Models of Information Processing in the Basal Ganglia

The credit assignment problem in cortico‐basal ganglia‐thalamic networks: A review, a problem and a possible solution

Rubin, J. E., Vich, C., Clapp, M., Noneman, K. K., & Verstynen, T. (2020) · European Journal of Neuroscience · doi:10.1111/ejn.14745 · Cited by 24

A modular neural model of motor synergies

Byadarhaly, K. V., Perdoor, M. C., & Minai, A. A. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.02.003 · Cited by 20

Speech and language disorders associated with subcortical pathology

Murdoch, B. E., & Whelan, B. (2009) · Wiley-Blackwell eBooks · Cited by 20

Criteria for an effective theory of consciousness and some preliminary attempts

Coward, L. A., & Sun, R. (2003) · Consciousness and Cognition · doi:10.1016/j.concog.2003.09.002 · Cited by 20

Chemical architecture of the basal ganglia

Graybiel, A. M., & Penney, J. B. (1999) · Handbook of chemical neuroanatomy · doi:10.1016/s0924-8196(99)80025-6 · Cited by 20

Model-based reinforcement learning: a computational model and an fMRI study

Yoshida, W., & Ishii, S. (2004) · Neurocomputing · doi:10.1016/j.neucom.2004.04.012 · Cited by 20

A Bioinspired Hierarchical Reinforcement Learning Architecture for Modeling Learning of Multiple Skills with Continuous States and Actions.

Caligiore, D., Mirolli, M., Parisi, D., et al. (2010) · Cited by 19

Functional Relevance of Different Basal Ganglia Pathways Investigated in a Spiking Model with Reward Dependent Plasticity

Berthet, P., Lindahl, M., Tully, P. J., Kotaleski, J. H., & Lansner, A. (2016) · Frontiers in Neural Circuits · doi:10.3389/fncir.2016.00053 · Cited by 19

A Computational Model of Learned Avoidance Behavior in a One-Way Avoidance Experiment

Johnson, J. D., Li, W., Li, J., & Klopf, A. H. (2001) · Adaptive Behavior · doi:10.1177/105971230200900205 · Cited by 20

Differential effects of calcineurin inhibition and protein kinase A activation on nucleus accumbens amphetamine‐produced conditioned place preference in rats

Gerdjikov, T. V., & Beninger, R. J. (2005) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2005.04256.x · Cited by 19

Self-Organization in the Basal Ganglia with Modulation of Reinforcement Signals

Nakahara, H., Амари, Ш.-И., & Hikosaka, O. (2002) · Neural Computation · doi:10.1162/089976602317318974 · Cited by 19

Comparing a Brain-inspired Robot Action Selection Mechanism with Winner-takes-all

Girard, B., Cuzin, V., Guillot, A., et al. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/3121.003.0015 · Cited by 19

Selective enhancement of recall through plasticity modulation in an autoassociative memory

Sandberg, A., Lansner, A., & Petersson, K. M. (2001) · Neurocomputing · doi:10.1016/s0925-2312(01)00363-0 · Cited by 19

Effects of potassium channel blockers on synaptic plasticity in the corticostriatal pathway

Wickens, J. R., Mckenzie, D., Costanzo, E. M., et al. (1998) · Neuropharmacology · doi:10.1016/s0028-3908(98)00054-9 · Cited by 19

Crossed Corticostriatal Projections in the Macaque Brain

Borra, E., Biancheri, D., Rizzo, M., Leonardi, F., & Luppino, G. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.0071-22.2022 · Cited by 18

Learning to grasp and extract affordances: the Integrated Learning of Grasps and Affordances (ILGA) model

Bonaiuto, J. J., & Arbib, M. A. (2015) · Biological Cybernetics · doi:10.1007/s00422-015-0666-2 · Cited by 18

The Hierarchical Organisation of Cortical and Basal-Ganglia Systems: A Computationally-Informed Review and Integrated Hypothesis

Baldassarre, G., Caligiore, D., & Mannella, F. (2013) · Springer eBooks · doi:10.1007/978-3-642-39875-9_11 · Cited by 18

Theoretical note: the C/T ratio in artificial neural networks

Burgos, J. E. (2005) · Behavioural Processes · doi:10.1016/j.beproc.2005.02.008 · Cited by 18

The storehouse/correspondence partition in memory research: Promises and perils

Kruglanski, A. W. (1996) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00042242 · Cited by 18

Adaptive learning via selectionism and Bayesianism, Part I: Connection between the two

Zhang, J. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.03.018 · Cited by 17

Dopamine-Glutamate Interactions in Reward-Related Incentive Learning

Beninger, R. J., & Gerdjikov, T. V. (2005) · Humana Press eBooks · doi:10.1007/978-1-59259-852-6_14 · Cited by 17

Physiotherapy for people with movement disorders arising from basal ganglia dysfunction

Bilney, B., Morris, M. E., & Denisenko, S. (2003) · Figshare · doi:10.26181/22200505 · Cited by 17

Dopamine disruption increases cleanerfish cooperative investment in novel client partners

Soares, M. C., Santos, T. P., & Messias, J. P. M. (2017) · Royal Society Open Science · doi:10.1098/rsos.160609 · Cited by 17

Cross-modal information integration in category learning

Smith, J. D., Johnston, J. R., Musgrave, R. D., Zakrzewski, A. C., Boomer, J. B., Church, B. A., & Ashby, F. G. (2014) · Attention Perception & Psychophysics · doi:10.3758/s13414-014-0659-6 · Cited by 16

Prospection by Any Other Name? A Response to Seligman et al. (2013)

Fukukura, J., Helzer, E. G., & Ferguson, M. J. (2013) · Perspectives on Psychological Science · doi:10.1177/1745691612474320 · Cited by 18

Mathematical properties of neuronal TD-rules and differential Hebbian learning: a comparison

Kolodziejski, C., Porr, B., & Wörgötter, F. (2008) · Biological Cybernetics · doi:10.1007/s00422-007-0209-6 · Cited by 17

Attention and the control of action: An investigation of the effects of selection on population coding of hand and eye movement

Houghton, G., & Tipper, S. P. (1999) · Perspectives in neural computing · doi:10.1007/978-1-4471-0813-9_25 · Cited by 17

Chapter IV Structural and functional interactions in the striatum at the receptor level

Wickens, J. R., & Arbuthnott, G. W. (2005) · Handbook of chemical neuroanatomy · doi:10.1016/s0924-8196(05)80008-9 · Cited by 19

Using a Time-Delay Actor-Critic Neural Architecture with Dopamine-Like Reinforcement Signal for Learning in Autonomous Robots

Pérez-Uribe, A. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44597-8_37 · Cited by 17

The Corticospinal System: A Structural Framework for the Central Control of Movement

Dum, R. P., & Strick, P. L. (1996) · Comprehensive physiology · doi:10.1002/cphy.cp120106 · Cited by 20

Effect on movement selection of an evolving sensory representation: A multiple controller model of skill acquisition

Shah, A., & Barto, A. G. (2009) · Brain Research · doi:10.1016/j.brainres.2009.07.006 · Cited by 16

A computational model for optimal muscle activity considering muscle viscoelasticity in wrist movements

Kambara, H., Shin, D., & Koike, Y. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00542.2011 · Cited by 16

A Spiking Neural Model for Stable Reinforcement of Synapses Based on Multiple Distal Rewards

O'Brien, M. J., & Srinivasa, N. (2012) · Neural Computation · doi:10.1162/neco_a_00387 · Cited by 16

Learning to select actions shapes recurrent dynamics in the corticostriatal system

Márton, C. D., Schultz, S. R., & Averbeck, B. B. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.09.008 · Cited by 18

The Basal Ganglia System as an Engine for Exploration

Chakravarthy, V. S., & Balasubramani, P. P. (2018) · Cognitive science and technology · doi:10.1007/978-981-10-8494-2_5 · Cited by 16

Striatal patch compartment lesions alter methamphetamine-induced behavior and immediate early gene expression in the striatum, substantia nigra and frontal cortex

Murray, R. C., Gilbert, Y. E., Logan, A. S., Hebbard, J. C., & Horner, K. A. (2013) · Brain Structure and Function · doi:10.1007/s00429-013-0559-x · Cited by 17

Executive attention, task selection and attention-based learning in a neurally controlled simulated robot

Garforth, J., McHale, S., & Meehan, A. S. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.11.018 · Cited by 16

Implicit category learning performance predicts rate of cognitive decline in nondemented patients with Parkinson's disease.

Filoteo, J. V., Maddox, W. T., Salmon, D. P., et al. (2007) · Neuropsychology · doi:10.1037/0894-4105.21.2.183 · Cited by 16

Preferences Constructed From Dynamic Microprocessing Mechanisms

Busemeyer, J. R., Johnson, J. G., & Jessup, R. K. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511618031.012 · Cited by 16

An overview of AIDS dementia complex

Meehan, R. A., & Brush, J. (2001) · American Journal of Alzheimer s Disease & Other Dementias® · doi:10.1177/153331750101600411 · Cited by 16

An Artificial Neural Network That Uses Eye-Tracking Performance to Identify Patients With Schizophrenia

Campana, A., Duci, A., Gambini, O., & Scarone, S. (1999) · Schizophrenia Bulletin · doi:10.1093/oxfordjournals.schbul.a033419 · Cited by 17

On the Role of Dopamine in Cognitive Vision

Vitay, J., & Hamker, F. H. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-77343-6_23 · Cited by 16

Selection Networks: Simulation of Plasticity through Reinforcement Learning

Donahoe, J. W. (1997) · Advances in psychology · doi:10.1016/s0166-4115(97)80104-5 · Cited by 16

A neural-network reinforcement-learning model of domestic chicks that learn to localize the centre of closed arenas

Mannella, F., & Baldassarre, G. (2007) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2006.1966 · Cited by 18

Rigid reduced successor representation as a potential mechanism for addiction

Shimomura, K., Kato, A., & Morita, K. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.15227 · Cited by 15

Exploration from Generalization Mediated by Multiple Controllers

Dayan, P. (2012) · Springer eBooks · doi:10.1007/978-3-642-32375-1_4 · Cited by 15

Hierarchical reinforcement learning and central pattern generators for modeling the development of rhythmic manipulation skills

Ciancio, A. L., Zollo, L., Guglielmelli, E., Caligiore, D., & Baldassarre, G. (2011) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/devlrn.2011.6037370 · Cited by 15

Decision making under uncertainty in a spiking neural network model of the basal ganglia

Héricé, C., Khalil, R., Moftah, M. Z., Boraud, T., Guthrie, M., & Garenne, A. (2016) · Journal of Integrative Neuroscience · doi:10.1142/s021963521650028x · Cited by 14

Why noise is useful in functional and neural mechanisms of interval timing?

Oprisan, S. A., & Buhusi, C. V. (2013) · BMC Neuroscience · doi:10.1186/1471-2202-14-84 · Cited by 15

Organization of Corticostriatal Projection Neuron Types

Reiner, A. J., & Deng, Y. (2016) · Handbook of behavioral neuroscience · doi:10.1016/b978-0-12-802206-1.00020-9 · Cited by 16

Effect of sensory stimulation in rat barrel cortex, dorsolateral striatum and on corticostriatal functional connectivity

Syed, E. C. J., Sharott, A., Moll, C. K. E., et al. (2010) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2010.07549.x · Cited by 15

Differential changes of synaptic transmission in spiny neurons of rat neostriatum following transient forebrain ischemia

Gajendiran, M., Ling, G. Y., Pang, Z. P., et al. (2001) · Neuroscience · doi:10.1016/s0306-4522(01)00163-4 · Cited by 15

Perceptual-motor sequence learning of general regularities and specific sequences.

Marsolek, C. J., & Field, J. E. (1999) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037//0096-1523.25.3.815 · Cited by 15

Reinforcement Learning: An Introduction; R.S. Sutton, A.G. Barto (Eds.); MIT Press, Cambridge, MA, 1998, 380 pages, ISBN 0-262-19398-1, $42.00

Rao, R. P. N. (2000) · Neural Networks · doi:10.1016/s0893-6080(99)00098-2 · Cited by 15

‘Proactive’ use of cue-context congruence for building reinforcement learning’s reward function

Zsuga, J., Bíró, K., Tajti, G., Szilasi, M. E., Papp, C., Juhász, B., & Gesztelyi, R. (2016) · BMC Neuroscience · doi:10.1186/s12868-016-0302-7 · Cited by 13

Deciding Which Skill to Learn When: Temporal-Difference Competence-Based Intrinsic Motivation (TD-CB-IM)

Baldassarre, G., & Mirolli, M. (2012) · Springer eBooks · doi:10.1007/978-3-642-32375-1_11 · Cited by 14

Modular and hierarchical brain organization to understand assimilation, accommodation and their relation to autism in reaching tasks: a developmental robotics hypothesis

Caligiore, D., Tommasino, P., Sperati, V., & Baldassarre, G. (2014) · Adaptive Behavior · doi:10.1177/1059712314539710 · Cited by 15

CHANGE DETECTION, MULTIPLE CONTROLLERS, AND DYNAMIC ENVIRONMENTS: INSIGHTS FROM THE BRAIN

Pearson, J. M., & Platt, M. L. (2012) · Journal of the Experimental Analysis of Behavior · doi:10.1002/jeab.5 · Cited by 14

Basal Ganglia Neural Coding of Natural Action Sequences

Aldridge, J. W., & Berridge, K. C. (2002) · Advances in behavioral biology · doi:10.1007/978-1-4615-0179-4_28 · Cited by 14

Dopamine and serotonin signals for reward across time scales

Cohen, J. Y. (2015) · Science · doi:10.1126/science.aad3003 · Cited by 14

Dual-System Learning Models and Drugs of Abuse

Simon, D. A., & Daw, N. D. (2011) · Springer eBooks · doi:10.1007/978-1-4614-0751-5_5 · Cited by 22

Bio-inspired Model of Robot Adaptive Learning and Mapping

Ramírez, A. B., & Ridel, A. W. (2006) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2006.282344 · Cited by 14

Independent Judgment-Linked and Motor-Linked Forms of Artificial Grammar Learning

Seger, C. A. (1998) · Consciousness and Cognition · doi:10.1006/ccog.1997.0331 · Cited by 12

Molecular Adaptations to Psychostimulants in Striatal Neurons: Toward a Pathophysiology of Addiction

Genova, L. M., Berke, J. D., & Hyman, S. E. (1997) · Neurobiology of Disease · doi:10.1006/nbdi.1997.0154 · Cited by 14

Optimal neurocontrol: practical benefits, new results and biological evidence

Werbos, P. J. (2002) · Western Electronic Show and Convention (WESCON) · doi:10.1109/wescon.1995.485445 · Cited by 14

Searching for the Holy Grail: Temporally Informative Firing Patterns in the Rat

Matell, M. S. (2014) · Advances in experimental medicine and biology · doi:10.1007/978-1-4939-1782-2_12 · Cited by 16

Connectionist Models of Reinforcement, Imitation, and Instruction in Learning to Solve Complex Problems

Dandurand, F., & Shultz, T. R. (2009) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2009.2031234 · Cited by 15

Cumulative Learning Through Intrinsic Reinforcements

Santucci, V. G., Baldassarre, G., & Mirolli, M. (2013) · Springer eBooks · doi:10.1007/978-3-642-37577-4_7 · Cited by 14

Neuroscientific implications for situated and embodied artificial intelligence

Downing, K. L. (2007) · Connection Science · doi:10.1080/09540090701192584 · Cited by 13

An Action-Selection Calculus

Witkowski, M. (2007) · Adaptive Behavior · doi:10.1177/1059712306076254 · Cited by 13

DYT1 dystonia increases risk taking in humans

Arkadir, D., Radulescu, A., Raymond, D., Lubarr, N., Bressman, S., Mazzoni, P., & Niv, Y. (2016) · eLife · doi:10.7554/elife.14155 · Cited by 14

Correlates of reward‐predictive value in learning‐related hippocampal neural activity

Okatan, M. (2009) · Hippocampus · doi:10.1002/hipo.20535 · Cited by 14

Synaptic plasticity model of a spiking neural network for reinforcement learning

Lee, K., & Kwon, D. (2008) · Neurocomputing · doi:10.1016/j.neucom.2007.09.009 · Cited by 13

A Model of Reaching that Integrates Reinforcement Learning and Population Encoding of Postures

Ognibene, D., Rega, A., & Baldassarre, G. (2006) · Lecture notes in computer science · doi:10.1007/11840541_32 · Cited by 12

The organization of behaviour revisited.

Fentress, J. C. (1999) · Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · doi:10.1037/h0087296 · Cited by 13

Neuroanatomía funcional de los aprendizajes implícitos: asociativos, motores y de hábito

Sanz, M. C. (2007) · Revista de Neurología · doi:10.33588/rn.4404.2006060 · Cited by 13

The Role of Learning and Kinematic Features in Dexterous Manipulation: A Comparative Study with Two Robotic Hands

Ciancio, A. L., Zollo, L., Baldassarre, G., Caligiore, D., & Guglielmelli, E. (2013) · International Journal of Advanced Robotic Systems · doi:10.5772/56479 · Cited by 11

Expanding the role of striatal cholinergic interneurons and the midbrain dopamine system in appetitive instrumental conditioning

Crossley, M. J., Horvitz, J. C., Balsam, P. D., & Ashby, F. G. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00473.2015 · Cited by 13

Tonically Active Neurons in the Monkey Dorsal Striatum Signal Outcome Feedback during Trial-and-error Search Behavior

Inokawa, H., Matsumoto, N., Kimura, M., & Yamada, H. (2020) · Neuroscience · doi:10.1016/j.neuroscience.2020.08.007 · Cited by 9

The dopamine circuit as a reward-taxis navigation system

Karin, O., & Alon, U. S. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010340 · Cited by 14

Basal Ganglia Circuitry and Synaptic Connectivity

Charara, A., Sidibé, M., & Smith, Y. (2003) · Humana Press eBooks · doi:10.1007/978-1-59259-312-5_2 · Cited by 12

A Neurocomputational Model of Automatic Sequence Production

Hélie, S., Roeder, J. L., Vucovich, L. E., Rünger, D., & Ashby, F. G. (2015) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00794 · Cited by 12

Gating of Cortical Input to the Striatum

Wickens, J. R., & Arbuthnott, G. W. (2010) · Handbook of behavioral neuroscience · doi:10.1016/b978-0-12-374767-9.00019-6 · Cited by 15

Static and Dynamic State Feedback Control Model of Basal Ganglia-Thalamocortical Loops

Lörincz, A. (1997) · International Journal of Neural Systems · doi:10.1142/s0129065797000343 · Cited by 12

Age-Related Change in Cognitive Function

Wilson, R. S., Bennett, D. A., & Swartzendruber, A. L. (1997) · Critical issues in neuropsychology · doi:10.1007/978-1-4899-1857-4_2 · Cited by 14

Using Classification to Understand the Motivation‐Learning Interface

Maddox, W. T., Markman, A. B., & Baldwin, G. C. (2006) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/s0079-7421(06)47007-9 · Cited by 12

Erratum to: Path-finding in real and simulated rats: assessing the influence of path characteristics on navigation learning

Tamošiūnaitė, M., Ainge, J. A., Kulvičius, T., Porr, B., Dudchenko, P. A., & Wörgötter, F. (2010) · Journal of Computational Neuroscience · doi:10.1007/s10827-010-0217-8 · Cited by 12

The architecture challenge: Future artificial-intelligence systems will require sophisticated architectures, and knowledge of the brain might guide their construction

Baldassarre, G., Santucci, V. G., Cartoni, E., & Caligiore, D. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x17000036 · Cited by 11

An immersive virtual reality tool for assessing left and right unilateral spatial neglect

Thomasson, M., Pérez-Marcos, D., Crottaz‐Herbette, S., Brenet, F., Saj̈, A., Bernati, T., Serino, A., Tadi, T., Blanke, O., & Ronchi, R. (2024) · Journal of Neuropsychology · doi:10.1111/jnp.12361 · Cited by 11

Creating the brain and interacting with the brain: an integrated approach to understanding the brain

Morimoto, J., & Kawato, M. (2015) · Journal of The Royal Society Interface · doi:10.1098/rsif.2014.1250 · Cited by 10

Modeling the sub-cellular signaling pathways involved in reinforcement learning at the striatum

Wanjerkhede, S. M., & Bapi, R. S. (2007) · Progress in brain research · doi:10.1016/s0079-6123(07)68016-9 · Cited by 11

The Development of Action Sequences

Fentress, J. C., & Gadbois, S. (2001) · Handbook of behavioral neurobiology · doi:10.1007/978-1-4615-1209-7_11 · Cited by 13

ACE (Actor–Critic–Explorer) paradigm for reinforcement learning in basal ganglia: Highlighting the role of subthalamic and pallidal nuclei

Joseph, D., Garipelli, G., & Chakravarthy, V. S. (2010) · Neurocomputing · doi:10.1016/j.neucom.2010.03.001 · Cited by 11

An implementation of reinforcement learning based on spike timing dependent plasticity

Roberts, P. D., Santiago, R. A., & Lafferriere, G. (2008) · Biological Cybernetics · doi:10.1007/s00422-008-0265-6 · Cited by 11

On the psychobiological complexity and stability of traits

Depue, R. A., & Collins, P. F. (1999) · Behavioral and Brain Sciences · doi:10.1017/s0140525x99462041 · Cited by 13

THE EFFECTS OF MOTIVATION ON HABITUAL INSTRUMENTAL BEHAVIOR

Niv, Y. (2007) · Cited by 11

Nucleus accumbens and dorsal medial striatal dopamine and neural activity are essential for action sequence performance

Fraser, K. M., Chen, B. J., & Janak, P. H. (2023) · European Journal of Neuroscience · doi:10.1111/ejn.16210 · Cited by 11

Chapter V Motor function(s) of the nigrostriatal dopamine system: Studies of lesions and behavior

Dunnett, S. B. (2005) · Handbook of chemical neuroanatomy · doi:10.1016/s0924-8196(05)80009-0 · Cited by 10

Conditional visuo-motor learning and dimension reduction

Hadj‐Bouziane, F., Frankowska, H., Meunier, M., Coquelin, P.-A., & Boussaoud, D. (2006) · Cognitive Processing · doi:10.1007/s10339-005-0028-4 · Cited by 10

A Population-Based Model of the Temporal Memory in the Hippocampus

Oprisan, S. A., Buhusi, M. C., & Buhusi, C. V. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00521 · Cited by 11

Connectionist approaches to development

(1996) · Infant Behavior and Development · doi:10.1016/s0163-6383(96)90232-5

Connectionism in a broad perspective : selected papers from the Swedish Conference on Connectionism, 1992

Niklasson, L., & Bodén, M. (1994) · Ellis Horwood eBooks · Cited by 2

What Should a Connectionist Philosophy of Science Look Like

McCauley, R. N., & Bechtel, W. (2002) · Cited by 34

Connectionism and Systematicity

Matthews, R. J. (2005) · Wiley eBooks · doi:10.1002/0470018860.s00164 · Cited by 2

From Paleo- to Neo-Connectionism

Andler, D. (1992) · Springer eBooks · doi:10.1007/978-94-015-8062-5_8 · Cited by 9