Axonal spike-count regimes link spinal cord stimulation periodicity to artificial sensory detection and discrimination in rodents
Tvrdy, T. J.; Bhattacharya, A.; Radhakrishna, R.; Slack, J. C.; Yadav, A. P.
Show abstract
Electrical stimulation is widely used to evoke artificial sensation, yet how temporal stimulation patterns are transformed into neural activity and perception remains poorly understood. Recent behavioral studies have shown that increasing the aperiodicity of spinal cord stimulation pulse trains alters both detection thresholds and discrimination performance despite constant pulse counts, suggesting a role for temporal structure beyond rate alone. However, the neural mechanisms underlying these effects remain unresolved. We developed a biophysically grounded computational framework linking a finite-element model of the rodent spinal cord, conductance-based axon simulations, and observer decision models to investigate how stimulation periodicity shapes neural responses and resulting behavior. By systematically varying stimulation amplitude, frequency, and inter-pulse interval variability, we identified distinct spike-count regimes arising from interactions between stimulation timing and axonal membrane dynamics. These regimes ranged from single spikes and small volleys to sustained spike trains, and they exhibited diverse frequency- and variability-dependent trends. No single regime was able to sufficiently explain experimentally observed detection threshold trends; instead, mixtures of regimes accurately reproduced both frequency-dependent threshold behavior and trial-level variability. Extending this framework to periodicity discrimination, we show that features derived from regime mixtures contain sufficient information to recover behavioral psychometric curves. Furthermore, observer model results provide a mechanistic account of behavioral asymmetries depending on the periodicity of the reference stimulus: discrimination relative to periodic inputs relied on combined rate and timing evidence, whereas discrimination relative to aperiodic inputs was dominated by timing irregularity. These results establish a mechanistic link between stimulation temporal structure, axonal spike generation, and perceptual behavior. This framework suggests that spinal cord stimulation does not operate within a single fixed neural regime but instead engages a spectrum of spike-count regimes whose mixtures shape perception. These findings have important implications for the design of biomimetic stimulation strategies, highlighting temporal patterning as a key dimension for controlling sensory outcomes.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Physiological activity within peripheral nerves influences neural output in response to electrical stimulation: an in vivo study 95%
- Responses of Model Cortical Neurons to Temporal Interference Stimulation and Related Transcranial Alternating Current Stimulation Modalities 95%
- A biomimetic electrical stimulation strategy to induce asynchronous stochastic neural activity 95%
Similar papers in this journal
- A computational model explains and predicts substantia nigra pars reticulata responses to pallidal and striatal inputs 95%
- Diverse and complex muscle spindle afferent firing properties emerge from multiscale muscle mechanics 95%
- Memory at your fingertips: how viscoelasticity affects tactile neuron signaling 95%
Similar papers in this journal
- Excitatory-inhibitory windows shape coherent neuronal dynamics driven by optogenetic stimulation in the primate brain 96%
- Excitation and inhibition delays within a feedforward inhibitory pathway modulate cerebellar Purkinje cell output in mice 95%
- Multiscale computer model of the spinal dorsal horn reveals changes in network processing associated with chronic pain 95%
Similar papers in this journal
- An integrate-and-fire spiking neural network model simulating artificially induced cortical plasticity 95%
- A Stochastic Dynamic Operator framework that improves the precision of analysis and prediction relative to the classical spike-triggered average method, extending the toolkit. 95%
- Modeling synaptic integration of bursty and beta oscillatory inputs in ventromedial motor thalamic neurons in normal and parkinsonian states 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.