Figure 8.

Computational modeling of [Cl⁻]_i dynamics in a network of RT neurons. A, Network composed of a linear array of 100 RT neurons, each projecting GABAergic synapses to the eight nearest neurons. Current injections to Cells 46, 51, and 56 simulated excitatory inputs to this network. B, Graphical representation of evolving [Cl⁻]_i within all model RT neurons over time. Action potential activity generated by individual model RT neurons is overlaid. White vertical lines indicate action potentials in neurons directly receiving current injection, whereas black vertical lines indicate synaptically evoked action potentials. In a network wherein Cells 46, 51, and 56 received 30 Hz stimulation and all cells were characterized by a Cl⁻ recovery rate (τ_rec) of 32 s, [Cl⁻]_i initially increased only in the neurons receiving direct GABAergic projections from the stimulated cells. Over time, however, activity propagated throughout the entire network. C, Example V_m (red) and [Cl⁻]_i (blue) traces from Cells 51, 53, and 80 from B. Cell 51 received direct, current stimulation. Cell 53 received monosynaptic input from a stimulated cell. Cell 80 was more distant from the site of stimulation. Expanded insets show the shifts in V_m and [Cl⁻]_I at various time points during the propagation of activity within the RT network (C1–C3). When [Cl⁻]_i became sufficiently elevated for GABAergic signaling to evoke action potentials in these neurons (C2), the rise in [Cl⁻]_i began to spread throughout the network, along with an increase in the number of cells firing action potentials (C3). D, Higher-frequency stimulation increased the number of activated neurons (arrows indicate the inflection point where activation first occurs) and (E) decreased the time required to first evoke action potentials (Cell 53, solid lines; Cell 80, dashed lines). F, Both slower τ_rec and more frequent stimulation were correlated with an increased likelihood of activity spreading within the RT nucleus.