It is well known that neuronal network activity is shaped by extrinsic neuromodulation, synaptic interactions and by the intrinsic properties of each neuron within the network. Intrinsic properties in turn are determined chiefly by the ionic currents expressed by each cell. The changes and regulation of each one of these processes results in a diverse repertoire of network outputs. Neurons and networks have been shown to generate stable electric activity despite wide variability in ionic current levels. Such stability could, however, be compromised if variability is allowed to go unchecked. The global ionic current variability in a neuron could be reduced, and output stability enhanced, if the conductance variance of multiple ionic currents depended on each other and were coordinately regulated. Regulation of ionic current levels can in principle be controlled by two classes of mechanisms: 1) mechanisms that sense a departure of activity from a given set point or range that trigger compensatory changes leading to activity restoration, 2) mechanisms that stabilize activity in an activity-independent manner. The crustacean pyloric and gastric mill networks of the stomatogastric ganglion have been used as model systems to study the role of neuromodulation, synaptic properties and intrinsic neuronal properties on the generation of rhythmic activity. These networks generate rhythmic activity patterns that drive digestive behaviors. Other rhythmic pattern generating networks drive behaviors that are also essential for survival (e.g. respiration, locomotion) or are thought to be key in cognitive functions (attention, memory, etc). Because of their basic nature, it could be argued that these rhythms need to be stable and able to recover from disruptive perturbations to maximize survival. The pyloric network has this kind of robust behavior and will be used to examine biophysical mechanisms that stabilize network output. The guiding hypothesis of this proposal is that neuronal and network activities are regulated by two distinct mechanisms at two different time scales: 1) via slow-acting neuromodulatory effects that control the levels and the correlated expression of multiple ionic currents that are not acutely modulated by them, 2) via fast-acting activity-dependent mechanisms that regulate ionic currents levels. I propose to examine the mechanisms of action of these two regulatory processes, characterize their effects in individual neurons, and examine their role on rhythmic activity generation and stability. We will focus especially on the novel, slow, neuromodulator-mediated process. We will use electrophysiological, molecular and computational methods. The capacity to generate stable neuronal output and to recover such output following disease or trauma is crucial to ensure behavioral stability and, ultimately, survival. The mechanisms underlying such stabilization and recovery of function are not well known, and their understanding may be of enormous therapeutical relevance.

Public Health Relevance

The generation of rhythms in the nervous system is crucial to the survival of animals since they are involved in the production of vital functions (heart beat, respiration, locomotion, digestion, etc.) and is also thought to be essential for the generation of many cognitive functions (memory, perception, awareness, sleep/wake cycles, etc). Biological rhythms are heavily regulated by neuroactive substances such as neuromodulators, hormones and neurotransmitters, as well as by their own state of activity. In this proposal we will examine the mechanisms by which neuromodulators and the neuronal networks own activity regulate rhythmic pattern generation in a simple system. This knowledge is essential to understand the normal function of the nervous system, its response to perturbations, and to design effective treatments of pathological states, such as trauma, memory and sleep disorders.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH064711-11
Application #
8434281
Study Section
Sensorimotor Integration Study Section (SMI)
Program Officer
Glanzman, Dennis L
Project Start
2001-12-01
Project End
2014-02-28
Budget Start
2013-03-01
Budget End
2014-02-28
Support Year
11
Fiscal Year
2013
Total Cost
$254,470
Indirect Cost
$88,150
Name
Rutgers University
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
075162990
City
Newark
State
NJ
Country
United States
Zip Code
07102
Bose, Amitabha; Golowasch, Jorge; Guan, Yinzheng et al. (2014) The role of linear and voltage-dependent ionic currents in the generation of slow wave oscillations. J Comput Neurosci 37:229-42
Zhang, Yili; Golowasch, Jorge (2011) Recovery of rhythmic activity in a central pattern generator: analysis of the role of neuromodulator and activity-dependent mechanisms. J Comput Neurosci 31:685-99
Golowasch, Jorge; Thomas, Gladis; Taylor, Adam L et al. (2009) Membrane capacitance measurements revisited: dependence of capacitance value on measurement method in nonisopotential neurons. J Neurophysiol 102:2161-75
Zhang, Yili; Golowasch, Jorge (2007) Modeling Recovery of Rhythmic Activity: Hypothesis for the role of a calcium pump. Neurocomputing 70:1657-1662
Gansert, Juliane; Golowasch, Jorge; Nadim, Farzan (2007) Sustained rhythmic activity in gap-junctionally coupled networks of model neurons depends on the diameter of coupled dendrites. J Neurophysiol 98:3450-60
Haedo, Rodolfo J; Golowasch, Jorge (2006) Ionic mechanism underlying recovery of rhythmic activity in adult isolated neurons. J Neurophysiol 96:1860-76
Nadim, Farzan; Golowasch, Jorge (2006) Signal transmission between gap-junctionally coupled passive cables is most effective at an optimal diameter. J Neurophysiol 95:3831-43
Rabbah, Pascale; Golowasch, Jorge; Nadim, Farzan (2005) Effect of electrical coupling on ionic current and synaptic potential measurements. J Neurophysiol 94:519-30
Luther, Jason A; Robie, Alice A; Yarotsky, John et al. (2003) Episodic bouts of activity accompany recovery of rhythmic output by a neuromodulator- and activity-deprived adult neural network. J Neurophysiol 90:2720-30