Multiple Firing Patterns in Deep Dorsal Horn Neurons of the Spinal Cord: Computational Analysis of Mechanisms and Functional Implications

Abstract

Deep dorsal horn relay neurons (dDHNs) of the spinal cord are known to exhibit multiple firing patterns under the control of local metabotropic neuromodulation: tonic firing, plateau potential, and spontaneous oscillations. This work investigates the role of interactions between voltage-gated channels and the occurrence of different firing patterns and then correlates these two phenomena with their functional role in sensory information processing. We designed a conductance-based model using the NEURON software package, which successfully reproduced the classical features of plateau in dDHNs, including a wind-up of the neuronal response after repetitive stimulation. This modeling approach allowed us to systematically test the impact of conductance interactions on the firing patterns. We found that the expression of multiple firing patterns can be reproduced by changes in the balance between two currents (L-type calcium and potassium inward rectifier conductances). By investigating a possible generalization of the firing state switch, we found that the switch can also occur by varying the balance of any hyperpolarizing and depolarizing conductances. This result extends the control of the firing switch to neuromodulators or to network effects such as synaptic inhibition. We observed that the switch between the different firing patterns occurs as a continuous function in the model, revealing a particular intermediate state called the accelerating mode. To characterize the functional effect of a firing switch on information transfer, we used correlation analysis between a model of peripheral nociceptive afference and the dDHN model. The simulation results indicate that the accelerating mode was the optimal firing state for information transfer.

Beyond faithful conduction: Short-term dynamics, neuromodulation, and long-term regulation of spike propagation in the axon

“…2C, middle panel). Many neurons even show different modes of activity at different times, probably related to different network states or modes of activation (Grace and Bunney, 1984a, b; Kao et al , 2008; Le Franc and Le Masson, 2010; Neiman et al , 2007). The instantaneous frequency during repetitive activity is usually not higher than a few hundred Hz, limited by the refractory period of the axon, as sodium channel inactivation and elevated potassium conductance following each spike usually prevent spiking for several milliseconds.…”

Origin of heterogeneous spiking patterns from continuously distributed ion channel densities: a computational study in spinal dorsal horn neurons

“…Systematically co‐varying those two conductances revealed boundaries that represent the transition between spiking patterns. Le Franc and Le Masson () used a similar approach to study spiking patterns in deep dorsal horn neurons, but we are not aware of previous studies like this in the superficial dorsal horn. The boundaries we found imply that subtle changes in or can cause a neuron to switch spiking patterns.…”

“…To test our hypothesis, we reproduced tonic, single, gap, delayed and reluctant spiking in a simple conductancebased computer model. Then, following the approach used by Le Franc and Le Masson (2010) to study spiking patterns in deep dorsal horn neurons, we systematically co-varied the densities of potassium channels responsible for SDH neuron spiking patterns in order to map out parameter combinations where the model switched patterns (i.e. bifurcated).…”

Investigating circadian rhythmicity in pain sensitivity using a neural circuit model for spinal cord processing of pain

“…This approach allows for a detailed representation of the geometry and biophysics of each neuron connected to the other neurons via a network whose biophysical behavior and characteristics are then calculated numerically [12]. Such a network model has been previously constructed for the interaction between a deep dorsal horn neuron and Aδ fibers [17], a wide-dynamic range projection neuron [1], and for the dorsal horn circuit between a projection, inhibitory, and excitatory neuron [38]. All these models were validated by showing that they are able to reproduce observed phenomena such as wind-up in the presence of nonzero calcium conductances and NMDA [1,17,38].…”