no code implementations • 12 Mar 2024 • Akwasi Akwaboah, Ralph Etienne-Cummings
The promise of increasing channel counts in high density ($> 10^4$) neural Microelectrode Arrays (MEAs) for high resolution recording comes with the curse of developing faster characterization strategies for concurrent acquisition of multichannel electrode integrities over a wide frequency spectrum.
no code implementations • 24 Oct 2023 • Caixin Wang, Jie Zhang, Matthew A. Wilson, Ralph Etienne-Cummings
By combining the versatility of pixel-wise sampling patterns with the strength of deep neural networks at decoding complex scenes, our method greatly enhances the vision system's adaptability and performance in dynamic conditions.
no code implementations • 16 Oct 2023 • Zhaoqi Chen, Ralph Etienne-Cummings
We demonstrate a mixed-mode implementation for spatial encoding neurons including theta cells, vector cells, and place cells.
no code implementations • 19 Jan 2022 • Ashwin De Silva, Rahul Ramesh, Lyle Ungar, Marshall Hussain Shuler, Noah J. Cowan, Michael Platt, Chen Li, Leyla Isik, Seung-Eon Roh, Adam Charles, Archana Venkataraman, Brian Caffo, Javier J. How, Justus M Kebschull, John W. Krakauer, Maxim Bichuch, Kaleab Alemayehu Kinfu, Eva Yezerets, Dinesh Jayaraman, Jong M. Shin, Soledad Villar, Ian Phillips, Carey E. Priebe, Thomas Hartung, Michael I. Miller, Jayanta Dey, Ningyuan, Huang, Eric Eaton, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Randal Burns, Onyema Osuagwu, Brett Mensh, Alysson R. Muotri, Julia Brown, Chris White, Weiwei Yang, Andrei A. Rusu, Timothy Verstynen, Konrad P. Kording, Pratik Chaudhari, Joshua T. Vogelstein
We conjecture that certain sequences of tasks are not retrospectively learnable (in which the data distribution is fixed), but are prospectively learnable (in which distributions may be dynamic), suggesting that prospective learning is more difficult in kind than retrospective learning.
no code implementations • 27 Feb 2020 • Jamal Lottier Molin, Chetan Singh Thakur, Ralph Etienne-Cummings, Ernst Niebur
The ability to attend to salient regions of a visual scene is an innate and necessary preprocessing step for both biological and engineered systems performing high-level visual tasks (e. g. object detection, tracking, and classification).
no code implementations • 23 May 2018 • Chetan Singh Thakur, Jamal Molin, Gert Cauwenberghs, Giacomo Indiveri, Kundan Kumar, Ning Qiao, Johannes Schemmel, Runchun Wang, Elisabetta Chicca, Jennifer Olson Hasler, Jae-sun Seo, Shimeng Yu, Yu Cao, André van Schaik, Ralph Etienne-Cummings
Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems.
no code implementations • 31 Oct 2015 • Garrick Orchard, Jacob G. Martin, R. Jacob Vogelstein, Ralph Etienne-Cummings
Recognition of objects in still images has traditionally been regarded as a difficult computational problem.
1 code implementation • 31 Oct 2015 • Garrick Orchard, Ralph Etienne-Cummings
Visual motion estimation is a computationally intensive, but important task for sighted animals.
no code implementations • 5 Aug 2015 • Garrick Orchard, Cedric Meyer, Ralph Etienne-Cummings, Christoph Posch, Nitish Thakor, Ryad Benosman
The asynchronous nature of these systems frees computation and communication from the rigid predetermined timing enforced by system clocks in conventional systems.