheena) 3 Multi-purposefulness The same network can be applied to many different BFC types of problems. 0% 3 Data Flexibility The network can deal with many different types of data BFC and representations. 0% 3 Rapid learning & relearning Can learn from single instances, BFC can fix incorrect learning, etc. Billy Retinal Modeling 1 100% Diana 1 1 Neural Diversity Machines II Gradient based NDMs. Fixed BDP learning rules. Parallel circuits. BDP 2 Abdullahi BDP 5% 0 Neural Diversity Machines I Basic neuroevolution, Problem signatures, TF complexification. 50% Idea detection / generation Text mining 45% (Haixia) Luminophonics (Shern Shiou) ACN fundamental theories and methods Computational Neuroscience applied to Brain Computer Interfaces Retinal Modelling & Prosthesis Design Kien 1 Retinal Modeling 2 90% 100% 2 Wet Electronic ANN 1 True hybrids. Genetic neurons on a dish with electronics. 0% 2 3 Wet Electronic ANN 2 0% Retina on a dish? Integrated with electronics. 3 Computational Neuroanatomy 2 Advanced microscopy (e.g. TEM) of the retinae of one selected species. 2% 0% 4 Legend Biological Design Principle Biol. Functional Capability CFFRC Mindset Neural Diversity Machine x Classification & Regression Final Goal Theory / Methodology PhD or 3-year postdoc 1-year postdoc Target (Environment) Serve an environmental vision by making “nature inspired `machines’ to save nature”. Contributed by all 4 Environment ANN closed loop E.g.: cybernetics; cognition grounded in sensory data; active perception; active learning; data environments; etc. 0% BDP BDP Biol. Design Principle BFC Biol. Functional Capability x% Progress 5 Ability to plan & reason BFC 0% Very helpful but possibly not essential x True parallelization DMA2KAN Data-flexible multipurpose automated adaptive (k)omplex ANN (or 1% autonomous ANN: A2N2) Explicit Target Notes: N1. Numbers in yellow squares denote phases where activities can be run in parallel. N2. If each box corresponds approximately to one PhD and each PhD takes approximately 4 years, since we have 5 phases, the whole roadmap is estimated to take 20 years to complete. In reality, it will not be possible to run all PhDs in parallel, therefore this is a lower bound. N3. The arrow notation A B, means that B is dependent on A being completed AND that A is informed by B. N4. Good generalization properties is a BFC implict in all of the components above, and therefore is not explicitelty represented. Retinal Prosthesis on an Animal Model Implant retinal prosthesis in chosen animal model (e.g. Beetle). Behavioural tests. Focus on non-invasive stimulation? 0% Retinal Prosthesis Labs? ... ??? Target Currently Unspecified (Waiting for Engineering & Wet Labs) Computational Neuroanatomy 1 Basic histology of retinae from different species. Justified selection. Computational neuroanatomy. Connectomics. UM collaboration. 2%