Dr. Teuscher presented his paper on “Material and Physical Reservoir Computing for Beyond CMOS Electronics: Quo Vadis?” at the 18th ACM International Symposium on Nanoscale Architectures (NANOARCH 23), which was held in Dresden, Germany, Dec 18-20, 2023.
Dr. Teuscher was quoted in a New Scientist article on “Life may be less chaotic than we thought, say physicists.”
“Christof Teuscher at Portland State University in Oregon says the new computational method is an exciting tool and the conclusions it led to complicate the discussion of what exactly the edge of chaos is. Though models rigorously rooted in laboratory studies haven’t been so extensively studied before, the new study may still include too few of them for generalising its conclusions to all life, he says. There is no question that living organisms exist at “sweet spots” somewhere between order and chaos, but it remains an open question how similar those spots are across all life forms and all of life’s processes, says Teuscher.”
Original article: Kyu Hyong Park, Felipe Xavier Costa, Luis M. Rocha, Réka Albert, and Jordan C. Rozum, Models of Cell Processes are Far from the Edge of Chaos, PRX Life 1, 023009, https://doi.org/10.1103/PRXLife.1.023009
tlab undergraduate researcher Nancy MacKenzie presented a poster at the Society for Industrial and Applied Mathematics (SIAM) conference on Applications of Dynamical Systems (DS23).
The poster was entitled “A Mini-Review of Developmental Mechanisms to Complexify Dynamical Systems.”
The conference was held May 14 – 18, 2023, in Portland, OR.
Download the poster here: nancy_siamposter_2023.pdf
D. Tran and C. Teuscher, Multi-tasking Memcapacitive Networks, in IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2023. doi: 10.1109/JETCAS.2023.3235242.
Abstract:
Recent studies have shown that networks of memcapacitive devices provide an ideal computing platform of low power consumption for reservoir computing systems. Random, crossbar, or small-world power-law (SWPL) structures are common topologies for reservoir substrates to compute single tasks. However, neurological studies have shown that the interconnections of cortical brain regions associated with different functions form a rich-club structure. This structure allows human brains to perform multiple activities simultaneously. So far, memcapacitive reservoirs can perform only single tasks. Here, we propose, for the first time, cluster networks functioning as memcapacitive reservoirs to perform multiple tasks simultaneously. Our results illustrate that cluster networks surpassed crossbar and SWPL networks by factors of 4.1×, 5.2×, and 1.7× on three tasks: Isolated Spoken Digits, MNIST, and CIFAR-10. Compared to single-task networks in our previous and published results, multitasking cluster networks could accomplish similar accuracies of 86%, 94.4%, and 27.9% for MNIST, Isolated Spoken Digits, and CIFAR-10. Our extended simulations reveal that both the input signal amplitudes and the inter-cluster connections contribute to the accuracy of cluster networks. Selecting optimal values for signal amplitudes and inter-cluster links is key to obtaining high classification accuracy and low power consumption. Our results illustrate the promise of memcapacitive brain-inspired cluster networks and their capability to solve multiple tasks simultaneously. Such novel computing architectures have the potential to make edge applications more efficient and allow systems that cannot be reconfigured to solve multiple tasks.