Spontaneous dynamics and information transfer in sensory neurons A dissertation presented to the faculty of the College of Arts and Sciences of Ohio University In partial fulfillment of the requirements for the degree Doctor of Philosophy Hoai T. Nguyen August 2012 © 2012 Hoai T. Nguyen. All Rights Reserved. 2 This dissertation titled Spontaneous dynamics and information transfer in sensory neurons by HOAI T. NGUYEN has been approved for the Department of Physics and Astronomy and the College of Arts and Sciences by Alexander B. Neiman Associate Professor of Physics and Astronomy Howard Dewald Dean, College of Arts and Sciences 3 Abstract NGUYEN, HOAI T., Ph.D., August 2012, Physics and Astronomy Spontaneous dynamics and information transfer in sensory neurons (101 pp.) Director of Dissertation: Alexander B. Neiman Peripheral sensory systems convey information about the outside world to the central nervous system and are organized in a feed-forward networks passing information through a series of layers. Of high interest are basic problems of how sensory information is encoded in dynamical states of a network and how correlated activity progresses from one network layer to another. Often peripheral receptors are characterized by spontaneous noisy oscillatory activity which introduces temporal and spatial correlations in neuronal spike trains. This dissertation uses computational methods to elucidate the role of oscillations in shaping of spontaneous dynamics and information transfer in sensory receptors. In particular, we concentrate on hair cell - sensory neurons types of receptors which mediate the senses of hearing, balance and electroreception. We develop a conductance-based Hodgkin-Huxley type model in which spontaneous noisy inputs from sensory cells modulate stochastic synaptic transmission. The model is tuned to reproduce experimental data from electroreceptors of paddlefish and from vestibular utricle sensory neurons of a turtle. Using the model we study how the interplay of ionic currents and synaptic noise affects the statistics of spontaneous dynamics and response properties of the system. In particular, we isolate a region in the parameter space of the model which maximizes information transmission of the system. We contrasted linear and non-linear responses of the model and show that coherent oscillations from epithelial cells enhance significantly stimulus-induced synchronization of neural responses and information transfer. We develop a network model for peripheral electroreceptors to study how correlated noises common to several sensory neurons influence the overall dynamics of the network and transmission of sensory stimuli. In contrast to previous studies on noise 4 correlations which concentrated on weakly correlated white noise, we consider strongly correlated narrow-band fluctuations. We describe in detail transformation of input correlated signals through the network. Furthermore, we show that while coherent epithelial oscillations enhance information transmission for a single sensory neuron, the presence of spatially correlated noise introduces redundancy reducing stimulus coding efficiency and information rate on the network level. Interestingly, this information reduction can be minimized if the frequency of noisy oscillations is about one half of the firing rates of neurons in the network. Although the model is set to mimic electroreceptors in paddlefish, it can be viewed in a more general context of a networks of oscillatory neurons processing correlated rhythmical signals. Approved: Alexander B. Neiman Associate Professor of Physics and Astronomy 5 Acknowledgements Almost five years have passed since I began my PhD studies in Ohio University, and I could not have come this far without the guidance, support, help, advice, and encouragement of many people present along my journey. First and foremost, I would like to express my deepest gratitude to my PhD advisor Professor Alexander B. Neiman, for his excellent guidance, caring, patience, and providing me with an excellent atmosphere for doing research. In addition, I also appreciate his support and understanding in my life outside of physics. Without it, I could not have spent an incredible and beautiful time with my husband Anh Ngo and my son Chi Kien Ngo when we moved to Baton Rouge, Louisiana, one year ago and now in the lovely city Madison, Wisconsin. At the same time, I am thankful for all insightful discussion, support from my PhD degree committee: Professors Nancy Sandler, Michael H. Rowe, and David F. J. Tees. I would never have been able to finish my dissertation without the guidance of my committee members. I specially thank Professors M.H. Rowe and D.F. Russell for providing experimental data. Special thanks to Professor Sergio Ulloa. I really appreciate all kindness and encouragement he gave to not only me but also my small family. I would also like to thank Department of Physics and Astronomy for the immense support I have had all along. Last but most certainly not least, I would like to call attention to my family: my Mom Pham Thi Than and my Dad Nguyen Van Khang, my husband Anh Ngo, my wonderful son Chi Kien Ngo. Thank you for your unconditional love and support. You are the world to me. I acknowledge financial support from the National Institutes of Health under Grant No. DC05063 and by the Biomimetic Nanoscience and Nanotechnology program of Ohio University. 6 Table of Contents Page Abstract ......................................... 3 Acknowledgements ................................... 5 List of Figures ...................................... 8 1 Introduction ..................................... 13 2 Spontaneous dynamics and response properties of peripheral sensory receptors . 19 2.1 Statistical measures of spontaneous activity ................. 19 2.2 Characterization of response dynamics of a single neuron .......... 23 3 Spontaneous dynamics of vestibular utricle and electroreceptor afferents ..... 30 3.1 Vestibular afferents .............................. 30 3.2 Electroreceptors afferents ........................... 34 3.3 Conclusion .................................. 38 4 Single neuron models of stochastic spontaneous activity .............. 41 4.1 Conductance-based Hodgkin-Huxley type model .............. 41 4.2 Stochastic synaptic model ........................... 43 4.3 Numerical simulations of the modified Hodgkin-Huxley model: compari- son with experimental data .......................... 47 4.4 Simplified neuronal model: theta neuron ................... 49 4.5 Conclusion .................................. 52 5 Effect of temporal correlations on spontaneous and response dynamics of sensory neurons ....................................... 54 5.1 Spontaneous dynamics of the Hodgkin-Huxley model of electroreceptors . 55 5.2 Linear response and stimulus encoding .................... 62 5.3 Nonlinear response ............................. 64 5.4 Conclusion .................................. 70 6 Effect of spatial and temporal correlations on spontaneous dynamics and stimulus coding by small-scale networks of peripheral sensory neurons ........... 72 6.1 Network model ................................ 73 6.2 Input – output correlation measures ..................... 75 6.3 Stimulus encoding by a network ....................... 78 6.4 Transformations of noise correlations by the network ............ 79 6.5 Stimulus encoding and information transmission by the network ...... 85 7 6.6 Conclusion .................................. 90 7 Conclusion and Outlook .............................. 92 References ........................................ 95 8 List of Figures Figure Page 1.1 Schematic drawing of different classes of vestibular afferents. Shown are two types of sensory hair cells (I and II) and three classes of afferents: pure-calyx (C), dimorphic (D) and pure button (B). Mechanoelectrical transduction ion channels in stereocilia of hair cells are labeled as Met. Possible locations of spike initiation zones in afferents are labeled by asterisks. Grey unlabeled structures are efferents, which are neurons transferring feedback signals from the brain to hair cells. Modified from a courtesy figure obtained from Dr. Ruth Anne Eatock (see also [1]). ............................ 14 1.2 The organization of an electroreceptor on the rostrum of paddlefish. Shown is a cluster of skin pores each leading to a canal. Sensory cells in epithelial layer at the bottom of canals innervated by a few afferent neurons. Modified from [2]. 15 2.1 Schematics of neuron perturbed by the stimulus s(t). .............. 24 3.1 Spontaneous dynamics vestibular utricle afferents. (a) Raw extracellular recording (lower panel) and a sequence of identified spike times of a vestibular afferent. (b) Probability density function (PDF) of interspike intervals for three representative vestibular afferent discharges. ................... 31 3.2 Serial correlation coefficients (SCCs) (a) and power spectral density (PSD) for three representative utricle afferents. The data is the same as in Fig 3.1: black, red and blue colored lines show SCCs and PSD for the same afferents as in Fig 3.1. Grey lines in panel (a) show minimum and maximum SCCs values for corresponding renewal spike trains. In panel (b) grey lines show PSDs of renewal spike trains. ............................... 32 3.3 Dynamical entropies for ISIs sequences of two VAs. Red line and symbols shows dynamical entropies calculated for the original sequence. Blue line correspond to corresponding renewal surrogate ISIs. Errorbars we estimated by calculating