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Methodological Issues in Modelling at Multiple Levels of Description

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Computational Systems Neurobiology

Abstract

Computational neuroscience and Systems Biology are comparatively young, interdisciplinary areas in the life sciences, dealing with, arguably, the most complex systems we know of. All these factors conspire to make the status, and process, of building models in these areas problematic. Oftentimes modellers make tacit assumptions about their general approach, but we would argue that such assumptions should be explicit, and that establishing sound methodological principles is an important foundation stone for making progress.

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Notes

  1. 1.

    In Marr’s original formulation of the computational framework, which appeared in an MIT technical report (Marr and Poggio 1976), a fourth level was described. However, this was dropped in the more popular account in Marr (1982). Independently, Gurney proposed a four level account in Gurney (1997) which was subsequently developed in Gurney et al. (2004b).

  2. 2.

    It is often argued that a ‘divine gift’ of a complete model of the brain would be useless. In the light of the above discussion, however, it would appear this is not true. It may be arduous to unravel the function of all aspects of the model/brain, but this task would certainly be easier than using biological experiments alone.

  3. 3.

    We use the term ‘system level’ to denote a large scale (‘low magnification’) view of the brain, that incorporates at least one anatomically defined, functional set of nuclei. This is in contrast with the use of the term in ‘systems biology’ where it usually denotes the cellular level.

References

  • Akkal D, Burbaud P, Audin J, Bioulac B (1996) Responses of substantia nigra pars reticulata neurons to intrastriatal d1 and d2 dopaminergic agonist injections in the rat. Neurosci Lett 213(1):66–70

    Article  PubMed  CAS  Google Scholar 

  • Bar-Gad I, Morris G, Bergman H (2003) Information processing, dimensionality reduction and reinforcement learning in the basal ganglia. Prog Neurobiol 71(6):439–473

    Article  PubMed  Google Scholar 

  • Berke JD, Hyman SE (2000) Addiction, dopamine, and the molecular mechanisms of memory. Neuron 25(3):515–532. doi:10.1016/S0896-6273(00)81056-9

    Article  PubMed  CAS  Google Scholar 

  • Bogacz R, Gurney K (2007) The basal ganglia and cortex implement optimal decision making between alternative actions. Neural Comput 19(2):442–477. doi:10.1162/neco. 2007.19.2.442

    Article  PubMed  Google Scholar 

  • Brown P, Kupsch A, Magill PJ, Sharott A, Harnack D, Meissner W (2002) Oscillatory local field potentials recorded from the subthalamic nucleus of the alert rat. Exp Neurol 177(2):581–585

    Article  PubMed  Google Scholar 

  • Churchland PS, Sejnowski TJ (1992) The computational brain. Computational neuroscience. The MIT Press, Cambridge

    Google Scholar 

  • Connor CE, Egeth HE, Yantis S (2004) Visual attention: bottom-up versus top-down. Curr Biol 14(19):R850–R852. doi:10.1016/j.cub.2004.09.041

    Article  PubMed  CAS  Google Scholar 

  • Doya K (1999) What are the computations of the cerebellum, the basal ganglia and the cerebral cortex? Neural Netw 12(7–8):961–974

    Article  PubMed  Google Scholar 

  • Dragalin V, Tartakovsky A, Veeravalli V (1999) Multihypothesis sequential probability ratio tests – part I: asymptotic optimality. IEEE Trans Inf Theory 45(7):2448–2461

    Article  Google Scholar 

  • Engel AK, Singer W (2001) Temporal binding and the neural correlates of sensory awareness. Trends Cogn Sci 5(1):16–25

    Article  PubMed  Google Scholar 

  • Fernandez r, Schiappa R, Girault J, Novre NL (2006) DARPP-32 is a robust integrator of dopamine and glutamate signals. PLoS Comput Biol 2(12):e176. doi:10.1371/journal. pcbi.0020176

    Google Scholar 

  • Fries P (2009) Neuronal gamma-band synchronization as a fundamental process in cortical computation. Annu Rev Neurosci 32(1):209–224. doi:10.1146/annurev.neuro.051508. 135603

    Article  PubMed  CAS  Google Scholar 

  • Gerfen C, Engber T, Mahan L, Susel Z, Chase T, Monsma F, Sibley D (1990) D1 and d2 dopamine receptor-regulated gene expression of striatonigral and striatopallidal neurons. Science 250:1429–1432

    Article  PubMed  CAS  Google Scholar 

  • Girard B, Berthoz A (2005) From brainstem to cortex: computational models of saccade generation circuitry. Prog Neurobiol 77(4):215–251. doi:10.1016/j.pneurobio.2005.11. 001

    Article  PubMed  CAS  Google Scholar 

  • Gruber A, Solla S, Surmeier D, Houk J (2003) Modulation of striatal single units by expected reward: a spiny neuron model displaying dopamine-induced bistability. J Neurophysiol 90(2):1095–1114

    Article  PubMed  Google Scholar 

  • Gurney KN (1997) An introduction to neural networks. UCL Press (Taylor and Francis group), London

    Google Scholar 

  • Gurney K (2009a) Computational models in neuroscience: from membranes to robots. In: Computational modelling in behavioural neuroscience: closing the gap between neurophysiology and behaviou, Advances in behavioural brain science. Psychology Press, East Sussex, p 107

    Google Scholar 

  • Gurney KN (2009b) Reverse engineering the vertebrate brain: Methodological principles for a biologically grounded programme of cognitive modelling. Cogn Comput 1(1):29–41. doi:10.1007/s12559-009-9010-2

    Article  Google Scholar 

  • Gurney KN, Prescott TJ, Redgrave P (2001a) A computational model of action selection in the basal ganglia I: a new functional anatomy. Biol Cybern 84:401–410

    Article  PubMed  CAS  Google Scholar 

  • Gurney KN, Prescott TJ, Redgrave P (2001b) A computational model of action selection in the basal ganglia II: analysis and simulation of behaviour. Biol Cybern 84:411–423

    Article  PubMed  CAS  Google Scholar 

  • Gurney KN, Humphries M, Wood R, Prescott TJ, Redgrave P (2004a) Testing computational hypotheses of brain systems function: a case study with the basal ganglia. Network 15(4):263–290

    Article  PubMed  CAS  Google Scholar 

  • Gurney KN, Prescott TJ, Wickens JR, Redgrave P (2004b) Computational models of the basal ganglia: from robots to membranes. Trends Neurosci 27(8):453–459

    Article  PubMed  CAS  Google Scholar 

  • Harsing JLG, Zigmond MJ (1997) Influence of dopamine on gaba release in striatum: evidence for d1-d2 interactions and non-synaptic influences. Neuroscience 77(2):419–29

    Article  PubMed  CAS  Google Scholar 

  • Hikosaka O, Nakamura K, Nakahara H (2006) Basal ganglia orient eyes to reward. J Neurophysiol 95(2):567–584. doi:10.1152/jn.00458.2005

    Article  PubMed  Google Scholar 

  • Humphries MD (2007) High level modeling of tonic dopamine mechanisms in striatal neurons. arXivorg: q-bio/0701022, http://arxiv.org/abs/q-bio/0701022

  • Humphries MD, Gurney KN (2007) Deep brain stimulation of the subthalamic nucleus causes paradoxical inhibition of output in a computational model of the “parkinsonian” basal ganglia. In: Society for neuroscience annulal meeting session 622.9

    Google Scholar 

  • Humphries M, Prescott T, Gurney K, Kaynak O, Alpaydin E, Oja E, Xu L (2003) The interaction of recurrent axon collateral networks in the basal ganglia. In: Joint international conference ICANN/ICONIP. Lecture notes in computer science, Springer, Istanbul, pp 797–804

    Google Scholar 

  • Humphries MD, Stewart RD, Gurney KN (2006) A physiologically plausible model of action selection and oscillatory activity in the basal ganglia. J Neurosci 26(50):12921–12942. doi:10.1523/JNEUROSCI.3486-06.2006

    Article  PubMed  CAS  Google Scholar 

  • Humphries M, Lepora N, Wood R, Gurney K (2009a) Capturing dopaminergic modulation and bimodal membrane behaviour of striatal medium spiny neurons in accurate, reduced models. Front Comput Neurosci 3. doi:10.3389/neuro.10.026.2009

    Google Scholar 

  • Humphries MD, Wood R, Gurney K (2009b) Dopamine-modulated dynamic cell assemblies generated by the GABAergic striatal microcircuit. Neural Netw 22(8):1174–1188. doi:10.1016/j.neunet.2009.07.018, PMID: 19646846

    Google Scholar 

  • Izhikevich EM (2007) Dynamical systems in neuroscience: the geometry of excitability. MIT Press, Cambridge

    Google Scholar 

  • Jahr CE, Stevens CF (1990) Voltage dependence of NMDA-activated macroscopic conductances predicted by single-channel kinetics. J Neurosci 10(9):3178

    PubMed  CAS  Google Scholar 

  • Kitano H (2002) Computational systems biology. Nature 420(6912):206–210. doi:10.1038/ nature01254

    Article  PubMed  CAS  Google Scholar 

  • Koch C (1999) The biophysics of computation: information processing in single neurons. Oxford University Press, New York

    Google Scholar 

  • Lindskog M, Kim M, Wikstrm MA, Blackwell KT, Kotaleski JH (2006) Transient calcium and dopamine increase PKA activity and DARPP-32 phosphorylation. PLoS Computat Biol 2(9):e119. doi:10.1371/journal.pcbi.0020119, PMID: 16965177

    Article  Google Scholar 

  • Magill PJ, Bolam JP, Bevan MD (2001) Dopamine regulates the impact of the cerebral cortex on the subthalamic nucleus-globus pallidus network. Neuroscience 106:313–330

    Article  PubMed  CAS  Google Scholar 

  • Markram H (2006) The blue brain project. Nat Rev Neurosci 7(2):153–160. doi:10.1038/ nrn1848

    Article  PubMed  CAS  Google Scholar 

  • Marr D (1982) Vision: a computational investigation into human representation and processing of visual information. Freeeman, New York

    Google Scholar 

  • Marr D, Poggio T (1976) From understanding computation to understanding neural circuitry. Technical report AIM-357, MIT, Cambridge

    Google Scholar 

  • Mel BW, Ruderman DL, Archie KA (1998) Translation-invariant orientation tuning in visual “complex” cells could derive from intradendritic computations. J Neurosci 18(11):4325–4334

    PubMed  CAS  Google Scholar 

  • Mink JW, Thach WT (1993) Basal ganglia intrinsic circuits and their role in behavior. Curr Opin Neurobiol 3(6):950–957

    Article  PubMed  CAS  Google Scholar 

  • Moyer JT, Wolf JA, Finkel LH (2007) Effects of dopaminergic modulation on the integrative properties of the ventral striatal medium spiny neuron. J Neurophysiol 98(6):3731–3748. doi:10.1152/jn.00335.2007

    Article  PubMed  CAS  Google Scholar 

  • Nicola SM, Surmeier DJ, Malenka RC (2000) Dopaminergic modulation of neuronal excitability in the striatum and nucleus accumbens. Annu Rev Neurosci 23(1):185–215. doi:10.1146/annurev.neuro.23.1.185

    Article  PubMed  CAS  Google Scholar 

  • Oja E (1992) Principal components, minor components, and linear neural networks. Neural Netw 5:927–927

    Article  Google Scholar 

  • Prescott AJ, Gonzales FM, Gurney KN, Humphries M, Redgrave P (2006) A robot model of the basal ganglia: behavior and intrinsic processing. Neural Netw 19(1):31–61

    Article  PubMed  Google Scholar 

  • Qian A, Buller AL, Johnson JW (2005) NR2 subunit-dependence of NMDA receptor channel block by external mg2 + . J Physiol 562(2):319–331. doi:10.1113/jphysiol.2004.076737

    Article  PubMed  CAS  Google Scholar 

  • Redgrave P (2007) Basal ganglia. Scholarpedia 2(6):1825

    Google Scholar 

  • Redgrave P, Prescott TJ, Gurney KN (1999) The basal ganglia: a vertebrate solution to the selection problem? Neuroscience 89:1009–1023

    Article  PubMed  CAS  Google Scholar 

  • Reynolds JNJ, Wickens JR (2002) Dopamine-dependent plasticity of corticostriatal synapses. Neural Netw 15(4–6):507–521, PMID: 12371508

    Article  PubMed  Google Scholar 

  • Salamone J, Correa M, Farrar A, Nunes E, Pardo M (2009) Dopamine, behavioral economics, and effort. Front Behav Neurosci 3(13). doi:10.3389/neuro.08.013.2009

    Google Scholar 

  • Schall JD (2002) The neural selection and control of saccades by the frontal eye field. Philos Trans R Soc Lond B Biol Sci 357(1424):1073–1082. doi:10.1098/rstb.2002.1098

    Article  PubMed  Google Scholar 

  • Servan-Schreiber D, printz H, Cohen J (1990) A network model of catecholamine effects: gain, signal-to-noise ratio and behavior. Science 249:892–895

    Google Scholar 

  • Stafford T, Gurney KN (2007) Biologically constrained action selection improves cognitive control in a model of the stroop task. Philos Trans R Soc Lond B Biol Sci 362(1485):1671–1684. doi:10.1098/rstb.2007.2060

    Article  PubMed  Google Scholar 

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Acknowledgements

This work was supported by UK EPSRC grant EP/C516303/1, and French grants: Marie Curie BIND, and an ANR Chaire d’Excellence.

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Correspondence to Kevin Gurney .

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Gurney, K., Humphries, M. (2012). Methodological Issues in Modelling at Multiple Levels of Description. In: Le Novère, N. (eds) Computational Systems Neurobiology. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-3858-4_9

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