The neural code is expressed as either the rate or the timing of action potentials (spikes). Yet, spike patterns can be affected by changes in the speed and precision of axonal spike propagation, by spike failures or generation of ectopic spikes in the axon. These phenomena depend on the axon morphology, passive membrane properties, and the complement and properties of voltage-gated ion channels. Although axons are often presumed to faithfully transmit spikes with uniform velocity, conduction velocity often depends on the history of axonal activity on both fast and slow time scales. Thus, spike patterns generated at one end of the axon can change dramatically during propagation to the other end, potentially affecting neural coding. In addition, the degree to which axons contribute to the shaping of activity can depend on neuromodulators like dopamine or serotonin. Additionally, changes in axon excitability and propagation are widely used as diagnostic tools for peripheral neuropathies, commonly associated with dysregulation of ion channels. Yet, these measurements do not take into account how the natural temporal patterns of spikes are changed as they propagate along the axon. In sensorimotor systems, highly repetitive spiking is prevalent. During ongoing repetitive activity, history-dependence can occur with large time scales and, in turn, have distinct effects on shorter time scale dynamics like the frequency-dependence of propagation speed. Here, for the first time, we propose to develop a conceptual description of the history-dependence of axonal propagation, its modification by modulators and its influence on the neural code. Crustacean axons provide several experimental advantages to this end: they allow for multiple long-lasting electrophysiological recordings from different sites are amenable to voltage-clamp measurements, have a well-described range of natural activity patterns, readily follow artificially imposed patterns and share with mammalian axons in their constituent ion channels and activity-dependent dynamics of propagation. Furthermore, the motor patterns they are involved in are well defined and straightforward to monitor. Biophysical and pharmacological methods will be used to establish the types and properties of different ionic currents in these axons. Multiple-site electrophysiological recordings and imposed stimulation patterns will be used to establish the history- and frequency-dependence of propagation over multiple time scales, and their dependence on different ionic mechanisms and neuromodulators. Computational models will be constructed to aid in understanding the non-linear interactions between different ionic mechanisms. A mathematical decoding framework will be developed to produce a description of history- dependence that can be generalized for comparison between different axons, treatments and pathological conditions. Finally, a combination of experimental and theoretical methods will be used to characterize how axon dynamics affect neural coding, specifically how they change motor output and muscle dynamics.
The propagation of electrical signals in nerves and axon tracts is fundamental for nerve cell communication and information processing in the nervous system, and is altered in a range of diseases that change the electrical properties of nerve cell membranes. The impact on temporal fidelity and coding strategies are not well understood. The experimental and theoretical approaches proposed here aim at defining in general terms how the highly non-linear properties of axon membranes shape and alter the temporal patterns of neural activity in normal axons, in axons with altered membrane properties, or under the influence of chemical mediators, and how these changes affect postsynaptic responses.
|Li, Xinping; Bucher, Dirk; Nadim, Farzan (2018) Distinct Co-Modulation Rules of Synapses and Voltage-Gated Currents Coordinate Interactions of Multiple Neuromodulators. J Neurosci 38:8549-8562|
|Anwar, Haroon; Li, Xinping; Bucher, Dirk et al. (2017) Functional roles of short-term synaptic plasticity with an emphasis on inhibition. Curr Opin Neurobiol 43:71-78|
|Golowasch, Jorge; Bose, Amitabha; Guan, Yinzheng et al. (2017) A balance of outward and linear inward ionic currents is required for generation of slow-wave oscillations. J Neurophysiol 118:1092-1104|
|Zhang, Yang; Bucher, Dirk; Nadim, Farzan (2017) Ionic mechanisms underlying history-dependence of conduction delay in an unmyelinated axon. Elife 6:|
|Fox, David M; Tseng, Hua-An; Smolinski, Tomasz G et al. (2017) Mechanisms of generation of membrane potential resonance in a neuron with multiple resonant ionic currents. PLoS Comput Biol 13:e1005565|
|Chen, Yinbo; Li, Xinping; Rotstein, Horacio G et al. (2016) Membrane potential resonance frequency directly influences network frequency through electrical coupling. J Neurophysiol 116:1554-1563|
|Daur, Nelly; Nadim, Farzan; Bucher, Dirk (2016) The complexity of small circuits: the stomatogastric nervous system. Curr Opin Neurobiol 41:1-7|
|Mouser, Christina; Bose, Amitabha; Nadim, Farzan (2016) The role of electrical coupling in generating and modulating oscillations in a neuronal network. Math Biosci 278:11-21|
|Kintos, Nickolas; Nusbaum, Michael P; Nadim, Farzan (2016) Convergent neuromodulation onto a network neuron can have divergent effects at the network level. J Comput Neurosci 40:113-35|
|Garcia, Veronica J; Daur, Nelly; Temporal, Simone et al. (2015) Neuropeptide receptor transcript expression levels and magnitude of ionic current responses show cell type-specific differences in a small motor circuit. J Neurosci 35:6786-800|
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