World Scientific
  • Search
  •   
Skip main navigation

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×

System Upgrade on Tue, May 28th, 2024 at 2am (EDT)

Existing users will be able to log into the site and access content. However, E-commerce and registration of new users may not be available for up to 12 hours.
For online purchase, please visit us again. Contact us at [email protected] for any enquiries.

How the Cerebellum and Prefrontal Cortex Cooperate During Trace Eyeblinking Conditioning

    https://doi.org/10.1142/S0129065720500410Cited by:2 (Source: Crossref)

    Several data have demonstrated that during the widely used experimental paradigm for studying associative learning, trace eye blinking conditioning (TEBC), there is a strong interaction between cerebellum and medial prefrontal cortex (mPFC). Despite this evidence, the neural mechanisms underlying this interaction are still not clear. Here, we propose a neurophysiologically plausible computational model to address this issue. The model is constrained on the basis of two critical anatomo-physiological features: (i) the cerebello-cortical organization through two circuits, respectively, targeting M1 and mPFC; (ii) the different timing in the plasticity mechanisms of these parallel circuits produced by the granule cells time sensitivity according to which different subpopulations are active at different moments during conditioned stimuli. The computer simulations run with the model suggest that these features are critical to understand how the cooperation between cerebellum and mPFC supports motor areas during TEBC. In particular, a greater trace interval produces greater plasticity changes at the slow path synapses involving mPFC with respect to plasticity changes at the fast path involving M1. As a consequence, the greater is the trace interval, the stronger is the mPFC involvement. The model has been validated by reproducing data collected through recent real mice experiments.

    References

    • 1. C. Weiss and J. F. Disterhoft , Exploring prefrontal cortical memory mechanisms with eyeblink conditioning, Behav. Neurosci. 125 (2011) 318–326. Crossref, Medline, Web of ScienceGoogle Scholar
    • 2. J. J. Siegel, W. Taylor, R. Gray, B. Kalmbach, B. V. Zemelman, N. S. Desai, D. Johnston and R. A. Chitwood , Trace Eyeblink Conditioning in Mice Is Dependent upon the Dorsal Medial Prefrontal Cortex, Cerebellum, and Amygdala: Behavioral Characterization and Functional Circuitry, eNeuro 2 (2015) 1–29. Crossref, Web of ScienceGoogle Scholar
    • 3. J. E. Steinmetz, D. G. Lavond and R. F. Thompson , Classical conditioning in rabbits using pontine nucleus stimulation as a conditioned stimulus and inferior olive stimulation as an unconditioned stimulus, Synapse 3 (1989) 225–233. Crossref, Medline, Web of ScienceGoogle Scholar
    • 4. B. E. Kalmbach, T. Ohyama, J. C. Kreider, F. Riusech and M. D. Mauk , Interactions between prefrontal cortex and cerebellum revealed by trace eyelid conditioning, Learn. Mem. 16 (2009) 86–95. Crossref, Medline, Web of ScienceGoogle Scholar
    • 5. H. Chen, Y.-J. Wang, L. Yang, J.-F. Sui, Z.-A. Hu and B. Hu , Theta synchronization between medial prefrontal cortex and cerebellum is associated with adaptive performance of associative learning behavior, Sci. Rep. 6 (2016) 20960. Crossref, Medline, Web of ScienceGoogle Scholar
    • 6. D. S. Woodruff-Pak and J. F. Disterhoft , Where is the trace in trace conditioning? Trends Neurosci. 31 (2008) 105–112. Crossref, Medline, Web of ScienceGoogle Scholar
    • 7. M. Longley and C. H. Yeo , Distribution of Neural Plasticity in Cerebellum-Dependent Motor Learning, Prog. Brain Res. 210 (2014) 79–101. Crossref, Medline, Web of ScienceGoogle Scholar
    • 8. J. J. Kim, R. E. Clark and R. F. Thompson , Hippocampectomy impairs the memory of recently, but not remotely, acquired trace eyeblink conditioned responses, Behav. Neurosci. 109 (1995) 195–203. Crossref, Medline, Web of ScienceGoogle Scholar
    • 9. D. T. Cheng, J. F. Disterhoft, J. M. Power, D. A. Ellis and J. E. Desmond , Neural substrates underlying human delay and trace eyeblink conditioning, Proc. Natl. Acad. Sci. USA 105 (2008) 8108–8113. Crossref, Medline, Web of ScienceGoogle Scholar
    • 10. R. Thompson and J. Steinmetz , The role of the cerebellum in classical conditioning of discrete behavioral responses, Neuroscience 162 (2009) 732–755. Crossref, Medline, Web of ScienceGoogle Scholar
    • 11. J. H. Freeman and A. B. Steinmetz , Neural circuitry and plasticity mechanisms underlying delay eyeblink conditioning, Learn. Mem. 18 (2011) 666–677. Crossref, Medline, Web of ScienceGoogle Scholar
    • 12. N. Ramnani , The primate cortico-cerebellar system: anatomy and function, Nat. Rev. Neurosci. 7 (2006) 511–522. Crossref, Medline, Web of ScienceGoogle Scholar
    • 13. P. L. Strick, R. P. Dum and J. A. Fiez , Cerebellum and Nonmotor Function, Ann. Rev. Neurosci. 32 (2009) 413–434. Crossref, Medline, Web of ScienceGoogle Scholar
    • 14. H. Chen, L. Yang, Y. Xu, G.-Y. Wu, J. Yao, J. Zhang, Z.-R. Zhu, Z.-A. Hu, J.-F. Sui and B. Hu , Prefrontal Control of Cerebellum-Dependent Associative Motor Learning, Cerebellum 13 (2014) 64–78. Crossref, Medline, Web of ScienceGoogle Scholar
    • 15. M. A. Kronforst-Collins and J. F. Disterhoft , Lesions of the caudal area of rabbit medial prefrontal cortex impair trace eyeblink conditioning, Neurobiol. Learn. Mem. 69 (1998) 147–162. Crossref, Medline, Web of ScienceGoogle Scholar
    • 16. J. McLaughlin, H. Skaggs, J. Churchwell and D. A. Powell , Medial prefrontal cortex and pavlovian conditioning: Trace versus delay conditioning, Behav. Neurosci. 116 (2002) 37–47. Crossref, Medline, Web of ScienceGoogle Scholar
    • 17. K. Takehara-Nishiuchi and B. L. McNaughton , Spontaneous changes of neocortical code for associative memory during consolidation, Science 322 (2008) 960–963. Crossref, Medline, Web of ScienceGoogle Scholar
    • 18. J. J. Siegel , Modification of persistent responses in medial prefrontal cortex during learning in trace eyeblink conditioning, J. Neurophysiol. 112 (2014) 2123–2137. Crossref, Medline, Web of ScienceGoogle Scholar
    • 19. S. Hattori, T. Yoon, J. F. Disterhoft and C. Weiss , Functional reorganization of a prefrontal cortical network mediating consolidation of trace eyeblink conditioning, J. Neurosci. 34 (2014) 1432–1445. Crossref, Medline, Web of ScienceGoogle Scholar
    • 20. R. M. Kelly and P. L. Strick , Cerebellar loops with motor cortex and prefrontal cortex of a nonhuman primate, J. Neurosci. 23 (2003) 8432–8444. Crossref, Medline, Web of ScienceGoogle Scholar
    • 21. D. Caligiore, G. Pezzulo, R. C. Miall and G. Baldassarre , The contribution of brain sub-cortical loops in the expression and acquisition of action understanding abilities, Neurosci. Biobehav. Rev. 37 (2013) 2504–2515. Crossref, Medline, Web of ScienceGoogle Scholar
    • 22. D. Caligiore, G. Pezzulo, G. Baldassarre, A. C. Bostan, P. L. Strick, K. Doya, R. C. Helmich, M. Dirkx, J. Houk, H. Jörntell, A. Lago-Rodriguez, J. M. Galea, R. C. Miall, T. Popa, A. Kishore, P. F. M. J. Verschure, R. Zucca and I. Herreros , Consensus Paper: Towards a Systems-Level View of Cerebellar Function: The Interplay Between Cerebellum, Basal Ganglia, and Cortex, Cerebellum 16 (2017) 203–229. Crossref, Medline, Web of ScienceGoogle Scholar
    • 23. J. F. Medina, K. S. Garcia, W. L. Nores, N. M. Taylor and M. D. Mauk , Timing mechanisms in the cerebellum: testing predictions of a large-scale computer simulation, J. Neurosci. 20 (2000) 5516–5525. Crossref, Medline, Web of ScienceGoogle Scholar
    • 24. E. D’Angelo and C. I. De Zeeuw , Timing and plasticity in the cerebellum: Focus on the granular layer, Trends Neurosci. 32 (2009) 30–40. Crossref, Medline, Web of ScienceGoogle Scholar
    • 25. S. K. Sudhakar, S. Hong, I. Raikov, R. Publio, C. Lang, T. Close, D. Guo, M. Negrello and E. De Schutter , Spatiotemporal network coding of physiological mossy fiber inputs by the cerebellar granular layer, PLoS Comput. Biol. 13 (2017) e1005754. Crossref, Medline, Web of ScienceGoogle Scholar
    • 26. J. M. Eppler, M. Helias, E. Muller, M. Diesmann and M. Gewaltig , Pynest: A convenient interface to the nest simulator, Front. Neuroinform. 2 (2009) 12. MedlineGoogle Scholar
    • 27. M.-O. Gewaltig and M. Diesmann , Nest (neural simulation tool), Scholarpedia 2 (2007) 1430. CrossrefGoogle Scholar
    • 28. M. Tsodyks, A. Uziel and H. Markram , Synchrony generation in recurrent networks with frequency-dependent synapses, J. Neurosci. 20 (2000) 825–835. Crossref, Web of ScienceGoogle Scholar
    • 29. A. N. Burkitt , A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input, Biolo. Cybern. 95 (2006) 1–19. Crossref, Medline, Web of ScienceGoogle Scholar
    • 30. S. Ghosh-Dastidar and H. Adeli , Spiking neural networks, Int. J. Neural Syst. 19 (2009) 295–308. Link, Web of ScienceGoogle Scholar
    • 31. A. Geminiani, C. Casellato, A. Antonietti, E. D’Angelo and A. Pedrocchi , A Multiple-Plasticity Spiking Neural Network Embedded in a Closed-Loop Control System to Model Cerebellar Pathologies, Int. J. Neural Syst. 28 (2018) 1750017. Link, Web of ScienceGoogle Scholar
    • 32. A. Antonietti, J. Monaco, E. D’Angelo, A. Pedrocchi and C. Casellato , Dynamic redistribution of plasticity in a cerebellar spiking neural network reproducing an associative learning task perturbed by TMS, Int. J. Neural Syst. 28 (2018) 1850020. Link, Web of ScienceGoogle Scholar
    • 33. M. Ito , Cerebellar microcomplexes, Int. Rev. Neurobiol. 41 (1997) 475–487. Crossref, Medline, Web of ScienceGoogle Scholar
    • 34. M. A. Arbib and J. Spoelstra , Microcomplexes: The basic unit of the cerebellar role in adaptive motor control, Behav. Brain Sci. 20 (1997) 245–246. Crossref, Web of ScienceGoogle Scholar
    • 35. E. D’Angelo, A. Antonietti, S. Casali, C. Casellato, J. A. Garrido, N. R. Luque, L. Mapelli, S. Masoli, A. Pedrocchi, F. Prestori, M. F. Rizza and E. Ros , Modeling the cerebellar microcircuit: New strategies for a long-standing issue, Front. Cell. Neurosci. 10 (2016) 176. Medline, Web of ScienceGoogle Scholar
    • 36. F. A. Middleton and P. L. Strick , Anatomical evidence for cerebellar and basal ganglia involvement in higher cognitive function, Science 266 (1994) 458–461. Crossref, Medline, Web of ScienceGoogle Scholar
    • 37. F. A. Middleton and P. L. Strick , Basal ganglia and cerebellar loops: motor and cognitive circuits, Brain Res. Brain Res. Rev. 31 (2000) 236–250. Crossref, MedlineGoogle Scholar
    • 38. R. P. Dum and P. L. Strick , An unfolded map of the cerebellar dentate nucleus and its projections to the cerebral cortex, J. Neurophysiol. 89 (2003) 634–639. Crossref, Medline, Web of ScienceGoogle Scholar
    • 39. S. Miyachi, X. Lu, S. Inoue, T. Iwasaki, S. Koike, A. Nambu and M. Takada , Organization of multisynaptic inputs from prefrontal cortex to primary motor cortex as revealed by retrograde transneuronal transport of rabies virus, J. Neurosci. 25 (2005) 2547–2556. Crossref, Medline, Web of ScienceGoogle Scholar
    • 40. N. S. Narayanan and M. Laubach , Top-down control of motor cortex ensembles by dorsomedial prefrontal cortex, Neuron 52 (2006) 921–931. Crossref, Medline, Web of ScienceGoogle Scholar
    • 41. K. Nakamura and H. Kawabata , Transcranial direct current stimulation over the medial prefrontal cortex and left primary motor cortex (mPFC-lPMC) affects subjective beauty but not ugliness, Front. Human Neurosci. 9 (2015) 654. Crossref, Medline, Web of ScienceGoogle Scholar
    • 42. M. D. Mauk and D. V. Buonomano , The neural basis of temporal processing, Ann. Rev. Neurosci. 27 (2004) 307–340. Crossref, Medline, Web of ScienceGoogle Scholar
    • 43. M. Bareš, R. Apps, L. Avanzino, A. Breska, E. D’Angelo, P. Filip, M. Gerwig, R. B. Ivry, C. L. Lawrenson, E. D. Louis, N. A. Lusk, M. Manto, W. H. Meck, H. Mitoma and E. A. Petter , Consensus paper: Decoding the contributions of the cerebellum as a time machine. from neurons to clinical applications, Cerebellum 18 (2019) 266–286. Crossref, Medline, Web of ScienceGoogle Scholar
    • 44. D. Caligiore, M. A. Arbib, R. C. Miall and G. Baldassarre , The super-learning hypothesis: Integrating learning processes across cortex, cerebellum and basal ganglia, Neurosci. Biobehav. Rev. 100 (2019) 19–34. Crossref, Medline, Web of ScienceGoogle Scholar
    • 45. N. R. Luque, J. A. Garrido, R. R. Carrillo, S. Tolu and E. Ros , Adaptive cerebellar spiking model embedded in the control loop: Context switching and robustness against noise, Int. J. Neural Syst. 21 (2011) 385–401. Link, Web of ScienceGoogle Scholar
    • 46. N. R. Luque, J. A. Garrido, R. R. Carrillo, O. J. Coenen and E. Ros , Cerebellarlike corrective model inference engine for manipulation tasks, IEEE Trans. Syst. Man, Cybern. B, Cybern. 41 (2011) 1299–1312. Crossref, Medline, Web of ScienceGoogle Scholar
    • 47. R. E. Kettner, S. Mahamud, H.-C. Leung, N. Sitkoff, J. C. Houk, B. W. Peterson and A. G. Barto , Prediction of complex two-dimensional trajectories by a cerebellar model of smooth pursuit eye movement, J. Neurophysiol. 77 (1997) 2115–2130. Crossref, Medline, Web of ScienceGoogle Scholar
    • 48. J. L. Raymond and S. G. Lisberger , Neural learning rules for the vestibulo-ocular reflex, J. Neurosci. 18 (1998) 9112–9129. Crossref, Medline, Web of ScienceGoogle Scholar
    • 49. J. Spoelstra, N. Schweighofer and M. A. Arbib , Cerebellar learning of accurate predictive control for fast-reaching movements, Biol. Cybern. 82 (2000) 321–333. Crossref, Medline, Web of ScienceGoogle Scholar
    • 50. E. Ros, R. Carrillo, E. M. Ortigosa, B. Barbour and R. Agís , Event-Driven Simulation Scheme for Spiking Neural Networks Using Lookup Tables to Characterize Neuronal Dynamics, Neural Comput. 18 (2006) 2959–2993. Crossref, Medline, Web of ScienceGoogle Scholar
    • 51. R. R. Carrillo, E. Ros, C. Boucheny and O. J.-M. Coenen , A real-time spiking cerebellum model for learning robot control, Biosystems 94 (2008) 18–27. Crossref, Medline, Web of ScienceGoogle Scholar
    • 52. S. J. Kiebel, J. Daunizeau and K. J. Friston , A Hierarchy of Time-Scales and the Brain, PLoS Comput. Biol. 4 (2008) e1000209. Crossref, Medline, Web of ScienceGoogle Scholar
    • 53. M. D. Humphries, R. D. Stewart and K. N. Gurney , A physiologically plausible model of action selection and oscillatory activity in the basal ganglia, J. Neurosci. 26 (2006) 12921–12942. Crossref, Medline, Web of ScienceGoogle Scholar
    • 54. P. Dayan and L. F. Abbott , Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (The MIT Press, Cambridge, MA, 2001). Google Scholar
    • 55. N. Schweighofer, K. Doya, H. Fukai, J. V. Chiron, T. Furukawa and M. Kawato , Chaos may enhance information transmission in the inferior olive, Proc. Natl. Acad. Sci. 101 (2004) 4655–4660. Crossref, Medline, Web of ScienceGoogle Scholar
    • 56. G. A. Jacobson, K. Diba, A. Yaron-Jakoubovitch, Y. Oz, C. Koch, I. Segev and Y. Yarom , Subthreshold voltage noise of rat neocortical pyramidal neurones, J. Physiol. 564 (2005) 145–160. Crossref, Medline, Web of ScienceGoogle Scholar
    • 57. M. Ito and M. Kano , Long-lasting depression of parallel fiber-Purkinje cell transmission induced by conjunctive stimulation of parallel fibers and climbing fibers in the cerebellar cortex, Neurosci. Lett. 33 (1982) 253–258. Crossref, Medline, Web of ScienceGoogle Scholar
    • 58. M. Ito , Cerebellar long-term depression: Characterization, signal transduction, and functional roles, Physiol. Rev. 81 (2001) 1143–1195. Crossref, Medline, Web of ScienceGoogle Scholar
    • 59. E. D’Angelo, L. Mapelli, C. Casellato, J. A. Garrido, N. Luque, J. Monaco, F. Prestori, A. Pedrocchi and E. Ros , Distributed circuit plasticity: New clues for the cerebellar mechanisms of learning, Cerebellum 15 (2016) 139–151. Crossref, Medline, Web of ScienceGoogle Scholar
    • 60. V. Lev-Ram, S. B. Mehta, D. Kleinfeld and R. Y. Tsien , Reversing cerebellar long-term depression, Proc. Natl. Acad. Sci. 100 (2003) 15989–15993. Crossref, Medline, Web of ScienceGoogle Scholar
    • 61. J. A. Garrido, N. R. Luque, E. D’Angelo and E. Ros , Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: A closed-loop robotic simulation, Front. Neural Circuits 7 (2013) 159. Crossref, Medline, Web of ScienceGoogle Scholar
    • 62. A. Antonietti, C. Casellato, J. A. Garrido, N. R. Luque, F. Naveros, E. Ros, E. DAngelo and A. Pedrocchi , Spiking neural network with distributed plasticity reproduces cerebellar learning in eye blink conditioning paradigms, IEEE Trans. Biomed. Eng. 63 (2016) 210–219. Crossref, Medline, Web of ScienceGoogle Scholar
    • 63. P. J. Sjöström, G. G. Turrigiano and S. B. Nelson , Rate, timing, and cooperativity jointly determine cortical synaptic plasticity, Neuron 32 (2001) 1149–1164. Crossref, Medline, Web of ScienceGoogle Scholar
    • 64. T. Nevian and B. Sakmann , Spine Ca2+ signaling in spike-timing-dependent plasticity, J. Neurosci. 26 (2006) 11001–11013. Crossref, Medline, Web of ScienceGoogle Scholar
    • 65. T. Ishikawa, S. Tomatsu, Y. Tsunoda, J. Lee, D. S. Hoffman and S. Kakei , Releasing dentate nucleus cells from Purkinje cell inhibition generates output from the cerebrocerebellum, PloS One 9(10) (2014) e108774. Crossref, Medline, Web of ScienceGoogle Scholar
    • 66. K. Y. Tseng and P. O’Donnell , Dopamine-Glutamate interactions controlling prefrontal cortical pyramidal cell excitability involve multiple signaling mechanisms, J. Neurosci. 24 (2004) 5131–5139. Crossref, Medline, Web of ScienceGoogle Scholar
    • 67. S. N. Chettih, S. D. McDougle, L. I. Ruffolo and J. F. Medina , Adaptive timing of motor output in the mouse: The role of movement oscillations in eyelid conditioning, Front. Integr. Neurosci. 5 (2011) 72. Crossref, MedlineGoogle Scholar
    • 68. S. A. Heiney, M. P. Wohl, S. N. Chettih, L. I. Ruffolo and J. F. Medina , Cerebellar-dependent expression of motor learning during eyeblink conditioning in head-fixed mice, J. Neurosci. 34 (2014) 14845–14853. Crossref, Medline, Web of ScienceGoogle Scholar
    • 69. M. J. Dizon and K. Khodakhah , The role of interneurons in shaping purkinje cell responses in the cerebellar cortex, J. Neurosci. 31 (2011) 10463–10473. Crossref, Medline, Web of ScienceGoogle Scholar
    • 70. T. D. Rogers, P. E. Dickson, E. McKimm, D. H. Heck, D. Goldowitz, C. D. Blaha and G. Mittleman , Reorganization of circuits underlying cerebellar modulation of prefrontal cortical dopamine in mouse models of autism spectrum disorder, Cerebellum 12 (2013) 547–556. Crossref, Medline, Web of ScienceGoogle Scholar
    • 71. R. S. Snider, A. Maiti and S. R. Snider , Cerebellar pathways to ventral midbrain and nigra, Exper. Neurol. 53 (1976) 714–728. Crossref, Medline, Web of ScienceGoogle Scholar
    • 72. T. D. Rogers, P. E. Dickson, D. H. Heck, D. Goldowitz, G. Mittleman and C. D. Blaha , Connecting the dots of the cerebro-cerebellar role in cognitive function: Neuronal pathways for cerebellar modulation of dopamine release in the prefrontal cortex, Synapse 65 (2011) 1204–1212. Crossref, Medline, Web of ScienceGoogle Scholar
    • 73. M. R. Bennett , Monoaminergic synapses and schizophrenia: 45 Years of neuroleptics, J. Psychopharmacol. 12 (1998) 289–304. Crossref, Medline, Web of ScienceGoogle Scholar
    • 74. M. Ernst, A. J. Zametkin, J. A. Matochik, D. Pascualvaca and R. M. Cohen , Low medial prefrontal dopaminergic activity in autistic children [4], Lancet 350 (1997) 638. Crossref, Medline, Web of ScienceGoogle Scholar
    • 75. S. Aalto, A. Brück, M. Laine, K. Någren and J. O. Rinne , Frontal and temporal dopamine release during working memory and attention tasks in healthy humans: A positron emission tomography study using the high-affinity dopamine D2 receptor ligand [11C]FLB 457, J. Neurosci. 25 (2005) 2471–2477. Crossref, Medline, Web of ScienceGoogle Scholar
    • 76. N. J. Gamo, M. Wang and A. F. Arnsten , Methylphenidate and atomoxetine enhance prefrontal function through α2-adrenergic and dopamine D1 receptors, J. Am. Acad. Child Adol. Psychiatry 49 (2010) 1011–1023. Crossref, Medline, Web of ScienceGoogle Scholar
    • 77. D. S. Woodruff-Pak, R. G. Finkbiner and D. K. Sasse , Eyeblink conditioning discriminates Alzheimer’s patients from non-demented aged, Neuroreport 1 (1990) 45–48. Crossref, MedlineGoogle Scholar
    • 78. C. Weiss and J. F. Disterhoft , The impact of hippocampal lesions on trace-eyeblink conditioning and forebrain-cerebellar interactions, Behav. Neurosci. 129 (2015) 512–522. Crossref, Medline, Web of ScienceGoogle Scholar
    • 79. L. L. Sears, P. R. Finn and J. E. Steinmetz , Abnormal classical eye-blink conditioning in autism, J. Autism Develop. Disord. 24 (1994) 737–751. Crossref, Medline, Web of ScienceGoogle Scholar
    • 80. J. P. Welsh and J. T. Oristaglio , Autism and classical eyeblink conditioning: Performance changes of the conditioned response related to autism spectrum disorder diagnosis, Front. Psychiatry 7 (2016) 137. Crossref, Medline, Web of ScienceGoogle Scholar
    • 81. J. Oristaglio, S. Hyman West, M. Ghaffari, M. S. Lech, B. R. Verma, J. A. Harvey, J. P. Welsh and R. P. Malone , Children with autism spectrum disorders show abnormal conditioned response timing on delay, but not trace, eyeblink conditioning, Neuroscience 248 (2013) 708–718. Crossref, Medline, Web of ScienceGoogle Scholar
    • 82. D. R. Hampson and G. J. Blatt , Autism spectrum disorders and neuropathology of the cerebellum, Front. Neurosci. 9 (2015) 1–16. Crossref, Medline, Web of ScienceGoogle Scholar
    • 83. T. Spisák, V. Román, E. Papp, R. Kedves, K. Sághy, C. K. Csölle, A. Varga, D. Gajári, G. Nyitrai, Z. Spisák, Z. T. Kincses, G. Lévay, B. Lendvai and A. Czurkó , Purkinje cell number-correlated cerebrocerebellar circuit anomaly in the valproate model of autism, Sci. Rep. 9 (2019) 9225. Crossref, Medline, Web of ScienceGoogle Scholar
    • 84. N. R. Luque, J. A. Garrido, R. R. Carrillo, E. D’Angelo and E. Ros , Fast convergence of learning requires plasticity between inferior olive and deep cerebellar nuclei in a manipulation task: A closed-loop robotic simulation, Front. Comput. Neurosci. 8 (2014) 1–16. Crossref, Medline, Web of ScienceGoogle Scholar
    • 85. C. Casellato, A. Antonietti, J. A. Garrido, R. R. Carrillo, N. R. Luque, E. Ros, A. Pedrocchi and E. D’Angelo , Adaptive Robotic Control Driven by a Versatile Spiking Cerebellar Network, PLoS One 9 (2014) e112265. Crossref, Medline, Web of ScienceGoogle Scholar
    • 86. C. Casellato, A. Antonietti, J. A. Garrido, G. Ferrigno, E. D’Angelo and A. Pedrocchi , Distributed cerebellar plasticity implements generalized multiple-scale memory components in real-robot sensorimotor tasks, Front. Comput. Neurosci. 9 (2015) 1–24. Crossref, Medline, Web of ScienceGoogle Scholar
    • 87. A. C. Bostan and P. L. Strick , The basal ganglia and the cerebellum: Nodes in an integrated network, Nat. Rev. Neurosci. 19 (2018) 338–350. Crossref, Medline, Web of ScienceGoogle Scholar
    • 88. S. Ghosh-Dastidar and H. Adeli , A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection, Neural Netw. 22 (2009) 1419–1431. Crossref, Medline, Web of ScienceGoogle Scholar
    • 89. S. Ghosh-Dastidar and H. Adeli , Improved spiking neural networks for EEG classification and epilepsy and seizure detection, Integr. Comput.-Aided Eng. 14 (2007) 187–212. Crossref, Web of ScienceGoogle Scholar
    • 90. X. Zhang, G. Foderaro, C. Henriquez and S. Ferrari , A scalable weight-free learning algorithm for regulatory control of cell activity in spiking neuronal networks, Int. J. Neural Syst. 28 (2018) 1750015. LinkGoogle Scholar
    • 91. M. Bernert and B. Yvert , An attention-based spiking neural network for unsupervised spike-sorting, Int. J. Neural Syst. 29 (2019) 1–19. Link, Web of ScienceGoogle Scholar
    • 92. F. Galán-Prado, A. Morán, J. Font, M. Roca and J. L. Rosselló , Compact hardware synthesis of stochastic spiking neural networks, Int. J. Neural Syst. 29 (2019) 1950004. Link, Web of ScienceGoogle Scholar
    • 93. G. Antunes, S. F. Faria da Silva and F. M. Simoes de Souza , Mirror neurons modeled through spike-timing-dependent plasticity are affected by channelopathies associated with autism spectrum disorder, Int. J. Neural Syst. 28 (2018) 1750058. Link, Web of ScienceGoogle Scholar
    • 94. T. Wu, F.-D. Bîlbîe, A. Păun, L. Pan and F. Neri , Simplified and Yet Turing Universal Spiking Neural P Systems with Communication on Request, Int. J. Neural Syst. 28 (2018) 1850013. Link, Web of ScienceGoogle Scholar
    • 95. Y. Todo, Z. Tang, H. Todo, J. Ji and K. Yamashita , Neurons with multiplicative interactions of nonlinear synapses, Int. J. Neural Syst. 29 (2019) 1–18. Link, Web of ScienceGoogle Scholar
    • 96. R. Hu, Q. Huang, H. Wang, J. He and S. Chang , Monitor-based spiking recurrent network for the representation of complex dynamic patterns, Int. J. Neural Syst. 29 (2019) 1950006. Link, Web of ScienceGoogle Scholar
    • 97. J. R. Moyer, R. A. Deyo and J. F. Disterhoft , Hippocampectomy disrupts trace eye-blink conditioning in rabbits, Behav. Neurosci. 104 (1990) 243–52. Crossref, Medline, Web of ScienceGoogle Scholar
    • 98. H.-J. Boele, S. K. E. Koekkoek and C. I. De Zeeuw , Cerebellar and extracerebellar involvement in mouse eyeblink conditioning: The ACDC model, Front. Cellular Neurosci. 3 (2010) 19. Crossref, Medline, Web of ScienceGoogle Scholar
    • 99. T. Lee and J. J. Kim , Differential effects of cerebellar, amygdalar, and hippocampal lesions on classical eyeblink conditioning in rats, J. Neurosci. 24 (2004) 3242–3250. Crossref, Medline, Web of ScienceGoogle Scholar
    • 100. T. Sakamoto and S. Endo , Amygdala, deep cerebellar nuclei and red nucleus contribute to delay eyeblink conditioning in C57BL/6 mice, Eur. J. Neurosci. 32 (2010) 1537–1551. Crossref, Medline, Web of ScienceGoogle Scholar
    • 101. A. H. Taub and M. Mintz , Amygdala conditioning modulates sensory input to the cerebellum, Neurobiol. Learn. Memory 94 (2010) 521–529. Crossref, Medline, Web of ScienceGoogle Scholar
    • 102. B. Oswald, B. Knuckley, K. Mahan, C. Sanders and D. A. Powell , Prefrontal control of trace versus delay eyeblink conditioning: Role of the unconditioned stimulus in rabbits (Oryctolagus cuniculus), Behav. Neurosci. 120 (2006) 1033–1042. Crossref, Medline, Web of ScienceGoogle Scholar
    • 103. W. Tseng, R. Guan, J. Disterhoft and C. Weiss , Trace eyeblink conditioning is hippocampally dependent in mice, Hippocampus 14 (2004) 58–65. Crossref, Medline, Web of ScienceGoogle Scholar
    • 104. M. W. Jones and M. A. Wilson , Theta rhythms coordinate hippocampal-prefrontal interactions in a spatial memory task, PLoS Biol. 3 (2005) e402. Crossref, Medline, Web of ScienceGoogle Scholar
    • 105. K. Benchenane, A. Peyrache, M. Khamassi, P. L. Tierney, Y. Gioanni, F. P. Battaglia and S. I. Wiener , Coherent theta oscillations and reorganization of spike timing in the hippocampal- prefrontal network upon learning, Neuron 66 (2010) 921–936. Crossref, Medline, Web of ScienceGoogle Scholar
    • 106. R. P. Dum and P. L. Strick , Transneuronal tracing with neurotropic viruses reveals network macroarchitecture, Curr. Opin. Neurobiol. 23 (2013) 245–249. Crossref, Medline, Web of ScienceGoogle Scholar
    • 107. M. M. Ten Brinke, S. A. Heiney, X. Wang, M. Proietti-Onori, H.-J. Boele, J. Bakermans, J. F. Medina, Z. Gao and C. I. De Zeeuw , Dynamic modulation of activity in cerebellar nuclei neurons during pavlovian eyeblink conditioning in mice, eLife 6 (2017) 1–27. Web of ScienceGoogle Scholar
    • 108. J. Zhang, K.-Y. Zhang, L.-B. Zhang, W.-W. Zhang, H. Feng, Z.-X. Yao, B. Hu and H. Chen , A method for combining multiple-units readout of optogenetic control with natural stimulation-evoked eyeblink conditioning in freely-moving mice, Sci. Rep. 9 (2019) 1857. Crossref, Medline, Web of ScienceGoogle Scholar
    Remember to check out the Most Cited Articles!

    Check out our titles in neural networks today!