DMA 2 KAN Data-flexible multipurpose automated adaptive (k)omplex ANN (or autonomous ANN: A2N2) Neuronal Diversity Why important and prevalent? E.g. aspects: intrinsic properties, connectivity, dynamics, rationale, etc. 1 Specialized architectures E.g.: modularity, small-worlds, compositionality, motifs, recurrence, parallel circuits, etc. 2 Data Flexibility The network can deal with many different types of data and representations. 3 Ability to plan & reason5 EDLA E.g.; integrated evolution, development, learning, and adaptation mechanisms; evolvability; learning to learn; evolving learning rules; etc. 2 Environment ANN closed loop E.g.: cybernetics; cognition grounded in sensory data; active perception; data environments; etc. 4 Rapid learning & relearning Can learn from single instances, can fix incorrect learning, etc. 3 Large memory capacity Can store large numbers of rules, patterns, etc. 3 Classification & Regression x Contributed by all Multi-purposefulness The same network can be applied to many different types of problems. 3 True parallelizationx Very helpful but possibly not essential 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. Biological aspects Capabilities Neural Diversity Machines Final Goal Legend Theory / Methodology About 1 PhD About 1 year postdoc Neural Diversity Machines I (Abdullahi) Basic neuroevolution, Problem signatures, TF complexification. Neural Diversity Machines II Learning based NDMs. Fixed learning rules. Modularity, adaptive structure. 1 0 ACN fundamental theories and methods 0 Computational Neuroscience applied to Artificial Neural NetworksGeneral ACN applicationsComputational Neuroscience applied to Brain Computer Interfaces Explicit Target Target (Environment) Serve an environmental vision by making “nature inspired `machines’ to save nature”. Neural membrane detection (Rajeswari) 3 Crop recognition Parallel & Interacting Circuits (Tuong Phan) 3 Construction monitoring (Darshana) 3 Luminophonics (Shern Shiou) 3 Idea detection / generation (Haixia) 3 Target Currently Unspecified (Waiting for Engineering & Wet Labs) ??? Retinal Modeling 1 (Billy) Retinal Modelling & Prosthesis Design (Kien) Retinal Modeling 2 (Diana) Wet Electronic ANN 1 True hybrids. Genetic neurons on a dish with electronics. Retinal Prosthesis on an Animal Model Implant retinal prosthesis in chosen animal model (e.g. Beetle). Behavioural tests. Focus on non-invasive stimulation?... Retinal Prosthesis Labs? Computational Neuroanatomy 1 Basic histology of retinae from different species. Justified selection. Computational neuroanatomy. Connectomics. UM collaboration. 2 3 Task decomposition E.g.: automatically breaking problems into sub-problems; multiple learners; etc. 2 Wet Electronic ANN 2 Retina on a dish? Integrated with electronics. Computational Neuroanatomy 2 Advanced microscopy (e.g. TEM) of the retinae of one selected species. 3 4 Applied Computational Neuroscience Roadmap v CFFRC Mindset BDP Biol. Design Pattern BDP