Pain-related sensory input is processed in the spinal cord before being relayed to the brain. That processing profoundly influences whether stimuli are correctly or incorrectly perceived as painful.Significant advances have been made in identifying the types of excitatory and inhibitory neurons that comprise the spinal dorsal horn (SDH), and there is some information about how neuron types are connected, but it remains unclear how the overall circuit processes sensory input. To explore SDH circuit function, we developed a computational model of the circuit that is tightly constrained by experimental data. Our model comprises conductance-based neuron models that reproduce the characteristic firing patterns of excitatory and inhibitory neurons. Excitatory neuron subtypes defined by calretinin, somatostatin, delta opioid receptor, protein kinase C gamma, or vesicular glutamate transporter 3 expression or by transient/central spiking/morphology, and inhibitory neuron subtypes defined by parvalbumin or dynorphin expression or by islet morphology were synaptically connected according to available qualitative data. Synaptic weights were adjusted to produce firing in projection neurons, defined by neurokinin-1 expression, matching experimentally measured responses to a range of mechanical stimulus intensities. Input to the circuit was provided by three types of afferents whose firing rates were also matched to experimental data. To validate our model, we ablated specific neuron types or applied other changes and compared model output with experimental data after equivalent manipulations. The resulting model provides a valuable tool for testing hypotheses in silico to plan novel experiments on SDH circuit dynamics and function.

Spinal cord stimulation (SCS) reduces chronic pain. Conventional (40-60 Hz) SCS engages spinal inhibitory mechanisms by activating low-threshold mechanoreceptive afferents with axons in the dorsal columns (DCs). But activating DC axons typically causes a buzzing sensation (paresthesia) that can be uncomfortable. Kilohertz-frequency (1-10 kHz) SCS produces analgesia without paresthesia and is thought, therefore, not to activate DC axons, leaving its mechanism unclear. Here we show in rats that kilohertz-frequency SCS activates DC axons but causes them to spike less synchronously than conventional SCS. Spikes desynchronize because axons entrain irregularly when stimulated at intervals shorter than their refractory period, a phenomenon we call overdrive desynchronization. Effects of overdrive desynchronization on evoked compound action potentials were verified in simulations, rats, pigs, and a chronic pain patient. Whereas synchronous spiking in DC axons is necessary for paresthesia, asynchronous spiking is sufficient to produce analgesia. Asynchronous activation of DC axons thus produces paresthesia-free analgesia.

Subfornical organ (SFO) neurons exhibit heterogeneity in current expression and spiking behaviour, where the two major spiking phenotypes appear as tonic and burst firing. Insight into the mechanisms behind this heterogeneity is critical for understanding how the SFO, a sensory circumventricular organ, integrates and selectively influences physiological function. To integrate efficient methods for studying this heterogeneity, we built a single-compartment, Hodgkin-Huxley type model of an SFO neuron that is parameterized by SFO-specific in vitro patch clamp data. The model accounts for the membrane potential distribution and spike train variability of both tonic and burst firing SFO neurons. Analysis of model dynamics confirms that a persistent Na and Ca current are required for burst initiation and maintenance, and suggests that a slow-activating K current may be responsible for burst termination in SFO neurons. Additionally, the model suggests that heterogeneity in current expression and subsequent influence on spike afterpotential underlies the behavioural differences between tonic and burst firing SFO neurons. Future use of this model in coordination with single neuron patch clamp electrophysiology, provides a platform for explaining and predicting the response of SFO neurons to various combinations of circulating signals, and thus elucidating the mechanisms underlying physiological signal integration within the SFO.