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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%