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Neuromorphic artificial intelligence, Robot control, Medical / Healthcare

Development of artificial neural networks with brain-like structureand function and evaluation of their learning performance
Symbiotic Intelligent Systems Research Center at Institute for Open and Transdisciplinary Research Initiatives
SA Associate Professor Yuji Kawai
Researchmap https://researchmap.jp/yujikawai/?lang=en

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Abstract
The brain is an enormous network of a huge number of neurons,
which is neither fully regular nor fully irregular but it is very complex. Neural activity on this network is thought to process information and enable animals to behave intelligently. We introduce thestructural properties of brain networks into artificial neural networks
and analyze their neural dynamics to improve their learning performance and deepen our understanding of the relationship between
brain structure and function. Using a small-world property, a representative structural property of brain networks, we successfully
developed recurrent neural networks with efficiency of information
propagation and robustness against structural changes.

low-power-consumption brains. Elucidation of neural mechanismsof such intelligence leads to development of neuromorphic artificial
intelligent and robotic systems that can realize such information
processing and behavior. We are also trying to understand mechanisms of disorders such as autism spectrum disorder by simulating
atypical brain networks.

▍ Background & Results
The small-worldness of brain networks is the property that there
are many local connections, i.e., clusters, between brain regions
and a few long-range connections, i.e., shortcuts, which can propagate information efficiently and quickly. We used echo state networks (reservoir computing), a type of recurrent neural networks,
to investigate how the property influences neural computation.
A typical echo state network has a randomly connected network(reservoir) with fixed weights and learns output time-series from
input-induced complex dynamics in the reservoir network. We
implemented the small-world property into the reservoir networks
and evaluated their performance in time-series learning tasks. It isfound that local connections enables the networks to robustly learn
them in a wide range of a network parameter, and long-range connections makes information propagation fast. In contrast, randomreservoir networks with many long-range connections are sensitive
to the network parameter and performs well only in a very limited
range of the parameter, and input information fades out between
input-output pathways in reservoir networks consisting of only local
connections. In addition, we developed echo state networks basedon anatomical connectivity actually measured from human brains
and conformed that the human brain reservoirs have the samelearning property as the above artificial reservoirs. Furthermore, weconstructed small-world networks using a spiking neuron model
which behaves more like biological neurons. Simulation of the model showed that many local connections reduced the complexity ofneural activity, and thus, exhibited a similar tendency to empiricalobservations in human electroencephalography studies, suggesting that they might be robust to structural changes as the above.

▍ Significance of the research and Future perspective
Our studies on brain-inspired artificial neural networks aim to not
only improve their learning performance but also understand theinformation processing in the brain and evolutionary and developmental origins of brain networks. Animals can adapt to changing environments robustly and quickly, acquire functions throughexperience, and achieve their goals autonomously with their very

P a t e n t
Kawai, Yuji; Park, Jihoon; Asada, Minoru. A small-world topology enhances the echo state property and signal propagation in reservoir computing. NeuralNetworks. 2019; 112: 15-23. doi: 10.1016/j.neunet.2019.01.002
Park, Jihoon; Ichinose, Koki; Kawai, Yuji et al. Macroscopic cluster organizations change the complexity of neural activity. Entropy. 2019; 21(2): 214. doi:10.3390/e21020214
http://www.er.ams.eng.osaka-u.ac.jp/kawai/hp/
URL
http://www.er.ams.eng.osaka-u.ac.jp/asadalab/K e y w o r d neural network, complex network, small-world network, reservoir computing, brain-inspired computing
Tr e a t i s e