Abstract
Many research groups have sought to measure phase response curves (PRCs) from real neurons. In contrast to the numerical calculations of the PRCs for the mathematical neuron models, electrophysiological experiments on real neurons face serious problems whereby PRCs have to be retrieved from noisy data. However, methods for estimating PRCs from noisy spike response data have yet to be established. In this chapter, we explain our Bayesian approach to estimating the PRCs and its application to physiological data. In the first half of this chapter, we describe a Bayesian algorithm for estimating PRCs from noisy spike response data. This algorithm is based on a likelihood function derived from a detailed model of the spike response in PRC measurements that is formulated as a Langevin phase equation. We construct a maximum a posteriori (MAP) estimation algorithm based on the analytically obtained likelihood function. This algorithm gives estimates of not only the PRC but also the Langevin force intensity. In the last half of this chapter, we apply the MAP estimation algorithm to physiological data measured from a hippocampal CA1 pyramidal neuron. We explain the protocol of the PRC measurement in a dynamic clamp, which maintains the baseline firing frequencies as close to a target value for as long as the perturbation experiment lasts. Finally, we verify the reliability of the estimated PRC by testing whether the Fokker–Planck equation based on the estimated PRC and Langevin force intensity captures the stochastic oscillatory behavior of the same neuron disturbed by periodic perturbations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Sensitivity is defined as the phase response normalized by the strength of the pulse perturbation.
- 2.
β defined in (8.30) (see Appendix 1) is directly measurable, because 1 ∕ β is the variance of the inter-spike interval. In the following numerical calculations, we determined β by calculating it from the sampling data.
- 3.
The equi-phase plane is defined as a set of initial points which converges to the same point on the limit cycle orbit after an infinite number of periods.
References
Berlin TH, Kac M (1952) The spherical model of a ferromagnet. Phys Rev 86:821.
Ermentrout GB, (1996) Type I membranes, phase resetting curves, and synchrony. Neural Comput, 8:979-1001.
Ermentrout GB, Saunders D (2006) Phase resetting and coupling of noisy neural oscillators. J Comput Neurosci 20:179-190.
Ermentrout GB, Galán RF, Urban NN (2007) Relating neural dynamics to neural coding. Phys Rev Lett 99:248103.
Galán RF, Ermentrout GB, Urban NN (2005) Efficient estimation of phase-resetting curves in real neurons and its significance for neural-network modeling. Phys Rev Lett 94:158101.
Galán RF, Ermentrout GB, Urban NN (2007) Stochastic dynamics of uncoupled neural oscillators: Fokker-Planck studies with the finite element method. Phys Rev E 76:056110.
Goldberg JA, Deister CA, Wilson CJ (2007) Response properties and synchronization of rhythmically firing dendritic neurons. J Neurophysiol 97:208-219.
Kuramoto Y (1984) Chemical oscillations, waves, and turbulence. Springer-Verlag.
Kuramoto Y (unpublished).
Lengyel M, Kwag J, Paulsen O, Dayan P (2005) Matching storage and recall: hippocampal spike timing-dependent plasticity and phase response curves. Nat Neurosci 8:1677-1683.
Mancilla JG, Lewis TJ, Pinto DJ, Rinzel J, Connors BW (2007) Synchronization of electrically coupled pairs of inhibitory interneurons in neocortex. J Neurosci 27:2058-2073.
Marella S, Ermentrout GB (2008) Class- II neurons display a higher degree of stochastic synchronization than class- II neurons. Phys Rev E 77:041918
Morris C, Lecar H (1981) Voltage oscillations in the barnacle giant muscle fiber. Biophys J, 35:193-213.
Nakao H, Arai K, Kawamura Y (2007) Noise-induced synchronization and clustering in ensembles of uncoupled limit-cycle oscillators. Phys Rev Lett 98:184101.
Neltner L, Hansel D, Mato G, Meunier C (2000) Synchrony in heterogeneous networks of spiking neurons. Neural Comput 12:1607-1641
Netoff TI, Acker CD, Bettencourt JC (2004) Beyond two-cell networks: experimental measurement of neuronal responses to multiple synaptic inputs. J Comput Neurosci 18:287-295
Netoff TI, Banks MI, Dorval AD, Acker CD, Haas JS, Kopell N, White JA (2005) Synchronization in hybrid neuronal networks of the hippocampal formation. J Neurophysiol 93:1197-1208.
Oprisan SA, Prinz AA, Canavier CC (2004) Phase resetting and phase locking in hybrid circuits of one model and one biological neuron. Biophysical J 87:2283-2298.
Ota K, Omori T, Aonishi T (2009) MAP estimation algorithm for phase response curves based on analysis of the observation process. J. Comput. Neurosci. 26:185-202.
Preyer AJ, Butera RJ (2005) Neuronal oscillators in aplysia californica that demonstrate weak coupling in vitro Phys Rev Lett 95:138103.
Reyes AD, Fez EE (1993) Two modes of interspike interval shortening by brief transient depolarizations in cat neocortical neurons. J Neurophysiol 69:1661-1672.
Risken H (1989) The fokker-planck equation. Methods of solution and applications. Springer, Berlin.
Robinson HP, Kawai N (1993) Injection of digitally synthesized synaptic conductance transients to measure the integrative properties of neurons. J Neurosci Methods 49:157-165.
Sharp AA, O’Neil MB, Abbott LF, Marder E (1993) Dynamic clamp: computer-generated conductances in real neurons. J Neurophysiol 69:992-995.
Stoop R, Schindler K, Bunimovich LA (2000) Neocortical networks of pyramidal neurons: from local locking and chaos to macroscopic chaos and synchronization. Nonlinearity 13:1515-1529.
Tateno T, Robinson HP (2007) Phase resetting curves and oscillatory stability in interneurons of rat somatosensory cortex. Biophysical J 92:683-695.
Teramae J, Tanaka D (2004) Robustness of the noise-induced phase synchronization in a general class of limit cycle oscillators. Phys Rev Lett 93:204103.
Teramae J, Fukai T (2008) Temporal precision of spike response to fluctuating input in pulse-coupled networks of oscillating neurons. Phys Rev Lett 101:248105.
Tsubo Y, Takada M, Reyes AD, Fukai T (2007a) Layer and frequency dependencies of phase response properties of pyramidal neurons in rat motor cortex. Eur J Neurosci 25:3429-3441.
Tsubo Y, Teramae J, Fukai T (2007b) Synchronization of excitatory neurons with strongly heterogeneous phase responses. Phys Rev Lett 99:228101.
Winfree AT (1967) Biological rhythms and the behavior of populations of coupled oscillators. J Theor Biol 16:15-42.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Ota, K., Aonishi, T. (2012). Bayesian Approach to Estimating Phase Response Curves. In: Schultheiss, N., Prinz, A., Butera, R. (eds) Phase Response Curves in Neuroscience. Springer Series in Computational Neuroscience, vol 6. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0739-3_8
Download citation
DOI: https://doi.org/10.1007/978-1-4614-0739-3_8
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-0738-6
Online ISBN: 978-1-4614-0739-3
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)