Results 71 to 80 of about 1,528,226 (285)

Real-time decoding of arm kinematics during grasping based on F5 neural spike data

, 2017
Due to copyright restrictions, the access to the full text of this article is only available via subscription.Several studies have shown that the information related to grip type, object identity and kinematics of monkey grasping actions is available in ...
Raos, V., Öztop, Erhan, Erhan Oztop, Narges Ashena, Ashena, Narges, Vassilis Raos, Papadourakis, V., Vassilis Papadourakis   +7 more
core   +1 more source

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

Degradation-aware neural imputation: Advancing decoding stability in brain machine interfaces

APL Bioengineering
Neural signal degradation poses a significant challenge in maintaining stable performance when decoding motor tasks using multiunit activity (MUA) and local field potential (LFP) signals in the implantable brain machine interface (iBMI) applications ...
Yun-Ting Kuo, Han-Lin Wang, Bo-Wei Chen, Ching-Fu Wang, Yu-Chun Lo, Sheng-Huang Lin, Po-Chuan Chen, You-Yin Chen   +7 more
doaj   +1 more source

Current Advances in Neural Decoding

, 2019
Neural decoding refers to the extraction of semantically meaningful information from brain activity patterns. We discuss how advances in machine learning drive new advances in neural decoding. While linear methods allow for the reconstruction of basic stimuli from brain activity, more sophisticated nonlinear methods are required when reconstructing ...
Gerven, M.A.J. van, Seeliger, K., Güçlü, U., Güçlütürk, Y.   +3 more
openaire   +2 more sources

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Controlled beam search for neural machine translation using subword units leveraging phrase-based statistical machine translation outputs

Discover Computing
The decoding phase is a crucial component in machine translation systems, alongside the creation of the model. Beam search is the most commonly used algorithm for decoding in these systems.
Emre Satir, Hasan Bulut
doaj   +1 more source

Neural Encoding and Decoding at Scale

CoRR
Recent work has demonstrated that large-scale, multi-animal models are powerful tools for characterizing the relationship between neural activity and behavior. Current large-scale approaches, however, focus exclusively on either predicting neural activity from behavior (encoding) or predicting behavior from neural activity (decoding), limiting their ...
Yizi Zhang, Yanchen Wang, Mehdi Azabou, Alexandre Andre, Zixuan Wang, Hanrui Lyu, International Brain Laboratory, Eva L. Dyer, Liam Paninski, Cole L. Hurwitz   +9 more
openaire   +4 more sources

MIND: Model Independent Neural Decoder [PDF]

2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2019
Standard decoding approaches rely on model-based channel estimation methods to compensate for varying channel effects, which degrade in performance whenever there is a model mismatch. Recently proposed Deep learning based neural decoders address this problem by leveraging a model-free approach via gradient-based training.
Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan   +3 more
openaire   +3 more sources

SORN : a self-organizing recurrent neural network [PDF]

, 2009
Understanding the dynamics of recurrent neural networks is crucial for explaining how the brain processes information. In the neocortex, a range of different plasticity mechanisms are shaping recurrent networks into effective information processing ...
Pipa, Gordon, Andreea Lazar, Triesch, Jochen, Lazar, Andreea, Gordon Pipa, Jochen Triesch   +5 more
core   +1 more source

Nonlinear Approaches for Neural Encoding and Decoding [PDF]

, 2020
Understanding the mapping between stimulus, behavior, and neural responses is vital for understanding sensory, motor, and general neural processing.
Batty, Eleanor
core   +1 more source