Using Linear Systems Theory to Study Nonlinear Dynamics of Relay Cells
Rahul Agarwal, Sridevi V. Sarma
2012
Abstract
Relay cells are prevalent throughout sensory systems and receive two types of inputs: driving and modulating. The driving input contains receptive field properties that must be relayed while the modulating input alters the reliability of this relay. In this paper, we analyze a biophysical based nonlinear model of a relay cell and use systems theoretic tools to construct analytic bounds on how well the cell transmits a driving input as a function of the neuron’s electrophysiological properties, the modulating input, and the driving signal parameters. Our analysis applies to both 2nd & 3rd order model as long as the neuron does not spike without a driving input pulse and exhibits a refractory period. Our bounds suggest, for instance, that if the frequency of the modulating input increases and the DC offset decreases, then reliability increases. Our analysis also shows how the biophysical properties of the neuron (e.g. ion channel dynamics) define the oscillatory patterns needed in the modulating input for appropriately timed relay of sensory information.
References
- Agarwal R. and Sarma S. V. (2011). The effects of dbs patterns on basal ganglia activity and thalamic relay. J Comput Neurosci, (accepted for publication).
- Bekisz M. and Wrobel A. (1999). Coupling of beta and gamma activity in corticothalamic system of cats attending to visual stimuli. NeuroReport, 10:3589-94.
- Erwin E., Baker F. H., Busen W. F., and Malpeli J. G. (1999). Relationship between laminar topology and retinotopy in the rhesus lateral geniculate nucleus: results from a functional atlas. J.Comp neurol, 407:92- 102.
- FitzHugh R. (1955). Mathematical models of threshold phenomena in the nerve membrane. Bulletin of Mathematical Biology, 17(4):257-278.
- Greenstied Charles M. and Snell J. Laurie (2003). Introduction to Probability. American Mathematical Society.
- Guillery R. W. and Sherman S. M. (2002). Thalamic relay functions and their role in corticocortical communication: Generalizations from the visual system. Neuron, 33:163-176.
- Guo Y., Rubin J. E., McIntyre C. C., Vitek J. L., and Terman D. (2008). Thalamocortical relay fidelity varies in deep brain stmulation protocols in data-driven computational models. J. Neurophysiol., 99:1477-1492.
- Hirsch J. C., Fourment A., and Marc M. E. (1983). Sleeprelated variations of membrane potential in the lateral geniculate body relay neurons of the cat. Brain Research, 259(2):308-312.
- Hughes S. W., Lorincz M., Cope D. W., Blethyn K. L., Kekesi K. A., Parri H. R., Juhasz G., and Crunelli V. (2004). Synchronized oscillations at a and q frequencies in the lateral geniculate nucleus. Neuron, Vol. 42, 253268, April 22, 2004,.
- Kastner S., Schneider K. A., and Wunderlich K. (2006). Chapter 8 beyond a relay nucleus: Neuroimaging views on the human lgn. Progress in Brain Research, 155 Part B:125-143.
- Lagier Samuel , Carleton Alan, and Lledo Pierre-Marie (2004). Interplay between local gabaergic interneurons and relay neurons generates g oscillations in the rat olfactory bulb. The Journal of Neuroscience, 24(18):4382-4392.
- Lodish H., Berk A., and Zipursky S. L. (2000). Molecular Cell Biology. 4th edition. W. H. Freeman.
- Logothetis N. K. (2002). The neural basis of blood oxygen level dependent functional magnetic resonance imaging signal. Phil. Trans R. Soc. Lond B, 357(1003-37).
- Lorincz M. L., Kekesi K. A., Juhasz G., Crunelli V., and Hughes S. W. (2009). Temporal framing of thalamic relay-mode firing by phasic inhibition during the alpha rhythm. Neuron, 63:683-96.
- Manor Y., Rinzel J., Segav I., and Yarom Y. (1997). Low amplitude oscillations in inferior olive: A model based on electrical coupling of neurons with heterogeneous channel densities. J. Neurophysiol.
- Masson G. L., Masson S. R. L., D., and Bal T. (2002). Feedback inhibition controls spike transfer in hybrid thalamic circuits. Nature, 417:854-858.
- O'Connor D. H., Fukui M. M., Pinsk M. A., and Kastner S. (2002). Attention modulates responses in the human lateral geniculate nucleus. nature neuroscience, 5(11):1203-1209.
- Platkiewicz J. and Brette R. (2010). A threshold equation for action potential initiation. PloS Comput Biol, 6(7):1000850.
- Reinagel P., Godwin D., Sherman M., and Koch C. (1999). Encoding of visual information by lgn bursts. Journal of Neurophysiology, 81:2558-69.
- Rubin J. and Josic K. (2007). The firing of an excitable neuron in the presence of stochastic trains of strong synaptic inputs. Neural Computation, 19:1251-1294.
- Rubin J. E. and Terman D. (2004). High frequency stimulation of the subthalamic nucleus eliminates pathological thalamic rhythmicity in a computational model. J. Comput. Neurosci., 16(3):211-35.
- Seki Kazuhiko, Perlmutter Steve I., and Fetz Eberhard E. (2003). Sensory input to primate spinal cord is presynaptically inhibited during voluntary movement. Nature Neuroscience, 6:1309-1316.
- Sherman S. M. (2007). The thalamus is more than just a relay. Current Opinion in Neurobiology, 17(4):417- 422.
- Sherman S. M. and Guillery R. W. (1998). On the actions that one nerve can have on another: Distinguishing ”drivers” from ”modulators”. PNAS, 95(12):7121-26.
- Sherman S. M. and Guillery R. W. (2002). The role of the thalamus in the flow of information to the cortex. Phil. Trans. R. Soc. Lond. B., 357(1428):1695-1708.
- Sohal V. and Huguenard J. (2002). Reciprocal inhibition controls the oscillatory state in thalamic networks. Neurocomp, 44:653-659.
- Sohal V., Huntsman M., and Huguenard J. (2000). Reciprocal inhibitory connections regulate the spatiotemporal properties of intrathalamic oscillations. J Neurosci, 20:1735-1745.
- Wolfart J., Debay D., Masson G. L., Destexhe A., and Bal T. (2005). Synaptic background activity controls spike transfer from thalamus to cortex. Nature Neuroscience, 8(12):1760-1767.
- Zhan X., Cox C., Rinzel J., and Sherman S. (1999). Current clamp and modeling studies of lowthreshold calcium spikes in cells of the cats lateral geniculate nucleus. J. Neurophysiol, 81:2360-2373.
- Table 1: Parameters and functions for (1).
Paper Citation
in Harvard Style
Agarwal R. and V. Sarma S. (2012). Using Linear Systems Theory to Study Nonlinear Dynamics of Relay Cells . In Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8565-21-1, pages 433-438. DOI: 10.5220/0003987504330438
in Bibtex Style
@conference{icinco12,
author={Rahul Agarwal and Sridevi V. Sarma},
title={Using Linear Systems Theory to Study Nonlinear Dynamics of Relay Cells},
booktitle={Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2012},
pages={433-438},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003987504330438},
isbn={978-989-8565-21-1},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Using Linear Systems Theory to Study Nonlinear Dynamics of Relay Cells
SN - 978-989-8565-21-1
AU - Agarwal R.
AU - V. Sarma S.
PY - 2012
SP - 433
EP - 438
DO - 10.5220/0003987504330438