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Publications by Anders Lansner

Peer reviewed

Articles

[1]
J. Xu et al., "Modeling Cycle-to-Cycle Variation in Memristors for In-Situ Unsupervised Trace-STDP Learning," IEEE Transactions on Circuits and Systems - II - Express Briefs, vol. 71, no. 2, pp. 627-631, 2024.
[2]
A. Lansner, F. Fiebig and P. Herman, "Fast Hebbian plasticity and working memory," Current Opinion in Neurobiology, vol. 83, 2023.
[4]
D. Wang et al., "Mapping the BCPNN Learning Rule to a Memristor Model," Frontiers in Neuroscience, vol. 15, 2021.
[5]
F. Fiebig, P. Herman and A. Lansner, "An Indexing Theory for Working Memory Based on Fast Hebbian Plasticity," ENEURO, vol. 7, no. 2, 2020.
[6]
Y. Yang et al., "Optimizing BCPNN Learning Rule for Memory Access," Frontiers in Neuroscience, vol. 14, 2020.
[7]
D. Stathis et al., "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, vol. 92, no. 11, pp. 1323-1343, 2020.
[8]
N. Chrysanthidis, F. Fiebig and A. Lansner, "Introducing double bouquet cells into a modular cortical associative memory model," Journal of Computational Neuroscience, vol. 47, no. 2-3, pp. 223-230, 2019.
[9]
R. H. Martinez Mayorquin, A. Lansner and P. Herman, "Probabilistic associative learning suffices for learning the temporal structure of multiple sequences," PLOS ONE, vol. 14, no. 8, 2019.
[11]
F. Fiebig and A. Lansner, "A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation," Journal of Neuroscience, vol. 37, no. 1, pp. 83-96, 2017.
[14]
P. J. Tully et al., "Spike-Based Bayesian-Hebbian Learning of Temporal Sequences," PloS Computational Biology, vol. 12, no. 5, 2016.
[15]
A. Mazzoni et al., "Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models," PloS Computational Biology, vol. 11, no. 12, 2015.
[16]
P. Krishnamurthy, G. Silberberg and A. Lansner, "Long-range recruitment of Martinotti cells causes surround suppression and promotes saliency in an attractor network model," Frontiers in Neural Circuits, vol. 9, 2015.
[17]
J. Eriksson et al., "Neurocognitive Architecture of Working Memory," Neuron, vol. 88, no. 1, pp. 33-46, 2015.
[18]
B. Vogginger et al., "Reducing the computational footprint for real-time BCPNN learning," Frontiers in Neuroengineering, vol. 9, no. 2, 2015.
[19]
S. Marco et al., "A biomimetic approach to machine olfaction, featuring a very large-scale chemical sensor array and embedded neuro-bio-inspired computation," Microsystem Technologies : Micro- and Nanosystems Information Storage and Processing Systems, vol. 20, no. 4-5, pp. 729-742, 2014.
[20]
B. A. Kaplan and A. Lansner, "A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system," Frontiers in Neural Circuits, vol. 8, no. Feb, pp. 5, 2014.
[22]
F. Fiebig and A. Lansner, "Memory consolidation from seconds to weeks : a three-stage neural network model with autonomous reinstatement dynamics," Frontiers in Computational Neuroscience, vol. 8, pp. 64, 2014.
[24]
P. Tully, M. Hennig and A. Lansner, "Synaptic and nonsynaptic plasticity approximating probabilistic inference," Frontiers in Synaptic Neuroscience, vol. 6, no. APR, 2014.
[25]
C. Meli and A. Lansner, "A modular attractor associative memory with patchy connectivity and weight pruning," Network, vol. 24, no. 4, pp. 129-150, 2013.
[26]
B. Kaplan et al., "Anisotropic connectivity implements motion-basedprediction in a spiking neural network," Frontiers in Computational Neuroscience, 2013.
[27]
M. Schain et al., "Arterial input function derived from pairwise correlations between PET-image voxels," Journal of Cerebral Blood Flow and Metabolism, vol. 33, no. 7, pp. 1058-1065, 2013.
[28]
M. Lundqvist, P. Herman and A. Lansner, "Effect of Prestimulus Alpha Power, Phase, and Synchronization on Stimulus Detection Rates in a Biophysical Attractor Network Model," Journal of Neuroscience, vol. 33, no. 29, pp. 11817-11824, 2013.
[29]
P. A. Herman, M. Lundqvist and A. Lansner, "Nested theta to gamma oscillations and precise spatiotemporal firing during memory retrieval in a simulated attractor network," Brain Research, vol. 1536, no. SI, pp. 68-87, 2013.
[31]
A. Lansner et al., "Reactivation in Working Memory : An Attractor Network Model of Free Recall," PLOS ONE, vol. 8, no. 8, pp. e73776, 2013.
[33]
P. Krishnamurthy, G. Silberberg and A. Lansner, "A Cortical Attractor Network with Martinotti Cells Driven by Facilitating Synapses," PLOS ONE, vol. 7, no. 4, pp. e30752, 2012.
[34]
P. Berthet, J. Hällgren Kotaleski and A. Lansner, "Action selection performance of a reconfigurable Basal Ganglia inspired model with Hebbian-Bayesian Go-NoGo connectivity," Frontiers in Behavioral Neuroscience, vol. 6, pp. 65, 2012.
[35]
S. Benjaminsson and A. Lansner, "Nexa : A scalable neural simulator with integrated analysis," Network, vol. 23, no. 4, pp. 254-271, 2012.
[36]
M. Lundqvist, P. Herman and A. Lansner, "Variability of spike firing during theta-coupled replay of memories in a simulated attractor network," Brain Research, vol. 1434, pp. 152-161, 2012.
[37]
D. Bruederle et al., "A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems," Biological Cybernetics, vol. 104, no. 4-5, pp. 263-296, 2011.
[38]
D. N. Silverstein and A. Lansner, "Is attentional blink a byproduct of neocortical attractors?," Frontiers in Computational Neuroscience, vol. 5, 2011.
[39]
B. Auffarth, B. Kaplan and L. Anders, "Map formation in the olfactory bulb by axon guidance of olfactory neurons," Frontiers in Systems Neuroscience, vol. 5, no. 0, 2011.
[40]
S. Benjaminsson, P. Fransson and A. Lansner, "A Novel Model-Free Data Analysis Technique Based on Clustering in a Mutual Information Space : Application to Resting-State fMRI," Frontiers in Systems Neuroscience, vol. 4, pp. 34:1-34:8, 2010.
[41]
M. Lundqvist, A. Compte and A. Lansner, "Bistable, Irregular Firing and Population Oscillations in a Modular Attractor Memory Network," PloS Computational Biology, vol. 6, no. 6, pp. e1000803, 2010.
[42]
M. Lundqvist, P. Herman and A. Lansner, "Theta and Gamma Power Increases and Alpha/Beta Power Decreases with Memory Load in an Attractor Network Model," Journal of cognitive neuroscience, vol. 23, no. 10, pp. 3008-3020, 2010.
[43]
A. Lansner, "Associative memory models : from the cell-assembly theory to biophysically detailed cortex simulations," TINS - Trends in Neurosciences, vol. 32, no. 3, pp. 178-186, 2009.
[44]
C. Johansson and A. Lansner, "Implementing Plastic Weights in Neural Networks using Low Precision Arithmetic," Neurocomputing, vol. 72, no. 4-6, pp. 968-972, 2009.
[45]
M. Sandström et al., "Modeling the response of a population of olfactory receptor neurons to an odorant," Journal of Computational Neuroscience, vol. 27, pp. 337-355, 2009.
[46]
A. Kozlov et al., "Simple cellular and network control principles govern complex patterns of motor behavior," Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 47, pp. 20027-20032, 2009.
[47]
M. Djurfeldt et al., "Brain-scale simulation of the neocortex on the IBM Blue Gene/L  supercomputer," IBM Journal of Research and Development, vol. 52, no. 1-2, pp. 31-41, 2008.
[48]
M. Djurfeldt, Ö. Ekeberg and A. Lansner, "Large-scale modeling - a tool for conquering the complexity of the brain," Frontiers in Neuroinformatics, vol. 2, pp. 1-4, 2008.
[49]
A. . K. Kozlov et al., "A hemicord locomotor network of excitatory interneurons : a simulation study," Biological Cybernetics, vol. 96, no. 2, pp. 229-243, 2007.
[50]
P. Westermark et al., "A mathematical model of the mitochondrial NADH shuttles and anaplerosis in the pancreatic beta-cell," American Journal of Physiology. Endocrinology and Metabolism, vol. 292, no. 2, pp. E373-E393, 2007.
[51]
C. Johansson and A. Lansner, "Imposing Biological Constraints onto an Abstract Neocortical Attractor Network Model," Neural Computation, vol. 19, no. 7, pp. 1871-1896, 2007.
[52]
S. Grillner et al., "Modeling a vertebrate motor system : pattern generation, steering and control of body orientation," Progress in Brain Research, vol. 165, pp. 221-234, 2007.
[53]
M. Huss et al., "Roles of ionic currents in lamprey CPG neurons : a modeling study," Journal of Neurophysiology, vol. 97, no. 4, pp. 2696-2711, 2007.
[54]
M. Sandström, J. Hellgren Kotaleski and A. Lansner, "Scaling effects in a model of the olfactory bulb," Neurocomputing, vol. 70, no. 10-12, pp. 1802-1807, 2007.
[55]
R. Brette et al., "Simulation of networks of spiking neurons : A review of tools and strategies," Journal of Computational Neuroscience, vol. 23, no. 3, pp. 349-398, 2007.
[56]
C. Johansson and A. Lansner, "Towards Cortex Sized Artificial Neural Systems," Neural Networks, vol. 20, no. 1, pp. 48-61, 2007.
[57]
M. Lundqvist et al., "Attractor dynamics in a modular network model of neocortex," Network, vol. 17, no. 3, pp. 253-276, 2006.
[58]
M. Lundqvist, M. Rehn and A. Lansner, "Attractor dynamics in a modular network model of the cerebral cortex," Neurocomputing, vol. 69, no. 10-12, pp. 1155-1159, 2006.
[59]
C. Johansson, M. Rehn and A. Lansner, "Attractor neural networks with patchy connectivity," Neurocomputing, vol. 69, no. 7-9, pp. 627-633, 2006.
[60]
C. Johansson, Ö. Ekeberg and A. Lansner, "Clustering of stored memories in an attractor network with local competition," International Journal of Neural Systems, vol. 16, no. 6, pp. 393-403, 2006.
[61]
M. Sandström et al., "The impact of the distribution of isoforms on CaMKII activation," Neurocomputing, vol. 69, no. 10-12, pp. 1010-1013, 2006.
[62]
B. Cürüklü and A. Lansner, "A model of the summation pools within the layer 4 (area 17)," Neurocomputing, vol. 65, pp. 167-172, 2005.
[63]
E. De Schutter et al., "Biophysically detailed modelling of microcircuits and beyond," TINS - Trends in Neurosciences, vol. 28, no. 10, pp. 562-569, 2005.
[64]
R. Yuste et al., "The cortex as a central pattern generator," Nature Reviews Neuroscience, vol. 6, no. 6, pp. 477-483, 2005.
[65]
A. Svantesson et al., "A mathematical model of the Pyrosequencing reaction system," Biophysical Chemistry, vol. 110, no. 02-jan, pp. 129-145, 2004.
[66]
P. Westermark, J. Hällgren Kotaleski and A. Lansner, "Derivation of a reversible Hill equation with modifiers affecting catalytic properties," WSEAS Transactions on Biology and Biomedicine, vol. 1, pp. 91-98, 2004.
[67]
P. Westermark, J. Hällgren Kotaleski and A. Lansner, "Glucose-stimulated insulin secretion - insights from modelling," Recent Research Developments in Biophysics, vol. 3, pp. 325-350, 2004.
[68]
M. Rehn and A. Lansner, "Sequence memory with dynamical synapses," Neurocomputing, vol. 58-60, pp. 271-278, 2004.
[69]
P. Westemark and A. B. Lansner, "A model of phosphofructokinase and glycolytic oscillations in the pancreatic beta-cell," Biophysical Journal, vol. 85, no. 1, pp. 126--139, 2003.
[70]
A. Sandberg, J. Tegner and A. Lansner, "A working memory model based on fast Hebbian learning," Network, vol. 14, no. 4, pp. 789-802, 2003.
[71]
A. Kozlov, A. Lansner and S. Grillner, "Burst dynamics under mixed NMDA and AMPA drive in the models of the lamprey spinal CPG," Neurocomputing, vol. 52-54, pp. 65-71, 2003.
[72]
A. Lansner, E. Fransén and A. Sandberg, "Cell assembly dynamics in detailed and abstract attractor models of cortical associative memory," Theory in biosciences, vol. 122, no. 1, pp. 19-36, 2003.
[73]
D. Eriksson et al., "Effects of short-term synaptic plasticity in a local microcircuit on cell firing," Neurocomputing, vol. 52-54, pp. 7-12, 2003.
[74]
M. Huss et al., "Role of A-current in lamprey locomotor network neurons," Neurocomputing, vol. 52-54, pp. 295-300, 2003.
[75]
A. Sandberg et al., "A Bayesian attractor network with incremental learning," Network, vol. 13, no. 2, pp. 179-194, 2002.
[76]
A. K. Kozlov et al., "Mechanisms for lateral turns in lamprey in response to descending unilateral commands : a modeling study," Biological Cybernetics, vol. 86, no. 1, pp. 1-14, 2002.
[77]
A. Sandberg and A. Lansner, "Synaptic depression as an intrinsic driver of reinstatement dynamics in an attractor network," Neurocomputing, vol. 44, pp. 615-622, 2002.
[78]
N. Wahlgren and A. Lansner, "Biological evaluation of a Hebbian-Bayesian learning rule," Neurocomputing, vol. 38, pp. 433-438, 2001.
[79]
A. Kozlov et al., "Modeling of substance P and 5-HT induced synaptic plasticity in the lamprey spinal CPG : Consequences for network pattern generation," Journal of Computational Neuroscience, vol. 11, no. 2, pp. 183-200, 2001.
[80]
A. Sandberg, A. Lansner and K. M. Petersson, "Selective enhancement of recall through plasticity modulation in an autoassociative memory," Neurocomputing, vol. 38, pp. 867-873, 2001.
[81]
A. Sandberg et al., "A palimpsest memory based on an incremental Bayesian learning rule," Neurocomputing, vol. 32, pp. 987-994, 2000.
[82]
R. Orre et al., "Bayesian neural networks with confidence estimations applied to data mining," Computational Statistics & Data Analysis, vol. 34, no. 4, pp. 473-493, 2000.
[83]
A. Bjorklund, A. Lansner and V. E. Grill, "Glucose-induced Ca2+ (i) abnormalities in human pancreatic islets - Important role of overstimulation," Diabetes, vol. 49, no. 11, pp. 1840-1848, 2000.
[85]
J. Hellgren Kotaleski, S. Grillner and A. Lansner, "Neural mechanisms potentially contributing to the intersegmental phase lag in lamprey I. : Segmental oscillations dependent on reciprocal inhibition," Biological Cybernetics, vol. 81, no. 4, pp. 317-330, 1999.
[87]
M. Djurfeldt et al., "See-A framework for simulation of biologically detailed and artificial neural networks and systems," Neurocomputing, vol. 26-27, pp. 997-1003, 1999.
[88]
E. Fransén and A. Lansner, "A model of cortical associative memory based on a horizontal network of connected columns," Network, vol. 9, no. 2, pp. 235-264, 1998.
[90]
[91]
J. Tegner et al., "Low-voltage-activated calcium channels in the lamprey locomotor network : Simulation and experiment," Journal of Neurophysiology, vol. 77, no. 4, pp. 1795-1812, 1997.
[92]
Ö. Ekeberg, S. Grillner and A. Lansner, "The Neural Control of Fish Swimming studied through Numerical Simulations," Adaptive Behavior, vol. 3, no. 4, pp. 363-384, 1995.

Conference papers

[93]
D. Wang et al., "FPGA-Based HPC for Associative Memory System," in 29TH ASIA AND SOUTH PACIFIC DESIGN AUTOMATION CONFERENCE, ASP-DAC 2024, 2024, pp. 52-57.
[94]
N. B. Ravichandran, A. Lansner and P. Herman, "Brain-like Combination of Feedforward and Recurrent Network Components Achieves Prototype Extraction and Robust Pattern Recognition," in Lecture Notes in Computer Science, 2023, pp. 488-501.
[95]
N. B. Ravichandran, A. Lansner and P. Herman, "Brain-like combination of feedforward and recurrentnetwork components achieves prototype extraction androbust pattern recognition," in Lecture Notes in Computer Science, 2023.
[96]
P. Pereira, A. Lansner and P. Herman, "Incremental Attractor Neural Network Modelling of the Lifespan Retrieval Curve," in 2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2022.
[97]
D. Wang et al., "Memristor-Based In-Circuit Computation for Trace-Based STDP," in 2022 Ieee International Conference On Artificial Intelligence Circuits And Systems (Aicas 2022) : Intelligent Technology In The Post-Pandemic Era, 2022, pp. 1-4.
[98]
J. Xu et al., "A Memristor Model with Concise Window Function for Spiking Brain-Inspired Computation," in 3rd IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS, 2021.
[99]
D. Stathis et al., "Approximate computation of post-synaptic spikes reduces bandwidth to synaptic storage in a model of cortex," in PROCEEDINGS OF THE 2021 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE 2021), 2021, pp. 685-688.
[100]
N. B. Ravichandran, A. Lansner and P. Herman, "Brain-Like Approaches to Unsupervised Learning of Hidden Representations - A Comparative Study," in Artificial Neural Networks And Machine Learning,  ICANN 2021, Pt V, 2021, pp. 162-173.
[101]
N. B. Ravichandran, A. Lansner and P. Herman, "Semi-supervised learning with Bayesian Confidence Propagation Neural Network," in ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2021, pp. 441-446.
[102]
A. Podobas et al., "StreamBrain : An HPC Framework for Brain-like Neural Networks on CPUs, GPUs and FPGAs," in ACM International Conference Proceeding Series, 2021.
[103]
L. Liu et al., "A FPGA-based Hardware Accelerator for Bayesian Confidence Propagation Neural Network," in 2020 IEEE Nordic Circuits and Systems Conference, NORCAS 2020 - Proceedings, 2020.
[104]
G. Villani et al., "Analysis of free recall dynamics of an abstract working memory model," in 2020 American Control Conference  (ACC), 2020, pp. 2562-2567.
[105]
N. B. Ravichandran, A. Lansner and P. Herman, "Learning representations in Bayesian Confidence Propagation neural networks," in 2020 International joint conference on neural networks (IJCNN), 2020.
[106]
R. H. Martinez Mayorquin et al., "Sequence Disambiguation with Synaptic Traces in Associative Neural Networks," in 28th International Conference on Artificial Neural Networks, ICANN 2019, 2019, pp. 793-805.
[107]
N. B. Ravichandran et al., "Pedestrian simulation as multi-objective reinforcement learning," in Proceedings of the 18th International Conference on Intelligent Virtual Agents, IVA 2018, 2018, pp. 307-312.
[108]
F. Fiebig and A. Lansner, "Memory Consolidation from Seconds to Weeks Through Autonomous Reinstatement Dynamics in a Three-Stage Neural Network Model," in ADVANCES IN COGNITIVE NEURODYNAMICS (IV), 2015, pp. 47-53.
[109]
N. Farahini et al., "A scalable custom simulation machine for the Bayesian Confidence Propagation Neural Network model of the brain," in 2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC), 2014, pp. 578-585.
[110]
B. A. Kaplan et al., "Signature of an anticipatory response in area V1 as modeled by a probabilistic model and a spiking neural network," in PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2014, pp. 3205-3212.
[111]
B. A. Kaplan et al., "Signature of an anticipatory response in area VI as modeled by a probabilistic model and a spiking neural network," in 2014 International Joint Conference on Neural Networks (IJCNN), 2014, pp. 3205-3212.
[112]
A. Lansner, A. Hemani and N. Farahini, "Spiking brain models : Computation, memory and communication constraints for custom hardware implementation," in 2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC), 2014, pp. 556-562.
[113]
S. Marco et al., "Biologically inspired large scale chemical sensor arrays and embedded data processing," in Smart Sensors, Actuators, And Mems VI, 2013, p. 876303.
[114]
K. Persaud et al., "Reverse Engineering of Nature in the field of Chemical Sensors," in 14th International Meeting on Chemical Sensors - IMCS 2012, 2012.
[115]
J. Fonollosa et al., "Biologically inspired computation for chemical sensing," in Procedia Comput. Sci., 2011, pp. 226-227.
[116]
M. Sandström, T. Proschinger and A. Lansner, "A Bulb Model Implementing Fuzzy Coding of Odor Concentration," in Olfaction and Electronic Nose : Proceedings of the 13th International Symposium on Olfaction and Electronic Nose, 2009, pp. 159-162.
[117]
A. Lansner, S. Benjaminsson and C. Johansson, "From ANN to Biomimetic Information Processing," in BIOLOGICALLY INSPIRED SIGNAL PROCESSING FOR CHEMICAL SENSING, 2009, pp. 33-43.
[118]
M. Djurfeldt and A. Lansner, "Memory capacity in a model of cortical layers II/III," in Neuroinformatics 2008. Stockholm, Sweden. 7 Sep - 9 Sep 2008, 2008.
[119]
C. Johansson, M. Rehn and A. Lansner, "Attractor neural networks with patchy connectivity," in ESANN 2005 Proceedings - 13th European Symposium on Artificial Neural Networks, 2007, pp. 429-434.
[120]
C. Johansson and A. Lansner, "A Hierarchical Brain Inspired Computing System," in International Symposium on Nonlinear Theory and its Applications – NOLTA’06, Sep. 11-14, Bologna, Italy, 2006, pp. 599-602.
[121]
C. Johansson and A. Lansner, "Attractor Memory with Self-Organizing Input," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, pp. 265-280.
[122]
R. Steinert, M. Rehn and A. Lansner, "Recognition of handwritten digits using sparse codes generated by local feature extraction methods," in ESANN'2006 : 14th European Symposium on Artificial Neural Networks, 2006, pp. 161-166.
[123]
[124]
C. Johansson and A. Lansner, "Towards cortex sized artificial nervous systems," in KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 1, PROCEEDINGS, 2004, pp. 959-966.
[125]
C. Johansson and A. Lansner, "Towards cortex sized attractor ANN," in BIOLOGICALLY INSPIRED APPROACHES TO ADVANCED INFORMATION TECHNOLOGY, 2004, pp. 63-79.
[126]
C. Johansson, P. Raicevic and A. Lansner, "Reinforcement Learning Based on a Bayesian Confidence Propagating Neural Network," in 2003, April 10-11, SAIS-SSLS Joint Workshop, Center for Applied Autonomous Sensor Systems, Örebro, Sweden, 2003.
[127]
C. Johansson, A. Sandberg and A. Lansner, "A Neural Network with Hypercolumns," in In Proc. International Conference on Artificial Neural Networks - ICANN’02, 2002, pp. 192-197.

Chapters in books

[128]
A. Lansner and M. Diesmann, "Virtues, Pitfalls, and Methodology of Neuronal Network Modeling and Simulations on Supercomputers," in Computational Systems Neurobiology, Nicolas Le Novére Ed., : Springer, 2012, pp. 283-315.

Non-peer reviewed

Articles

[129]
A. Lansner, P. Herman and R. H. Martinez Mayorquin, "Storing long and overlapping sequences in an attractor memory network with Bayesian-Hebbian learning," Journal of Computational Neuroscience, vol. 51, pp. S67-S68, 2023.
[130]
N. Chrysanthidis et al., "Semantization of episodic memory in a spiking cortical attractor network model," Journal of Computational Neuroscience, vol. 49, no. SUPPL 1, pp. S86-S87, 2021.
[131]
M. Schain et al., "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, vol. 32, pp. S149-S151, 2012.
[132]
S. Benjaminsson and A. Lansner, "Adaptive sensor drift counteraction by a modular neural network," Chemical sensors, vol. 36, no. 1, pp. E41-E41, 2011.
[133]
S. Benjaminsson and A. Lansner, "Adaptive sensor drift counteraction by a modular neural network," Neuroscience research, vol. 68, pp. E212-E212, 2010.
[134]
S. Marco, A. Lansner and A. Gutierrez Galvez, "Exploratory Analysis of the Rat Olfactory Bulb Activity," Chemical Senses, vol. 31, no. 8, pp. E73-E73, 2006.
[135]
J. Hellgren Kotaleski, S. Grillner and A. Lansner, "Computer simulation of the segmental neural network generation locomotion in laprey by using populations of network inteneurons," Biological Cybernetics, vol. 68, pp. 1-13, 1992.

Conference papers

[136]
P. Herman and A. Lansner, "Odor recognition framework for evaluating olfactory codes," in 20th Congress of European Chemoreception Research Organization (ECRO-2010), 2011, pp. E17-E17.
[138]
A. Kozlov et al., "Detailed reduced models excitatory hemi-cord locomotor network lamprey," in Society for Neuroscience's Meeting, New Orleans, LA, USA, November 8-12, 2003 [k-sfn03], 2003.
[139]
B. Cürüclü and A. Lansner, "Quantitative Assessment of the Local and Long-Range Horizontal Connections within the Striate Cortex," in 2nd International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS), special session on 'Biologically Inspired Computer Vision', 2003.
[140]
J. Hellgren Kotaleski, A. Lansner and S. Grillner, "Production of phase lag in chains of neural networks oscillating through an escape mechanism," in Proceedings of the sixth annual conference on Computational neuroscience : trends in research, 1998, pp. 65-70.

Chapters in books

[141]
S. Benjaminsson, P. Herman and A. Lansner, "Performance of a computational model of the mammalian olfactory system," in Neuromorphic Olfaction, : CRC Press, 2016, pp. 173-211.
[142]
M. Mehta and A. Lansner, "Coordination in Circuits," in Dynamic Coordination in the Brain : From Neurons to Mind, C. Von der Malsburg, C., Phillips W. A., Singer W. Ed., : MIT Press, 2010, pp. 133-148.
[143]
A. Lansner and M. Lundqvist, "Modeling Coordination in the Neocortex at the Microcircuit and Global Network Level," in Dynamic Coordination in the Brain : From Neurons to Mind, von der Malsburg, C., Phillips W. A., Singer W. Ed., : MIT Press, 2010, pp. 83-99.
[144]
A. Lansner, "Cell assemblies," in Encyclopedia of Nonlinear Science, Alwyn Scott Ed., New York : Routledge, 2005, pp. 103-105.
[145]
A. Lansner, "Neural network models," in Encyclopedia of Nonlinear Science, New York : Routledge, 2005, pp. 614-616.

Reports

[146]
C. Meli and A. Lansner, "A modular attractor associative memory with patchy connectivity and weight pruning," KTH Royal Institute of Technology, TRITA-CSC-CB 2013:01, 2013.
[147]
E. M. Rehn, S. Benjaminsson and A. Lansner, "Event-based Sensor Interface for Supercomputer scale Neural Networks," KTH Royal Institute of Technology, TRITA-CSC-CB, 2012:02, 2012.
[149]
S. Benjaminsson and A. Lansner, "Extreme scaling of brain simulation on JUGENE," KTH Royal Insitute of Technology, 2011.
[150]
M. Djurfeldt and A. Lansner, "1st INCF Workshop on Large-scale Modeling of the Nervous System," Nature Publishing Group, 2007.
[152]
A. Lansner and C. Johansson, "A Mean Field Approximation of BCPNN," KTH, TRITA-NA, 0506, 2005.
[153]
[154]
[155]
C. Johansson and A. Lansner, "BCPNN Implemented with Fixed-Point Arithmetic," , Trita-NA-P, 0403, 2004.
[156]
C. Johansson and A. Lansner, "Mapping of the BCPNN onto Cluster Computers," , TRITA-NA-P, 0305, 2003.
[157]
A. Lansner, "INVESTIGATIONS INTO THE PATIERN PROCESSING CAPABILITIES OF ASSOCIATIVE NETS," Stockholm, Sweden : KTH Royal Institute of Technology, TRITA-NA, 8601, 1986.

Other

[158]
Ö. Ekeberg et al., "Computational Brain Science at CST, CSC, KTH," KTH Royal Institute of Technology, 2016.
[159]
[160]
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