Modeling Synaptic Integration of Tactile Inputs in a VB Thalamocortical Neuron

Ventrobasal (VB) thalamic neurons are the primary relay of somatosensory information to the cortex. In vivo, they operate in a high-conductance state shaped by continuous, asynchronous synaptic activity, yet must reliably detect and transmit transient sensory signals. Understanding how intrinsic membrane properties enable these neurons to distinguish phasic sensory inputs from ongoing synaptic noise is therefore essential for elucidating normal and pathological sensory processing. We developed a biophysically detailed, multicompartmental model of a VB neuron using Python and NEURON to investigate this process. To reproduce key features of the in vivo synaptic environment, we implemented a hybrid input paradigm combining stochastic background activity with a simulated tactile stimulus. Background network activity was represented by independent, uncorrelated Poisson processes driving balanced excitatory and inhibitory synaptic inputs. Superimposed on this ongoing activity, a simulated mechanical stimulus, such as von Frey filament indentation, was modeled as a synchronous, phasic burst of presynaptic activity with Gaussian temporal dispersion to capture biological variability in mechanoreceptor recruitment and axonal conduction. This framework enables us to examine how changes in intrinsic membrane conductance associated with neuromodulation or disease affect the detection, integration, and fidelity of tactile signals at the single-neuron level.