Here are some of my publications and conferences
Citation Statistics
Total citations: 529
h-index: 12
avg citations/paper: 35
Papers
Neuromorphic learning, working memory, and metaplasticity in nanowire networks
A. Loeffler, A. Diaz-Alvarez, R. Zhu, N. Ganesh, J.M. Shine, T. Nakayama, Z. Kuncic (2023)
DOI: 10.1126/sciadv.adg3289 (publication)
Published in: Science Advances (2023), 9 (16).
Nanowire networks (NWNs) mimic the brain’s neurosynaptic connectivity and emergent dynamics. Consequently, NWNs may also emulate the synaptic processes that enable higher-order cognitive functions such as learning and memory. A quintessential cognitive task used to measure human working memory is the n-back task. In this study, task variations inspired by the n-back task are implemented in a NWN device, and external feedback is applied to emulate brain-like supervised and reinforcement learning.
Topological properties of neuromorphic nanowire networks
A. Loeffler, R. Zhu, J. Hochstetter, M. Li, K. Fu, A. Diaz-Alvarez, T. Nakayama, J.M. Shine, Z. Kuncic (2020)
DOI: 10.3389/fnins.2020.00184 (publication)
Published in: Frontiers in Neuroscience (2020), 14.
Applied graph-theory techniques from neuroscience and the Brain Connectivity Toolbox to characterize novel neuromorphic nanowire networks. Compared nanowire networks with simple biological networks and real-world network candidates. I proposed the design and completed all coding, simulations, and writing.
Modularity and multitasking in neuro-memristive reservoir networks
A. Loeffler, R. Zhu, J. Hochstetter, A. Diaz-Alvarez, T. Nakayama, J.M. Shine, Z. Kuncic (2021)
DOI: 10.1088/2634-4386/ac156f (publication)
Published in: Neuromorphic Computing and Engineering (2021), 1.
Tested the impact of different architectures on neuromorphic nanowire networks using Reservoir Computing, to see how structure impacts function on benchmark learning tasks; specifically, multi-tasking capacity. I proposed the design and completed all coding, simulations, and writing.
Avalanches and edge-of-chaos learning in neuromorphic nanowire networks
J. Hochstetter, R. Zhu, A. Loeffler, A. Diaz-Alvarez, T. Nakayama, Z. Kuncic (2021)
DOI: 10.1038/s41467-021-24260-z (publication)
Published in: Nature Communications (2021), 12.
Explored dynamical range of neuromorphic nanowire networks and demonstrated that edge-of-chaos properties optimize information processing. I collaborated on writing, figures, and coding with co-authors.
Information dynamics in neuromorphic nanowire networks
R. Zhu, J. Hochstetter, A. Loeffler, A. Diaz-Alvarez, T. Nakayama, J.T. Lizier, Z. Kuncic (2021).
DOI: 10.1038/s41598-021-92170-7
Published in: Scientific Reports (2021), 11.
Explored information dynamics of neuromorphic nanowire networks using information theory metrics such as Transfer Entropy and Active Information Storage (AIS). Demonstrated learning task dependence on internal dynamic states of the networks. I collaborated on writing and coding with co-authors.
Dynamic Electrical Pathway Tuning in Neuromorphic Nanowire Networks
Q. Li, A. Diaz-Alvarez, R. Iguchi, J. Hochstetter, A. Loeffler, R. Zhu, Y. Shingaya, Z. Kuncic, K. Uchida, T. Nakayama (2020)
DOI: 10.1002/adfm.202003679 (publication)
Published in: Advanced Functional Materials (2020), 12.
Investigated neuromorphic nanowire network pathway formation, visualized through lock-in thermography. I collaborated on writing with co-authors.
Nanoscale neuromorphic networks and criticality: a perspective
C. Dunham, S. Lilak, J. Hochstetter, A. Loeffler, R. Zhu, C. Chase, A. Stieg, Z. Kuncic, J. Gimzewski (2021).
Published in: Journal of Physics: Complexity (2021), 2
Demonstrated Mackey-Glass signal forecasting with neuromorphic nanowire networks and showed that priming the network to more complex signals allows for greater performance on simpler signals. I contributed to writing.
Conferences
Reservoir Computing with Neuromemristive Nanowire Networks
K. Fu, R. Zhu, A. Loeffler, J. Hochstetter, A. Diaz-Alvarez, A. Stieg, J. Gimzewski, T. Nakayama, Z. Kuncic (2020)
DOI: 10.1109/IJCNN48605.2020.9207727 (publication)
Published in: IJCNN IEEE (2020)
Implemented reservoir computing using neuromorphic nanowire networks to perform benchmark AI tasks such as non-linear wave transformation and generation, and Mackey-Glass time series prediction. I wrote code for classification, collaborated on writing and coding with co-authors
Neuromorphic information processing with nanowire networks
Z. Kuncic, O. Kavehei, R. Zhu, A. Loeffler, K. Fu, J. Hochstetter, M. Li, J.M. Shine, A. Diaz-Alvarez, A. Stieg, J. Gimzewski, T. Nakayama (2020)
DOI: 10.1109/ISCAS45731.2020.9181034 (publication)
Published in: ISCAS IEEE (2020)
Demonstrated time-series prediction and hand-written digit recognition in neuromorphic nanowire networks. I contributed to coding, writing and figures.
MNIST classification using neuromorphic nanowire networks
R. Zhu, A. Loeffler, J. Hochstetter, A. Diaz-Alvarez, T. Nakayama, A. Stieg, J. Gimzewski, J.T. Lizier, Z. Kuncic (2021).
Published in: ICONS IEEE (2021)
Demonstrated handwritten digit classification in neuromorphic nanowire networks using MNIST database. I contributed to writing and coding
Harnessing adaptive dynamics in neuro-memristive nanowire networks for transfer learning
R. Zhu, J. Hochstetter, A. Loeffler, A. Diaz-Alvarez, T. Nakayama, A. Stieg, J. Gimzewski, Z. Kuncic (2020).
DOI: 10.1109/ICRC2020.2020.00007
Published in: ICRC IEEE (2020)
Demonstrated Mackey-Glass signal forecasting with neuromorphic nanowire networks and showed that priming the network to more complex signals allows for greater performance on simpler signals. I contributed to writing