Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
Skip header Section
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural SystemsSeptember 2005
Publisher:
  • The MIT Press
ISBN:978-0-262-54185-5
Published:01 September 2005
Skip Bibliometrics Section
Reflects downloads up to 15 Oct 2024Bibliometrics
Abstract

No abstract available.

Cited By

  1. Broucke M (2024). Disturbance Rejection in the Cerebellum, SN Computer Science, 5:1, Online publication date: 8-Jan-2024.
  2. Chen S, Jiang L, Rao R and Shea-Brown E Expressive probabilistic sampling in recurrent neural networks Proceedings of the 37th International Conference on Neural Information Processing Systems, (34981-35005)
  3. Confavreux B, Ramesh P, Gonçalves P, Macke J and Vogels T Meta-learning families of plasticity rules in recurrent spiking networks using simulation-based inference Proceedings of the 37th International Conference on Neural Information Processing Systems, (13545-13558)
  4. Lawson D, Li M and Linderman S NAS-X Proceedings of the 37th International Conference on Neural Information Processing Systems, (8602-8633)
  5. Forcella D, Romagnoni A and Destexhe A (2023). Neuronal cable equations derived from the hydrodynamic motion of charged particles, Zeitschrift für Angewandte Mathematik und Physik (ZAMP), 74:3, Online publication date: 1-Jun-2023.
  6. Lawson D, Raventós A, Warrington A and Linderman S SIXO Proceedings of the 36th International Conference on Neural Information Processing Systems, (38844-38858)
  7. Kozachkov L, Ennis M and Slotine J RNNs of RNNs Proceedings of the 36th International Conference on Neural Information Processing Systems, (30512-30527)
  8. Salmanpour A, Farshidi E, Ansari Asl K and Rezagholizadeh E (2022). Low voltage second-order alpha function synapse, Analog Integrated Circuits and Signal Processing, 112:3, (527-536), Online publication date: 1-Sep-2022.
  9. Thieu T and Melnik R Effects of Noise on Leaky Integrate-and-Fire Neuron Models for Neuromorphic Computing Applications Computational Science and Its Applications – ICCSA 2022, (3-18)
  10. Nedelcheva S, Ivanovska S, Durchova M and Koprinkova-Hristova P (2022). HPC parallel implementation combining NEST Simulator and Python modules, Cluster Computing, 25:3, (1637-1644), Online publication date: 1-Jun-2022.
  11. Dragoni L, Flamary R, Lounici K and Reynaud-Bouret P (2022). Sliding Window Strategy for Convolutional Spike Sorting with Lasso, Acta Applicandae Mathematicae: an international survey journal on applying mathematics and mathematical applications, 179:1, Online publication date: 1-Jun-2022.
  12. Jiménez Laredo J, Naudin L, Corson N and Fernandes C A Methodology for Determining Ion Channels from Membrane Potential Neuronal Recordings Applications of Evolutionary Computation, (15-29)
  13. Aghili Yajadda M, Robinson P and Henderson J (2022). Generalized neural field theory of cortical plasticity illustrated by an application to the linear phase of ocular dominance column formation in primary visual cortex, Biological Cybernetics, 116:1, (33-52), Online publication date: 1-Feb-2022.
  14. Hu X, Li K, Zhang W, Luo Y, Lemercier J and Gerkmann T Speech separation using an asynchronous fully recurrent convolutional neural network Proceedings of the 35th International Conference on Neural Information Processing Systems, (22509-22522)
  15. Husbands P, Shim Y, Garvie M, Dewar A, Domcsek N, Graham P, Knight J, Nowotny T and Philippides A (2021). Recent advances in evolutionary and bio-inspired adaptive robotics: Exploiting embodied dynamics, Applied Intelligence, 51:9, (6467-6496), Online publication date: 1-Sep-2021.
  16. Olson E, Wiens T and Gray J (2021). A model of feedforward, global, and lateral inhibition in the locust visual system predicts responses to looming stimuli, Biological Cybernetics, 115:3, (245-265), Online publication date: 1-Jun-2021.
  17. Schmuker M, Kupper R, Aertsen A, Wachtler T and Gewaltig M (2021). Feed-forward and noise-tolerant detection of feature homogeneity in spiking networks with a latency code, Biological Cybernetics, 115:2, (161-176), Online publication date: 1-Apr-2021.
  18. Shuvaev S, Starosta S, Kvitsiani D, Kepecs A and Koulakov A R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making Proceedings of the 34th International Conference on Neural Information Processing Systems, (18872-18882)
  19. Podlaski W and Machens C Biological credit assignment through dynamic inversion of feedforward networks Proceedings of the 34th International Conference on Neural Information Processing Systems, (10065-10076)
  20. Li Q and Pehlevan C Minimax dynamics of optimally balanced spiking networks of excitatory and inhibitory neurons Proceedings of the 34th International Conference on Neural Information Processing Systems, (4894-4904)
  21. Garvie M, Flascher I, Philippides A, Thompson A and Husbands P (2021). Evolved Transistor Array Robot Controllers, Evolutionary Computation, 28:4, (677-708), Online publication date: 1-Dec-2020.
  22. ACM
    Gu X, Peng X, Han F and Wang Z Regeneration of Gamma Oscillations in Large-scale Neural Network with Complicated Structure Based on CUDA Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning, (8-12)
  23. Salatiello A and Giese M Recurrent Neural Network Learning of Performance and Intrinsic Population Dynamics from Sparse Neural Data Artificial Neural Networks and Machine Learning – ICANN 2020, (874-886)
  24. Baker C, Froudarakis E, Yatsenko D, Tolias A and Rosenbaum R (2020). Inference of synaptic connectivity and external variability in neural microcircuits, Journal of Computational Neuroscience, 48:2, (123-147), Online publication date: 1-May-2020.
  25. Harel Y and Meir R (2020). Optimal Multivariate Tuning with Neuron-Level and Population-Level Energy Constraints, Neural Computation, 32:4, (794-828), Online publication date: 1-Apr-2020.
  26. Panda S, Ganguly C, Das S, Mandal R and Chakrabarti S Performance of a Leaky-Integrate-and-Fire Model vis-a-vis Measured Response of Diseased Neurons 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), (1-6)
  27. Cheng C and Lu C The Agility of a Neuron: Phase Shift Between Sinusoidal Current Input and Firing Rate Curve Computational Advances in Bio and Medical Sciences, (13-25)
  28. Cussat-Blanc S, Harrington K and Banzhaf W (2019). Artificial gene regulatory networks-a review, Artificial Life, 24:4, (296-328), Online publication date: 1-Mar-2019.
  29. Xu C, Yang J and Gao J (2019). Coupled-learning convolutional neural networks for object recognition, Multimedia Tools and Applications, 78:1, (573-589), Online publication date: 1-Jan-2019.
  30. ACM
    Yedjour H, Meftah B, Yedjour D and Benyettou A The leaky integrate-and-fire neuron model for a rigid and a non-rigid object tracking Proceedings of the 7th International Conference on Software Engineering and New Technologies, (1-4)
  31. Kim D, Park G and Lee S Hierarchical Control Architecture Regulating Competition between Model-Based and Context-Dependent Model-Free Reinforcement Learning Strategies 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), (990-994)
  32. Lee D, Lee G, Kwon D, Lee S, Kim Y and Kim J Flexon Proceedings of the 45th Annual International Symposium on Computer Architecture, (275-288)
  33. Chacon-Murguia M and Ramirez-Quintana J (2018). Bio-inspired architecture for static object segmentation in time varying background models from video sequences, Neurocomputing, 275:C, (1846-1860), Online publication date: 31-Jan-2018.
  34. Wang Y, Patel D, Raskin V, Baciu G, Ayesh A, Howard N, Rayz J, Mizoguchi F and Tsumoto S (2018). Cognitive Computing, International Journal of Software Science and Computational Intelligence, 10:1, (1-14), Online publication date: 1-Jan-2018.
  35. Wang Y, Peng J, Patel S, Gavrilova M, Fiorini R, Widrow B, Howard N, Kacprzyk J, Frieder O and Sheu P (2018). Cognitive Informatics, International Journal of Cognitive Informatics and Natural Intelligence, 12:1, (1-13), Online publication date: 1-Jan-2018.
  36. Mizoguchi F, Wang Y, Patel S, Raskin V, Tsumoto S, Baciu G, Widrow B, Zadeh L, Howard N, Beaufays F, Hsu D, Luo G, Wei W and Zhang D (2017). Abstract Intelligence, International Journal of Cognitive Informatics and Natural Intelligence, 11:1, (1-15), Online publication date: 1-Jan-2017.
  37. Kadakia N, Armstrong E, Breen D, Morone U, Daou A, Margoliash D and Abarbanel H (2016). Nonlinear statistical data assimilation for HVC$$_{\mathrm{RA}}$$RA neurons in the avian song system, Biological Cybernetics, 110:6, (417-434), Online publication date: 1-Dec-2016.
  38. Kafashan M, Nandi A and Ching S (2016). Relating observability and compressed sensing of time-varying signals in recurrent linear networks, Neural Networks, 83:C, (11-20), Online publication date: 1-Nov-2016.
  39. Yoo Y, Ozan Koyluoglu O, Vishwanath S and Fiete I (2016). Multi-periodic neural coding for adaptive information transfer, Theoretical Computer Science, 633:C, (37-53), Online publication date: 20-Jun-2016.
  40. Lazar A, Ukani N and Zhou Y (2016). A motion detection algorithm using local phase information, Computational Intelligence and Neuroscience, 2016, (40-40), Online publication date: 1-Jan-2016.
  41. ACM
    Azghadi M, Moradi S, Fasnacht D, Ozdas M and Indiveri G (2015). Programmable Spike-Timing-Dependent Plasticity Learning Circuits in Neuromorphic VLSI Architectures, ACM Journal on Emerging Technologies in Computing Systems, 12:2, (1-18), Online publication date: 2-Sep-2015.
  42. Tapson J, Cohen G and van Schaik A (2015). ELM solutions for event-based systems, Neurocomputing, 149:PA, (435-442), Online publication date: 3-Feb-2015.
  43. Grover P (2015). Information Friction and Its Implications on Minimum Energy Required for Communication, IEEE Transactions on Information Theory, 61:2, (895-907), Online publication date: 1-Feb-2015.
  44. ACM
    Payvand M, Rofeh J, Sodhi A and Theogarajan L A CMOS-memristive self-learning neural network for pattern classification applications Proceedings of the 2014 IEEE/ACM International Symposium on Nanoscale Architectures, (92-97)
  45. ACM
    Veletić M, Floor P and Balasingham I From Nano-Scale Neural Excitability to Long Term Synaptic Modification Proceedings of ACM The First Annual International Conference on Nanoscale Computing and Communication, (1-9)
  46. Li L, Brockmeier A, Choi J, Francis J, Sanchez J and Príncipe J (2014). A tensor-product-kernel framework for multiscale neural activity decoding and control, Computational Intelligence and Neuroscience, 2014, (2-2), Online publication date: 1-Jan-2014.
  47. Navaridas J, Furber S, Garside J, Jin X, Khan M, Lester D, Luján M, Miguel-Alonso J, Painkras E, Patterson C, Plana L, Rast A, Richards D, Shi Y, Temple S, Wu J and Yang S (2013). SpiNNaker, Parallel Computing, 39:11, (693-708), Online publication date: 1-Nov-2013.
  48. Zhang C, Dangelmayr G and Oprea I (2013). Storing cycles in Hopfield-type networks with pseudoinverse learning rule, Neural Networks, 46, (283-298), Online publication date: 1-Oct-2013.
  49. Chersi F, Mirolli M, Pezzulo G and Baldassarre G (2013). 2013 Special Issue, Neural Networks, 41, (212-224), Online publication date: 1-May-2013.
  50. Baldassarre G, Mannella F, Fiore V, Redgrave P, Gurney K and Mirolli M (2013). 2013 Special Issue, Neural Networks, 41, (168-187), Online publication date: 1-May-2013.
  51. Guo L, Yang Z, Graham B and Zhang D Characterisation of information flow in an izhikevich network Proceedings of the 19th international conference on Neural Information Processing - Volume Part I, (392-400)
  52. Luciw M and Schmidhuber J Low complexity proto-value function learning from sensory observations with incremental slow feature analysis Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II, (279-287)
  53. Mehboob Z, Yin H, Wuerger S and Parkes L Multivoxel pattern analysis using information-preserving EMD Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning, (19-26)
  54. He X, Peng Y and Gao H The neuron's modeling methods based on neurodynamics Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I, (188-195)
  55. Zhang X, Foderaro G, Henriquez C, VanDongen A and Ferrari S (2012). A radial basis function spike model for indirect learning via integrate-and-fire sampling and reconstruction techniques, Advances in Artificial Neural Systems, 2012, (10-10), Online publication date: 1-Jan-2012.
  56. ACM
    Mesiti F and Balasingham I Novel treatment strategies for neurodegenerative diseases based on RF exposure Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies, (1-5)
  57. Cai R, Wu Q, Wang P, Sun H and Wang Z Moving target detection and classification using spiking neural networks Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering, (210-217)
  58. Guo D A survey of signal propagation in feedforward neuronal networks Proceedings of the 8th international conference on Advances in neural networks - Volume Part I, (176-184)
  59. Guo D A Survey of Signal Propagation in Feedforward Neuronal Networks 8th International Symposium on Advances in Neural Networks --- ISNN 2011 - Volume 6675, (176-184)
  60. Michel V, Eger E, Keribin C and Thirion B (2011). Multiclass sparse Bayesian regression for fMRI-based prediction, Journal of Biomedical Imaging, 2011, (1-13), Online publication date: 1-Jan-2011.
  61. Haefner R and Bethge M Evaluating neuronal codes for inference using Fisher information Proceedings of the 23rd International Conference on Neural Information Processing Systems - Volume 2, (1993-2001)
  62. Merchant H, Bartolo R, Méndez J, Pérez O, Zarco W and Mendoza G What Can Be Inferred from Multiple-task Psychophysical Studies about the Mechanisms for Temporal Processing? Revised Selected Papers of the COST TD0904 International Workshop on Multidisciplinary Aspects of Time and Time Perception - Volume 6789, (207-229)
  63. Michel V, Eger E, Keribin C and Thirion B Multi-class sparse Bayesian regression for neuroimaging data analysis Proceedings of the First international conference on Machine learning in medical imaging, (50-57)
  64. Yusoff N and Grüning A Supervised associative learning in spiking neural network Proceedings of the 20th international conference on Artificial neural networks: Part I, (224-229)
  65. ACM
    Nuño-Maganda M and Torres-Huitzil C (2011). A temporal coding hardware implementation for spiking neural networks, ACM SIGARCH Computer Architecture News, 38:4, (2-7), Online publication date: 14-Sep-2010.
  66. Passot J, Luque N and Arleo A Internal models in the cerebellum Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats, (435-446)
  67. Bothe H and Al-Hamdani S FLIPPS for the Blind Proceedings of the 12th international conference on Computers helping people with special needs, (275-281)
  68. ACM
    Mouret J, Doncieux S and Girard B Importing the computational neuroscience toolbox into neuro-evolution-application to basal ganglia Proceedings of the 12th annual conference on Genetic and evolutionary computation, (587-594)
  69. Smolinski T and Prinz A Rough sets for solving classification problems in computational neuroscience Proceedings of the 7th international conference on Rough sets and current trends in computing, (620-629)
  70. McDonnell M, Burkitt A, Grayden D, Meffin H and Grant A (2010). A channel model for inferring the optimal number of electrodes for future cochlear implants, IEEE Transactions on Information Theory, 56:2, (928-940), Online publication date: 1-Feb-2010.
  71. Suksompong P and Berger T (2010). Capacity analysis for integrate-and-fire neurons with descending action potential thresholds, IEEE Transactions on Information Theory, 56:2, (838-851), Online publication date: 1-Feb-2010.
  72. Wu Q, McGinnity T, Maguire L, Ghani A and Condell J Spiking neural network performs discrete cosine transform for visual images Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications, (21-29)
  73. Kaplan B, Brüderle D, Schemmel J and Meier K High-conductance states in a neuromorphic hardware system Proceedings of the 2009 international joint conference on Neural Networks, (2593-2599)
  74. Guo W and Zhang L Temporal competitive learning induced in neural networks by spike timing-dependent plasticity Proceedings of the 2009 international joint conference on Neural Networks, (291-296)
  75. Chen D, Zhang L and Weng J (2009). Spatio-temporal adaptation in the unsupervised development of networked visual neurons, IEEE Transactions on Neural Networks, 20:6, (992-1008), Online publication date: 1-Jun-2009.
  76. Martins J, Tomás P and Sousa L (2009). Neural code metrics, Neurocomputing, 72:10-12, (2337-2350), Online publication date: 1-Jun-2009.
  77. Anzinger M and Rattay F (2009). Letters, Neurocomputing, 72:7-9, (2032-2034), Online publication date: 1-Mar-2009.
  78. Lazar A and Pnevmatikakis E (2009). Reconstruction of sensory stimuli encoded with integrate-and-fire neurons with random thresholds, EURASIP Journal on Advances in Signal Processing, 2009, (1-14), Online publication date: 1-Jan-2009.
  79. Jin Y, Schramm L and Sendhoff B A gene regulatory model for the development of primitive nervous systems Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I, (48-55)
  80. Wu Q, McGinnity T, Maguire L, Belatreche A and Glackin B (2008). 2D co-ordinate transformation based on a spike timing-dependent plasticity learning mechanism, Neural Networks, 21:9, (1318-1327), Online publication date: 1-Nov-2008.
  81. Wu Q, Mcginnity T, Maguire L, Cai J and Valderrama-Gonzalez G Motion Detection Using Spiking Neural Network Model Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence, (76-83)
  82. Samsonovich A, Ascoli G, Morowitz H and Kalbfleisch M A Scientific Perspective on the Hard Problem of Consciousness Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference, (493-505)
  83. Uysal I, Sathyendra H and Harris J (2008). Towards Spike-Based Speech Processing, International Journal of Applied Mathematics and Computer Science, 18:2, (129-137), Online publication date: 1-Jun-2008.
  84. Kokkinos I, Deriche R, Faugeras O and Maragos P (2008). Computational analysis and learning for a biologically motivated model of boundary detection, Neurocomputing, 71:10-12, (1798-1812), Online publication date: 1-Jun-2008.
  85. Wu Q, McGinnity T, Maguire L, Belatreche A and Glackin B (2008). Processing visual stimuli using hierarchical spiking neural networks, Neurocomputing, 71:10-12, (2055-2068), Online publication date: 1-Jun-2008.
  86. Girard B, Tabareau N, Pham Q, Berthoz A and Slotine J (2008). 2008 Special Issue, Neural Networks, 21:4, (628-641), Online publication date: 1-May-2008.
  87. Piccinini G (2008). Some neural networks compute, others don't, Neural Networks, 21:2, (311-321), Online publication date: 1-Mar-2008.
  88. Patnaik D, Sastry P and Unnikrishnan K (2008). Inferring neuronal network connectivity from spike data, Scientific Programming, 16:1, (49-77), Online publication date: 1-Jan-2008.
  89. ACM
    Ananthanarayanan R and Modha D Anatomy of a cortical simulator Proceedings of the 2007 ACM/IEEE conference on Supercomputing, (1-12)
  90. Erny J, Pastor J and Prade H SimBa Proceedings of the 17th international conference on Artificial neural networks, (29-38)
  91. Liu C and Shapiro J Implementing classical conditioning with spiking neurons Proceedings of the 17th international conference on Artificial neural networks, (400-410)
  92. Brüderle D, Grübl A, Meier K, Mueller E and Schemmel J A software framework for tuning the dynamics of neuromorphic silicon towards biology Proceedings of the 9th international work conference on Artificial neural networks, (479-486)
  93. Rao A, Cecchi G, Peck C and Kozloski J Emergence of Topographic Cortical Maps in a Parameterless Local Competition Network Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks, (552-561)
  94. Wilimzig C, Schneider S and Schöner G (2006). The time course of saccadic decision making, Neural Networks, 19:8, (1059-1074), Online publication date: 1-Oct-2006.
  95. Schulzke E and Eurich C Neuronal coding strategies for two-alternative forced choice tasks Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I, (235-240)
  96. Haykin S and Chen Z (2005). The Cocktail Party Problem, Neural Computation, 17:9, (1875-1902), Online publication date: 1-Sep-2005.
  97. Di Crescenzo A, Martinucci B and Pirozzi E Feedback effects in simulated stein's coupled neurons Proceedings of the 10th international conference on Computer Aided Systems Theory, (436-446)
  98. Schneider S, Igel C, Klaes C, Dinse H and Wiemer J (2004). Evolutionary Adaptation of Nonlinear Dynamical Systems in Computational Neuroscience, Genetic Programming and Evolvable Machines, 5:2, (215-227), Online publication date: 1-Jun-2004.
  99. Guigon E (2003). Computing with populations of monotonically tuned neurons, Neural Computation, 15:9, (2115-2127), Online publication date: 1-Sep-2003.
  100. Ascoli G (2003). Passive dendritic integration heavily affects spiking dynamics of recurrent networks, Neural Networks, 16:5-6, (657-663), Online publication date: 1-Jun-2003.
Contributors
  • Max Planck Institute for Biological Cybernetics
  • Vagelos College of Physicians and Surgeons

Recommendations