Electro-Magnetic Waves within a Model for Charged Solitons

We analyze the model of topological fermions (MTF), where charged fermions are treated as soliton solutions of the field equations. In the region far from the sources we find plane waves solutions with the properties of electro-magnetic waves.Comment: 4 pages, 2 figure

Modeling the connectome of a simple spinal cord.

In this paper we develop a computational model of the anatomy of a spinal cord. We address a long-standing ambition of neuroscience to understand the structure-function problem by modeling the complete spinal cord connectome map in the 2-day old hatchling Xenopus tadpole. Our approach to modeling neuronal connectivity is based on developmental processes of axon growth. A simple mathematical model of axon growth allows us to reconstruct a biologically realistic connectome of the tadpole spinal cord based on neurobiological data. In our model we distribute neuron cell bodies and dendrites on both sides of the body based on experimental measurements. If growing axons cross the dendrite of another neuron, they make a synaptic contact with a defined probability. The total neuronal network contains ∼1,500 neurons of six cell-types with a total of ∼120,000 connections. The anatomical model contains random components so each repetition of the connectome reconstruction procedure generates a different neuronal network, though all share consistent features such as distributions of cell bodies, dendrites, and axon lengths. Our study reveals a complex structure for the connectome with many interesting specific features including contrasting distributions of connection length distributions. The connectome also shows some similarities to connectivity graphs for other animals such as the global neuronal network of C. elegans. In addition to the interesting intrinsic properties of the connectome, we expect the ability to grow and analyze a biologically realistic spinal cord connectome will provide valuable insights into the properties of the real neuronal networks underlying simple behavior

Opposite, bidirectional shifts in excitation and inhibition in specific types of dorsal horn interneurons are associated with spasticity and pain post-SCI

Spasticity, a common complication after spinal cord injury (SCI), is frequently accompanied by chronic pain. The physiological origin of this pain (critical to its treatment) remains unknown, although spastic motor dysfunction has been related to the hyperexcitability of motoneurons and to changes in spinal sensory processing. Here we show that the pain mechanism involves changes in sensory circuits of the dorsal horn (DH) where nociceptive inputs integrate for pain processing. Spasticity is associated with the DH hyperexcitability resulting from an increase in excitation and disinhibition occurring in two respective types of sensory interneurons. In the tonic-firing inhibitory lamina II interneurons, glutamatergic drive was reduced while glycinergic inhibition was potentiated. In contrast, excitatory drive was boosted to the adapting-firing excitatory lamina II interneurons while GABAergic and glycinergic inhibition were reduced. Thus, increased activity of excitatory DH interneurons coupled with the reduced excitability of inhibitory DH interneurons post-SCI could provide a neurophysiological mechanism of central sensitization and chronic pain associated with spasticity

Fluctuation-driven rhythmogenesis in an excitatory neuronal network with slow adaptation

We study an excitatory all-to-all coupled network of N spiking neurons with synaptically filtered background noise and slow activity-dependent hyperpolarization currents. Such a system exhibits noise-induced burst oscillations over a range of values of the noise strength (variance) and level of cell excitability. Since both of these quantities depend on the rate of background synaptic inputs, we show how noise can provide a mechanism for increasing the robustness of rhythmic bursting and the range of burst frequencies. By exploiting a separation of time scales we also show how the system dynamics can be reduced to low-dimensional mean field equations in the limit N → ∞. Analysis of the bifurcation structure of the mean field equations provides insights into the dynamical mechanisms for initiating and terminating the bursts