astasik

• Skaar, Jan-Eirik Welle; Haug, Nicolai; Stasik, Alexander Johannes; Einevoll, Gaute & Tøndel, Kristin (2023). Metamodelling of a two-population spiking neural network. PLoS Computational Biology. ISSN 1553-734X. 19(11). doi: 10.1371/journal.pcbi.1011625. Fulltekst i vitenarkiv
• Rimehaug, Atle Eskeland; Stasik, Alexander Johannes; Hagen, Espen; Billeh, Yazan N.; Siegle, Josh H. & Dai, Kael [Vis alle 10 forfattere av denne artikkelen] (2023). Uncovering circuit mechanisms of current sinks and sources with biophysical simulations of primary visual cortex. eLIFE. ISSN 2050-084X. 12. doi: 10.7554/eLife.87169. Fulltekst i vitenarkiv
• Eidnes, Sølve; Stasik, Alexander Johannes; Sterud, Camilla; Bøhn, Eivind Eigil & Riemer-Sørensen, Signe (2023). Pseudo-Hamiltonian neural networks with state-dependent external forces. Physica D : Non-linear phenomena. ISSN 0167-2789. 446. doi: 10.1016/j.physd.2023.133673. Fulltekst i vitenarkiv Vis sammendrag

  Hybrid machine learning based on Hamiltonian formulations has recently been successfully demonstrated for simple mechanical systems, both energy conserving and not energy conserving. We introduce a pseudo-Hamiltonian formulation that is a generalization of the Hamiltonian formulation via the port-Hamiltonian formulation, and show that pseudo-Hamiltonian neural network models can be used to learn external forces acting on a system. We argue that this property is particularly useful when the external forces are state dependent, in which case it is the pseudo-Hamiltonian structure that facilitates the separation of internal and external forces. Numerical results are provided for a forced and damped mass–spring system and a tank system of higher complexity, and a symmetric fourth-order integration scheme is introduced for improved training on sparse and noisy data.

• Rimehaug, Atle Eskeland; Stasik, Alexander Johannes; Hagen, Espen; Siegle, Joshua H.; Dai, Kael & Olsen, Shawn R. [Vis alle 9 forfattere av denne artikkelen] (2022). Uncovering circuit mechanisms of current sinks and sources with biophysical simulations of primary visual cortex. bioRxiv. ISSN 2692-8205. doi: 10.1101/2022.02.22.481540.
• Skaar, Jan-Eirik Welle; Stasik, Alexander Johannes; Plesser, Hans Ekkehard; Einevoll, Gaute & Tøndel, Kristin (2022). Metamodelling of a two-population spiking neural network. bioRxiv. ISSN 2692-8205. doi: 10.1101/2022.09.05.506616.
• Waszak, Maryna; Moen, Terje; Eidnes, Sølve; Stasik, Alexander Johannes; Hansen, Anders & Bouquet, Gregory [Vis alle 12 forfattere av denne artikkelen] (2022). Data quality issues for vibration sensors: a case study in ferrosilicon production. I Nguyen, Phu Hong; Magnanini, Maria Chiara & Sen, Sagar (Red.), Proceedings of the 2nd International Workshop on Software Engineering and AI for Data Quality in Cyber-Physical Systems/Internet of Things (SEA4DQ 2022). Association for Computing Machinery (ACM). ISSN 978-1-4503-9459-8. s. 22–23. doi: 10.1145/3549037.3561273. Fulltekst i vitenarkiv Vis sammendrag

  Digitisation in the mining and metal processing industries plays a key role in their modernisation. Production processes are more and more supported by a variety of sensors that produce large amounts of data that meant to provide insights into the performance of production infrastructures. In the metal processing industry vibration sensors are essential in the monitoring of the production infrastructure. In this position paper we present the installation of vibration sensors in a real industrial environment and discuss the data quality issues we encountered while using such sensors.

• Fuchs, Franz Georg; Lye, Kjetil Olsen; Nilsen, Halvor Møll; Stasik, Alexander Johannes & Sartor, Giorgio (2022). Constraint Preserving Mixers for the Quantum Approximate Optimization Algorithm. Algorithms. ISSN 1999-4893. 15(6). doi: 10.3390/a15060202. Fulltekst i vitenarkiv Vis sammendrag

  The quantum approximate optimization algorithm/quantum alternating operator ansatz (QAOA) is a heuristic to find approximate solutions of combinatorial optimization problems. Most of the literature is limited to quadratic problems without constraints. However, many practically relevant optimization problems do have (hard) constraints that need to be fulfilled. In this article, we present a framework for constructing mixing operators that restrict the evolution to a subspace of the full Hilbert space given by these constraints. We generalize the “XY”-mixer designed to preserve the subspace of “one-hot” states to the general case of subspaces given by a number of computational basis states. We expose the underlying mathematical structure which reveals more of how mixers work and how one can minimize their cost in terms of the number of CX gates, particularly when Trotterization is taken into account. Our analysis also leads to valid Trotterizations for an “XY”-mixer with fewer CX gates than is known to date. In view of practical implementations, we also describe algorithms for efficient decomposition into basis gates. Several examples of more general cases are presented and analyzed.

• Hagen, Espen; Chambers, Anna; Einevoll, Gaute; Pettersen, Klas Henning; Enger, Rune & Stasik, Alexander Johannes (2021). RippleNet: a Recurrent Neural Network for Sharp Wave Ripple (SPW-R) Detection. Neuroinformatics. ISSN 1539-2791. 19, s. 493–514. doi: 10.1007/s12021-020-09496-2. Fulltekst i vitenarkiv Vis sammendrag

  Hippocampal sharp wave ripples (SPW-R) have been identified as key bio-markers of important brain functions such as memory consolidation and decision making. Understanding their underlying mechanisms in healthy and pathological brain function and behaviour rely on accurate SPW-R detection. In this multidisciplinary study, we propose a novel, self-improving artificial intelligence (AI) detection method in the form of deep Recurrent Neural Networks (RNN) with Long Short-Term memory (LSTM) layers that can learn features of SPW-R events from raw, labeled input data. The approach contrasts conventional routines that typically relies on hand-crafted, heuristic feature extraction and often laborious manual curation. The algorithm is trained using supervised learning on hand-curated data sets with SPW-R events obtained under controlled conditions. The input to the algorithm is the local field potential (LFP), the low-frequency part of extracellularly recorded electric potentials from the CA1 region of the hippocampus. Its output predictions can be interpreted as time-varying probabilities of SPW-R events for the duration of the inputs. A simple thresholding applied to the output probabilities is found to identify times of SPW-R events with high precision. The non-causal, or bidirectional variant of the proposed algorithm demonstrates consistently better accuracy compared to the causal, or unidirectional counterpart. Reference implementations of the algorithm, named ‘RippleNet’, are open source, freely available, and implemented using a common open-source framework for neural networks (tensorflow.keras) and can be easily incorporated into existing data analysis workflows for processing experimental data.

• Hagen, Espen; Chambers, Anna; Einevoll, Gaute; Pettersen, Klas Henning; Enger, Rune & Stasik, Alexander Johannes (2020). RippleNet: A Recurrent Neural Network for Sharp Wave Ripple (SPW-R) Detection. bioRxiv. ISSN 2692-8205. doi: 10.1101/2020.05.11.087874. Fulltekst i vitenarkiv Vis sammendrag

  Hippocampal sharp wave ripples (SPW-R) have been identified as key bio-markers of important brain functions such as memory consolidation and decision making. SPW-R detection typically relies on hand-crafted feature extraction, and laborious manual curation is often required. In this multidisciplinary study, we propose a novel, self-improving artificial intelligence (AI) method in the form of deep Recurrent Neural Networks (RNN) with Long Short-Term memory (LSTM) layers that can learn features of SPW-R events from raw, labeled input data. The algorithm is trained using supervised learning on hand-curated data sets with SPW-R events. The input to the algorithm is the local field potential (LFP), the low-frequency part of extracellularly recorded electric potentials from the CA1 region of the hippocampus. The output prediction can be interpreted as the time-varying probability of SPW-R events for the duration of the input. A simple thresholding applied to the output probabilities is found to identify times of events with high precision. The reference implementation of the algorithm, named ‘RippleNet’, is open source, freely available, and implemented using a common open-source framework for neural networks (tensorflow.keras) and can be easily incorporated into existing data analysis workflows for processing experimental data.

• Dai, Kael; Gratiy, Sergey L.; Billeh, Yazan; Xu, Richard; Cai, Binghuang & Cain, Nicholas [Vis alle 12 forfattere av denne artikkelen] (2020). Brain Modeling ToolKit: An open source software suite for multiscale modeling of brain circuits. PLoS Computational Biology. ISSN 1553-734X. 16(11). doi: 10.1371/journal.pcbi.1008386. Fulltekst i vitenarkiv
• Geddes, Justen; Einevoll, Gaute; Acar, Evrim & Stasik, Alexander Johannes (2020). Multi-Linear Population Analysis (MLPA) of LFP Data Using Tensor Decompositions. Frontiers in Applied Mathematics and Statistics. ISSN 2297-4687. doi: 10.3389/fams.2020.00041. Fulltekst i vitenarkiv
• Skaar, Jan-Eirik Welle; Stasik, Alexander Johannes; Hagen, Espen; Ness, Torbjørn V & Einevoll, Gaute (2020). Estimation of neural network model parameters from local field potentials (LFPs). PLoS Computational Biology. ISSN 1553-734X. 16(3). doi: 10.1371/journal.pcbi.1007725. Fulltekst i vitenarkiv

Se alle arbeider i Cristin

• Stasik, Alexander Johannes & Fuchs, Franz Georg (2023). QUantum reservoir computing.
• Stasik, Alexander Johannes (2023). The Norwegian Quantum Landscape.
• Eidnes, Sølve; Bøhn, Eivind Eigil; Lye, Kjetil Olsen; Myhr, Håkon Noren; Riemer-Sørensen, Signe & Stasik, Alexander Johannes [Vis alle 7 forfattere av denne artikkelen] (2023). Pseudo-Hamiltonian neural networks.
• Rimehaug, Atle Eskeland; Stasik, Alexander Johannes; Hagen, Espen; Billeh, Yazan; Siegle, Joshua H. & Dai, Kael [Vis alle 10 forfattere av denne artikkelen] (2022). Uncovering circuit mechanisms of current sinks and sources with biophysical simulations of primary visual cortex.
• Riemer-Sørensen, Signe; Kjenstad, Dag & Stasik, Alexander Johannes (2022). Mer miljøvennlig transport med kunstig intelligens. [Fagblad]. Teknisk Ukeblad. Vis sammendrag

  Debattinnlegg

• Stasik, Alexander Johannes (2021). Quantum reservoir computing for machine learning.
• Stasik, Alexander Johannes (2020). Method of Blind Discovery Analyses Design at the Example of Neutrino Points Searches.
• Stasik, Alexander Johannes (2019). What is machine learning and what does it have to do with statistics?
• Hagen, Espen; Stasik, Alexander Johannes; Billeh, Yazan N.; Siegle, Joshua H.; Dai, Kael & Rimehaug, Atle Eskeland [Vis alle 14 forfattere av denne artikkelen] (2019). Comparing spikes and the local field potential (LFP) in V1 between experimental data and a comprehensive biophysical model.
• Hagen, Espen; Stasik, Alexander Johannes; Billeh, Yazan; Siegle, Joshua H.; Dai, Kael & Rimehaug, Atle Eskeland [Vis alle 14 forfattere av denne artikkelen] (2019). Comparing spikes and the local field potential (LFP) in V1 between experimental data and a comprehensive biophysical model.
• Stasik, Alexander Johannes; Hagen, Espen; Skaar, Jan Eirik; Ness, Torbjørn V & Einevoll, Gaute (2019). Estimating parameters of biological neuronal networks using Convolutional Neural Networks.
• Stasik, Alexander Johannes; Hagen, Espen; Skaar, Jan Eirik; Ness, Torbjørn V & Einevoll, Gaute (2019). Estimating parameters of biological neuronal networks using Convolutional Neural Networks.
• Stasik, Alexander Johannes; Hagen, Espen; Skaar, Jan Eirik; Ness, Torbjørn V & Einevoll, Gaute (2019). Estimating parameters of biological neuronal networks using Convolutional Neural Networks.
• Dragly, Svenn-Arne; Mobarhan, Milad; Lepperød, Mikkel Elle; Tennøe, Simen; Stasik, Alexander Johannes & Fyhn, Marianne [Vis alle 8 forfattere av denne artikkelen] (2018). Exdir An alternative to HDF5 that uses Numpy files for data, YAML for metadata and simple directories to define the hierarchy.
• Stasik, Alexander Johannes; Mobarhan, Milad; Hagen, Espen & Einevoll, Gaute (2018). Generating Input from LGN for Simulation of Visual Cortex.
• Skaar, Jan-Eirik Welle; Hagen, Espen; Stasik, Alexander Johannes; Einevoll, Gaute & Ness, Torbjørn V (2018). Estimation of model parameters from LFPs of spiking neuron networks using deep learning.

Se alle arbeider i Cristin