!R-WoNC(2)
二日目

:: Requirements on neuromorphic computing from brain-scale neuronal networks
* A Python package for simulator-independent specification of neuronal network models. http://neuralensemble.org/PyNN/  こういうものあるのか．
* Multi-scale spiking network model of macaque visual cortex https://github.com/INM-6/multi-area-model にいろいろまとまっているのね．

:: Spiking Neural Network imulation on SpiNNaker
* 1% of human brain - 10 mics
* sPyNNaker: A Software Package for Running PyNN Simulations on SpiNNaker -  https://www.frontiersin.org/articles/10.3389/fnins.2018.00816/full
* Synaptic Rewriting - https://www.frontiersin.org/articles/10.3389/fnins.2018.00434/full
* HBP Neuro-robotics Platform https://neurorobotics.net/
** https://developer.humanbrainproject.eu/docs/projects/HBP%20Neurorobotics%20Platform/2.0/nrp/developer_manual/CLE/spinnaker.html
* Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model - https://www.frontiersin.org/articles/10.3389/fnins.2018.00291/full

::Biology Suggests New Forms of Deep Learning in Reccurent Networks of Spiking Neurons
* topics
** computational units that boost temporal processing capabilities
** powerfull
* backpropagation through time(BPTT) by e-prop
** cur. replace by feed forward connections
** proposed. e-prop, there is no transmission of error signals backwards in time or space
*** an abundance of error- and learning signals in the brain. microcircuitry of performance monitoring. Nature 2019 - https://www.biorxiv.org/content/10.1101/187989v2
*** backwards propagation of erros is replaed by augmented forward computation
* cf. Long short-term memory and learning-to-learn in networks of spiking neurons - https://arxiv.org/abs/1803.09574
* cf. Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets - https://arxiv.org/abs/1901.09049

:: Large-scale simulation of cortico-thalamo-cerebellar cicuits toward whole brain simulations
post-ke exploratory challenge 4
* スパコンでのspiking neural network modelは，50年で1 neuronから7 billion neurons まで進化してきた

::Nonlinear Neural Dynamics and its Electronic and Optical Implementation

::Physical models of biological computation
* topics
** real-time analog neural network emulator
** systems based on novel devices/materials
* motivations for keeping up the tradition
** massively parallel collections of non-linear dynamical elements
** analog computation, digital asynchronous communication
** memory and computation are co-localized
* The FeFET neuron, H.Mulaosmanovic et al., Nanoscale 2018 - Mimicking biological neurons with a nanoscale ferroelectric transistor https://pubs.rsc.org/en/content/articlelanding/2018/nr/c8nr07135g#!divAbstract
* Bilogical evidences - cf. https://www.ncbi.nlm.nih.gov/pubmed/3340148
* New materials: challenges and opportunity
* Learning and recall of orthogonal patterns

::Resistive Analog Neuromorphic Devices for Edge AI Computing

::Panel