Abstract
The nervous system faces a most challenging task – to receive information from the outside world, process it, to change adaptively, and to generate an output – the appropriate behavior of the organism in a complex world. The research agenda of computational neuroscience (CN) is to use theoretical tools in order to understand how the different elements composing the nervous system: membrane ion channels, synapses, neurons, networks, and the systems they form, implement this demanding challenge successfully. CN deals with theoretical questions at both the cellular and subcellular levels, as well as at the networks, system, and behavioral levels. It focuses both on extracting basic biophysical principles (e.g., the rules governing the input-output relationship in single neurons) as well as on high-level rules governing the computational functions of a whole system, e.g., “How is a spot of light moving in the visual field encoded in the retina?” Or “how do networks of interconnected neurons represent and retain memories?” Ultimately, CN aims to understand, via mathematical theory, how do high-level phenomena such as cognition, emotions, creativity, and imagination, as well as brain disorders such as autism and schizophrenia, emerge from elementary brain-mechanisms. Here we highlight a few theoretical approaches used in CN and provide the respective fundamental insights that were gained. We start with biophysical models of single neurons and end with examples for models at the network level.
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References
Abbott LF (2008) Theoretical neuroscience rising. Neuron 60:489–495
Agmon-Snir H, Carr CE, Rinzel J (1998) The role of dendrites in auditory coincidence detection. Nature 393:268–272
Agmon-Snir H, Segev I (1993) Signal delay and input synchronization in passive dendritic structures. J Neurophysiol 70:2066–2085
Amit DJ (1992) Modeling brain function: the world of attractor neural networks. Cambridge University Press, Cambridge/New York
Amit DJ, Gutfreund H, Sompolinsky H (1985) Storing infinite numbers of patterns in a spin-glass model of neural networks. Phys Rev Lett 55:1530–1533
Archie KA, Mel BW (2000) A model for intradendritic computation of binocular disparity. Nat Neurosci 3:54–63
Ascoli GA, Alonso-Nanclares L, Anderson SA et al. (2008) Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex. Nat Rev Neurosci 9:557–568
Branco T, Hausser M (2011) Synaptic integration gradients in single cortical pyramidal cell dendrites. Neuron 69:885–892
Briggman KL, Helmstaedter M, Denk W (2011) Wiring specificity in the direction-selectivity circuit of the retina. Nature 471:183–188
Chen BL, Hall DH, Chklovskii DB (2006) Wiring optimization can relate neuronal structure and function. Proc Natl Acad Sci USA 103:4723–4728
Dayan P, Abbott LF (2001) Theoretical neuroscience. MIT Press, Cambridge
Dayan P, Daw ND (2008) Decision theory, reinforcement learning, and the brain. Cogn Affect Behav Neurosci 8:429–453
Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New York
Druckmann S, Banitt Y, Gidon A et al. (2007) A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data. Front Neurosci 1:7–18
Fusi S, Abbott L (2007) Limits on the memory storage capacity of bounded synapses. Nat Neurosci 10:485–493
Gabbiani F, Krapp HG, Koch C, Laurent G (2002) Multiplicative computation in a visual neuron sensitive to looming. Nature 420:320–324
Gidon A, Segev I (2012) Principles governing the operation of synaptic inhibition in dendrites. Neuron 75:330–341
Gutig R, Sompolinsky H (2006) The tempotron: a neuron that learns spike timing-based decisions. Nat Neurosci 9:420–428
Helmstaedter M, Sakmann B, Feldmeyer D (2009) L2/3 interneuron groups defined by multiparameter analysis of axonal projection, dendritic geometry, and electrical excitability. Cereb Cortex 19:951–962
Hertz J, Krogh A, Palmer RG (1991) Introduction to the theory of neural computation. Westview Press, Boulder
Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79:2554–2558
Jack JJB, Noble D, Tsien RW (1975) Electric current flow in excitable cells. Clarendon, Oxford
Klausberger T, Somogyi P (2008) Neuronal diversity and temporal dynamics: the unity of hippocampal circuit operations. Science 321:53–57
Koch C, Poggio T, Torre V (1983) Nonlinear interactions in a dendritic tree: localization, timing, and role in information processing. Proc Natl Acad Sci USA 80:2799–2802
Koch C, Rapp M, Segev I (1996) A brief history of time (constants). Cereb Cortex 6:93–101
Livet J, Weissman TA, Kang H et al (2007) Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system. Nature 450:56–62
Loewenstein Y, Sompolinsky H (2003) Temporal integration by calcium dynamics in a model neuron. Nat Neurosci 6:961–967
Losonczy A, Makara JK, Magee JC (2008) Compartmentalized dendritic plasticity and input feature storage in neurons. Nature 452:436–441
Marder E, Taylor AL (2011) Multiple models to capture the variability in biological neurons and networks. Nat Neurosci 14:133–138
Markram H, Toledo-Rodriguez M, Wang Y et al (2004) Interneurons of the neocortical inhibitory system. Nat Rev Neurosci 5:793–807
Meinertzhagen IA (2010) The organisation of invertebrate brains: cells, synapses and circuits. Acta Zoologica 91:64–71
Nelson SB, Hempel C, Sugino K (2006) Probing the transcriptome of neuronal cell types. Curr Opin Neurobiol 16:571–576
Nicolelis MAL (2001) Actions from thoughts. Nature 409:403–407
Prinz AA, Bucher D, Marder E (2004) Similar network activity from disparate circuit parameters. Nat Neurosci 7:1345–1352
Rall W (1959) Branching dendritic trees and motoneuron membrane resistivity. Exp Neurol 1:491–527
Rall W (1964) Theoretical significance of dendritic trees for neuronal input-output relations. In: Reiss RF (ed) Neural theory and modelling. Stanford University Press, Stanford, pp 73–97
Rall W (1967) Distinguishing theoretical synaptic potentials computed for different soma-dendritic distributions of synaptic input. J Neurophysiol 30:1138–1168
Rall W (1969) Time constants and electrotonic length of membrane cylinders and neurons. Biophys J 9:1483–1508
Rall W, Agmon-Snir H (1998) Cable theory for dendritic neurons. In: Koch C, Segev I (eds) Methods in neuronal modeling: from ions to networks. MIT Press, Cambridge, pp 27–92
Rall W, Rinzel J (1973) Branch input resistance and steady attenuation for input to one branch of a dendritic neuron model. Biophys J 13:648–687
Rieke F (1999) Spikes: exploring the neural code. MIT Press, Cambridge
Rosenblatt F (1958) The perceptron. Psychol Rev 65:386–408
Rucci M, Bullock D, Santini F (2007) Integrating robotics and neuroscience: brains for robots, bodies for brains. Adv Robot 21:1115–1129
Segev I (1995) Dendritic processing. In: Arbib MA (ed) The handbook of brain theory and neuronal networks. MIT Press, Cambridge
Single S, Borst A (1998) Dendritic integration and its role in computing image velocity. Science 281:1848–1850
Sporns O, Tononi G, Kötter R (2005) The human connectome: a structural description of the human brain. PLoS Comput Biol 1:e42
Sterling P (2002) Neuroscience: how neurons compute direction. Nature 420:375–376
Walmsley B, Graham B, Nicol MJ (1995) Serial E-M and simulation study of presynaptic inhibition along a group Ia collateral in the spinal cord. J Neurophysiol 74:616–623
Acknowledgments
This work was supported by a grant from the Blue Brain Project and by the Gatsby Charitable Foundations.
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Druckmann, S., Gidon, A., Segev, I. (2013). Computational Neuroscience: Capturing the Essence. In: Galizia, C., Lledo, PM. (eds) Neurosciences - From Molecule to Behavior: a university textbook. Springer Spektrum, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10769-6_30
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