Ronald L. Calabrese - US grants

Rhythmic motor acts such as breathing, chewing and locomotion are attractive for the study of how motor systems are organized and modulated to produce adaptive output. These motor acts are generated fully or in part by rhythmically active neural networks, pattern generators, that are easily activated in the isolated CNS and are thus amenable to experimental analysis. Particularly in invertebrates, where the restricted number, large size, and identifiability of neurons offer technical advantages, progress in understanding pattern generating networks and their adaptive modulation has been rapid. We have analyzed in detail the neural network and muscular system that generates heartbeat in the medicinal leech. Reciprocally inhibitory pairs of heart interneurons pace the heartbeat rhythm. Critical switch interneurons coordinate the pattern of heart motor neurons innervating the two hearts to produce two alternating coordination states. The period and pattern of the rhythm generating interneurons and the properties of heart muscle are modulated by FMRFamide and we have identified endogenous RFamide peptides in the CNS. We have explored the ionic currents and graded synaptic transmission that contribute to rhythmicity in heart interneurons and have begun to organize these data in a realistic computer model. Here we propose to continue our study of the intrinsic membrane and synaptic properties of heart interneurons that contribute to rhythmicity, and how these properties are modulated by RFamide peptides. To guide these studies, data will be incorporated into a ongoing computer simulation. We will explore the diversity of RFamide peptides present in the CNS and their modulatory effects upon neural and muscular targets. We will analyze the membrane properties of heart motor neurons to determine how these properties transform the output of the pattern generating network of interneurons, and we will explore in detail how the alternating coordination states of motor outflow are generated and controlled. We will pursue a multifaceted approach toward these aims, involving biochemical, anatomical, and physiological techniques. By studying the mechanisms for oscillation in neural networks and for the modulation and reconfiguration of these networks in the leech, we will uncover important information applicable to other more complex motor systems.

DESCRIPTION (provided by applicant): Brain functions such as information processing, memory formation and motor control often involve oscillatory neuronal networks. Mathematical modeling has been particularly useful in gaining insights into how such oscillations are generated and how they contribute to nervous system function. Dynamic current clamp techniques (Sharp et al, 1993) that permit the interactions of computer models and neuronal networks through artificial synapses have opened up a new form of modeling, hybrid systems analysis. Recent progress in computer hardware makes incorporation of biophysically realistic mathematical models of neurons and networks into hybrid systems feasible. Still, hybrid systems are difficult to construct without model simplification. Analog very large-scale integrated circuit (aVLSI) models overcome the speed limitation of digital models, but until now very few aVLSI models have been as realistic or as amenable to parameter variations as their digital counterparts. Especially in studies of oscillatory neural networks, hybrid system having a model neuron connected to real counterpart combines advantages of mathematical modeling and real electrophysiological experiments. In the R21 phase, we will expand upon our research on a neuronal network that times heartbeat in the leech toward the goal of developing useful hybrid systems for the study of oscillation in neuronal networks. Aim 1. Development of a real-time dynamic clamp and a mathematical model of heart interneuron model cell running in real-time on a controller board. Aim 2. Development of an aVLSI (silicon) heart interneuron model. Aim 3. Construction of hybrid systems for studying half-center oscillators underlying leech heartbeat central pattern generation. Once these tools are developed, we will use them in the R33 phase to explore basic questions about the generation of oscillation in mutually inhibitory neuronal networks. Aim 1: Continue the development of the silicon models of a HN neuron and thoroughly ascertain their dynamics. Aim 2: Analyze dynamic behaviors of the silicon and living neurons using a controller board based dynamic clamp. Aim 3: Explore systematically the dynamics of hybrid half-center oscillators. The development of such tools will make hybrid system analysis easily accessible and thus further our insights into neuronal network dynamics in a variety of preparations.

DESCRIPTION (provided by applicant): We seek to foster and enhance Systems and Computational Neuroscience at Emory University by hiring a new assistant professor with primary appointment in the Department of Biology and adjunct appointments in the Departments of Physics, and Mathematics and Computer Science. A group of 4 NIH supported investigators in the Department of Biology, all of whom do both neurophysiology and computational neuroscience, will serve as the focus group for this new hire. This new hire will strengthen and broaden this core group by providing much needed systems expertise and will foster greater links to computational biologists in the Departments of Physics, and Mathematics and Computer Science and to the greater biomedical research complex of the health science centers of Emory University (principally the School of Medicine and the inter-institutional Wallace H. Coulter Department of Biomedical Engineering (BME) at Georgia Institute of Technology and Emory University) through the interdepartmental Graduate Program in Neuroscience (NS) and the Graduate Program in Biomedical Engineering (BME) and through direct collaboration. This hire would be particularly beneficial to the greater neuroscience and computational biology communities at Emory because systems level research, particularly sensory systems and sensory motor integration is not well represented in our faculty. The new faculty will have his/her lab located in the Department of Biology in space committed by Victor Corces, Chair of Biology on the second floor of the Rollins Research Building (Rooms 2152 and 2158- -1,100 ft2) in immediate proximity to the 4 computational neuroscientists in the Department of Biology. In the Department of Biology (part of the Emory College of Arts and Sciences), tenure track assistant professor appointments are normally for 6 years after which promotion to tenure is considered pending a successful 4th year review. The new faculty member will have limited teaching responsibilities, commensurate with the requirements of the P30 award and will be expected to join the graduate programs in Neuroscience (NS) and Biomedical Engineering (BME) and thus will have access to graduate students as research assistants. RELEVANCE (See instructions): Systems and Computational Neuroscience are growing areas within the neuroscience research community because they offer a synthesis of research at lower (molecular and cellular) and higher (behavioral). Thus these areas will become crucial to our understanding of disease states of the nervous system.

DESCRIPTION (provided by applicant): Many everyday rhythmic activities such as breathing, chewing, and locomoting, are programed in part by neuronal networks called central pattern generators (CPGs). Mechanistic analysis of CPGs has been especially fruitful in the CPGs of invertebrates, which have relatively few neurons and can therefore be precisely defined in terms of identified neurons and specific synaptic connections. CPG networks of invertebrates have become proxies for experimental and theoretical analyses of how brain networks reliably yet flexibly process sensory information or program motor output. Our detailed analyses of the leech heartbeat CPG continue to contribute substantially to the organizing principles by which we now understand network function. Theoretical studies and experimental analysis in different networks and species have shown that the intrinsic membrane properties of the neurons and the strengths of their synaptic connections show 2-5 fold animal-to-animal variability, yet networks still produce functional output. Nevertheless, there are clear examples - including from our own work - showing that individual variation in synaptic and intrinsic properties do have functional consequences that manifest in network performance or susceptibility to perturbation. These studies imply that to understand fundamentally a neuronal network, we must strive to gather complete data from individuals because networks from individuals, while functionally stereotyped, may have different mechanistic underpinnings at the level of membrane currents and synaptic connections with discernible functional consequences. In light of this variation, an ensemble modeling approach is necessary to generate and test hypotheses about living networks. In this approach, multiple model instances (with different parameters) generated by evolutionary algorithms or brute force parameter variation are used. A prerequisite for this approach is experimental studies that determine parameter variation in the living system across a variety of networks. Our experiments will integrate computational and neurophysiological approaches to elucidate basic mechanisms of network function and the impact of individual variation in network parameters. Moreover, we will use individual variation as an innovative tool to probe network function. The leech heartbeat CPG is analogous to spinal CPGs that produce coordinated rhythmic activity in motor neurons, and its relative simplicity and superb accessibility allow a level of cellular analysis not currently possible in spinal cord. We will analyze the impact of inter-individual variation in CPG input and motor neuron intrinsic properties on motor output and how movements reflect this variation. These studies will provide basic mechanistic insights into network function in individuals, which will be useful in the study of spinal and brain networks and can lead to therapies for spinal cord and brain injury and disorders affecting motor performance.