Publications

[Extracted from DiVA, the publication database at KTH.]

Ravichandran, N.; Lansner, A.; Herman, P. (2021):
Brain-Like Approaches to Unsupervised Learning of Hidden Representations - A Comparative Study.
[Conference paper] 30th International Conference on Artificial Neural Networks (ICANN), SEP 14-17, 2021, ELECTR NETWORK; Artificial Neural Networks And Machine Learning,  ICANN 2021, Pt V 162-173 [Details]
Fiebig, F.; Herman, P.; Lansner, A. (2020):
An Indexing Theory for Working Memory Based on Fast Hebbian Plasticity.
ENEURO 7: [Details]
Villani, G.; Jafarian, M.; Lansner, A.; Johansson, K. (2020):
Analysis of free recall dynamics of an abstract working memory model.
[Conference paper] 2020 American Control Conference, ACC 2020; Denver; United States; 1 July 2020 through 3 July 2020; 2020 American Control Conference  (ACC) 2562-2567 [Details]
Liu, L.; Wang, D.; Wang, Y.; Lansner, A.; Hemani, A.; Yang, Y.; Hu, X.; Zou, Z.; Zheng, L. (2020):
A FPGA-based Hardware Accelerator for Bayesian Confidence Propagation Neural Network.
[Conference paper] 2020 IEEE Nordic Circuits and Systems Conference (NorCAS); 2020 IEEE Nordic Circuits and Systems Conference (NorCAS) [Details]
Yang, Y.; Stathis, D.; Jordao, R.; Hemani, A.; Lansner, A. (2020):
Optimizing BCPNN Learning Rule for Memory Access.
Frontiers in Neuroscience 14: [Details]
Stathis, D.; Sudarshan, C.; Yang, Y.; Jung, M.; Weis, C.; Hemani, A.; Lansner, A.; Wehn, N. (2020):
eBrainII - a 3 kW Realtime Custom 3D DRAM Integrated ASIC Implementation of a Biologically Plausible Model of a Human Scale Cortex.
Journal of Signal Processing Systems 92: 1323-1343 [Details]
Ravichandran, N.; Lansner, A.; Herman, P. (2020):
Learning representations in Bayesian Confidence Propagation neural networks.
[Conference paper] International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI), JUL 19-24, 2020, ELECTR NETWORK; 2020 International joint conference on neural networks (IJCNN) [Details]
Martinez Mayorquin, R.; Kviman, O.; Lansner, A.; Herman, P. (2019):
Sequence Disambiguation with Synaptic Traces in Associative Neural Networks.
[Conference paper] Artificial Neural Networks and Machine Learning - ICANN 2019: Theoretical Neural Computation - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019; 28th International Conference on Artificial Neural Networks, ICANN 2019 793-805 [Details]
Chrysanthidis, N.; Fiebig, F.; Lansner, A. (2019):
Introducing double bouquet cells into a modular cortical associative memory model.
Journal of Computational Neuroscience 47: 223-230 [Details]
Martinez Mayorquin, R.; Lansner, A.; Herman, P. (2019):
Probabilistic associative learning suffices for learning the temporal structure of multiple sequences.
PLOS ONE 14: [Details]
Iatropoulos, G.; Herman, P.; Lansner, A.; Karlgren, J.; Larsson, M.; Olofsson, J. (2018):
The language of smell - Connecting linguistic and psychophysical properties of odor descriptors.
Cognition 178: 37-49 [Details]
Ravichandran, N.; Yang, F.; Peters, C.; Lansner, A.; Herman, P. (2018):
Pedestrian simulation as multi-objective reinforcement learning.
[Conference paper] 18th ACM International Conference on Intelligent Virtual Agents, IVA 2018; Western Sydney University's new Parramatta City Campus, Sydney; Australia; 5 November 2018 through 8 November 2018; Proceedings of the 18th International Conference on Intelligent Virtual Agents, IVA 2018 307-312 [Details]
Iatropoulos, G.; Olofsson, J.; Herman, P.; Lansner, A.; Larsson, M. (2017):
Analysis of Statistics and Semantic Relations of Odor-Describing Words in Written Olfactory Versus Non- Olfactory Contexts.
Chemical Senses 42: E34-E35 [Details]
Fiebig, F.; Lansner, A. (2017):
A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation.
Journal of Neuroscience 37: 83-96 [Details]
Ekeberg, Ö.; Fransén, E.; Hellgren Kotaleski, J.; Herman, P.; Kumar, A.; Lansner, A.; Lindeberg, T. (2016):
Computational Brain Science at CST, CSC, KTH.
Other [Details]
Berthet, P.; Lindahl, M.; Tully, P.; Hellgren-Kotaleski, J.; Lansner, A. (2016):
Functional Relevance of Different Basal Ganglia Pathways Investigated in a Spiking Model with Reward Dependent Plasticity.
Frontiers in Neural Circuits 10: [Details]
Benjaminsson, S.; Herman, P.; Lansner, A. (2016):
Performance of a computational model of the mammalian olfactory system.
Chapter in book [Details]
Tully, P.; Lindén, H.; Hennig, M.; Lansner, A. (2016):
Spike-Based Bayesian-Hebbian Learning of Temporal Sequences.
PloS Computational Biology 12: [Details]
Vogginger, B.; Schüffny, R.; Lansner, A.; Cederström, L.; Parzsch, J.; Höppner, S. (2015):
Reducing the computational footprint for real-time BCPNN learning.
Frontiers in Neuroengineering 9: [Details]
Eriksson, J.; Vogel, E.; Lansner, A.; Bergstrom, F.; Nyberg, L. (2015):
Neurocognitive Architecture of Working Memory.
Neuron 88: 33-46 [Details]
Fiebig, F.; Lansner, A. (2015):
Memory Consolidation from Seconds to Weeks Through Autonomous Reinstatement Dynamics in a Three-Stage Neural Network Model.
[Conference paper] ADVANCES IN COGNITIVE NEURODYNAMICS (IV) 47-53 [Details]
Mazzoni, A.; Lindén, H.; Cuntz, H.; Lansner, A.; Panzeri, S.; Einevoll, G. (2015):
Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models.
PloS Computational Biology 11: [Details]
Krishnamurthy, P.; Silberberg, G.; Lansner, A. (2015):
Long-range recruitment of Martinotti cells causes surround suppression and promotes saliency in an attractor network model.
Frontiers in Neural Circuits 9: [Details]
Farahini, N.; Hemani, A.; Lansner, A.; Clermidy, F.; Svensson, C. (2014):
A scalable custom simulation machine for the Bayesian Confidence Propagation Neural Network model of the brain.
[Conference paper] 2014 19th Asia and South Pacific Design Automation Conference, ASP-DAC 2014; Suntec; Singapore; 20 January 2014 through 23 January 2014; 2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC) 578-585 [Details]
Tully, P.; Hennig, M.; Lansner, A. (2014):
Synaptic and nonsynaptic plasticity approximating probabilistic inference.
Frontiers in Synaptic Neuroscience 6: [Details]
Marco, S.; Gutierrez-Galvez, A.; Lansner, A.; Martinez, D.; Rospars, J.; Beccherelli, R.; Perera, A.; Pearce, T.; Verschure, P.; Persaud, K. (2014):
A biomimetic approach to machine olfaction, featuring a very large-scale chemical sensor array and embedded neuro-bio-inspired computation.
Microsystem Technologies 20: 729-742 [Details]
Kaplan, B.; Khoei, M.; Lansner, A.; Perrinet, L. (2014):
Signature of an anticipatory response in area VI as modeled by a probabilistic model and a spiking neural network.
[Conference paper] 2014 International Joint Conference on Neural Networks, IJCNN 2014, 6 July 2014 through 11 July 2014, Beijing, China; 2014 International Joint Conference on Neural Networks (IJCNN) 3205-3212 [Details]
Lansner, A.; Hemani, A.; Farahini, N. (2014):
Spiking brain models - Computation, memory and communication constraints for custom hardware implementation.
[Conference paper] 2014 19th Asia and South Pacific Design Automation Conference, ASP-DAC 2014; Suntec; Singapore; 20 January 2014 through 23 January 2014; 2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC) 556-562 [Details]
Kaplan, B.; Khoei, M.; Lansner, A.; Perrinet, L. (2014):
Signature of an anticipatory response in area V1 as modeled by a probabilistic model and a spiking neural network.
[Conference paper] International Joint Conference on Neural Networks (IJCNN), JUL 06-11, 2014, Beijing, PEOPLES R CHINA; PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) 3205-3212 [Details]
Kaplan, B.; Lansner, A. (2014):
A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system.
Frontiers in Neural Circuits 8: 5 [Details]
Fiebig, F.; Lansner, A. (2014):
Memory consolidation from seconds to weeks - a three-stage neural network model with autonomous reinstatement dynamics.
Frontiers in Computational Neuroscience 8: 64 [Details]
Petrovici, M.; Vogginger, B.; Mueller, P.; Breitwieser, O.; Lundqvist, M.; Muller, L.; Ehrlich, M.; Destexhe, A.; Lansner, A.; Schueffny, R.; Schemmel, J.; Meier, K. (2014):
Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms.
PLOS ONE 9: e108590 [Details]
Berthet, P.; Lansner, A. (2014):
Optogenetic Stimulation in a Computational Model of the Basal Ganglia Biases Action Selection and Reward Prediction Error.
PLOS ONE 9: e90578 [Details]
Marco, S.; Gutiérrez-Gálvez, A.; Lansner, A.; Martinez, D.; Rospars, J.; Beccherelli, R.; Perera, A.; Pearce, T.; Vershure, P.; Persaud, K. (2013):
Biologically inspired large scale chemical sensor arrays and embedded data processing.
[Conference paper] Conference Smart Sensors, Actuators, and MEMS VI; Grenoble; France; 24 April 2013 through 26 April 2013; Smart Sensors, Actuators, And Mems VI 876303 [Details]
Herman, P.; Lundqvist, M.; Lansner, A. (2013):
Nested theta to gamma oscillations and precise spatiotemporal firing during memory retrieval in a simulated attractor network.
Brain Research 1536: 68-87 [Details]
Meli, C.; Lansner, A. (2013):
A modular attractor associative memory with patchy connectivity and weight pruning.
Report [Details]
Tully, P.; Lindén, H.; Hennig, M.; Lansner, A. (2013):
Probabilistic computation underlying sequence learning in a spiking attractor memory network.
BMC neuroscience (Online) 14 (Suppl 1): [Details]
Lundqvist, M.; Herman, P.; Palva, M.; Palva, S.; Silverstein, D.; Lansner, A. (2013):
Stimulus detection rate and latency, firing rates and 1-40Hz oscillatory power are modulated by infra-slow fluctuations in a bistable attractor network model.
NeuroImage 83: 458-471 [Details]
Schain, M.; Benjaminsson, S.; Varnäs, K.; Forsberg, A.; Halldin, C.; Lansner, A.; Farde, L.; Varrone, A. (2013):
Arterial input function derived from pairwise correlations between PET-image voxels.
Journal of Cerebral Blood Flow and Metabolism 33: 1058-1065 [Details]
Lundqvist, M.; Herman, P.; Lansner, A. (2013):
Effect of Prestimulus Alpha Power, Phase, and Synchronization on Stimulus Detection Rates in a Biophysical Attractor Network Model.
Journal of Neuroscience 33: 11817-11824 [Details]
Meli, C.; Lansner, A. (2013):
A modular attractor associative memory with patchy connectivity and weight pruning.
Network 24: 129-150 [Details]
Kaplan, B.; Anders, L.; Perrinet, L.; Masson, G. (2013):
Anisotropic connectivity implements motion-basedprediction in a spiking neural network.
Frontiers in Computational Neuroscience : [Details]
Lansner, A.; Marklund, P.; Sikström, S.; Nilsson, L. (2013):
Reactivation in Working Memory - An Attractor Network Model of Free Recall.
PLOS ONE 8: e73776 [Details]
Persaud, K.; Bernabei, M.; Benjaminsson, S.; Herman, P.; Lansner, A. (2012):
Reverse Engineering of Nature in the field of Chemical Sensors.
[Conference paper] 14th International Meeting on Chemical Sensors - IMCS 2012; [Details]
Schain, M.; Benjaminsson, S.; Varnas, K.; Forsberg, A.; Halldin, C.; Lansner, A.; Farde, L.; Varrone, A. (2012):
Image derived input function using a multivariate analysis method based on pair-wise correlation between PET-image voxels.
Journal of Cerebral Blood Flow and Metabolism 32: S149-S151 [Details]
Rehn, E.; Benjaminsson, S.; Lansner, A. (2012):
Event-based Sensor Interface for Supercomputer scale Neural Networks.
Report [Details]
Benjaminsson, S.; Silverstein, D.; Herman, P.; Melis, P.; Slavnić, V.; Spasojević, M.; Alexiev, K.; Lansner, A. (2012):
Visualization of Output from Large-Scale Brain Simulations.
Report [Details]
Lansner, A.; Diesmann, M. (2012):
Virtues, Pitfalls, and Methodology of Neuronal Network Modeling and Simulations on Supercomputers.
Chapter in book [Details]
Benjaminsson, S.; Lansner, A. (2012):
Nexa - A scalable neural simulator with integrated analysis.
Network 23: 254-271 [Details]
Lundqvist, M.; Herman, P.; Lansner, A. (2012):
Variability of spike firing during theta-coupled replay of memories in a simulated attractor network.
Brain Research 1434: 152-161 [Details]

More