A point process approach to encode tactile afferents

In daily activities, humans manipulate objects and do so with great precision. Empirical studies have demonstrated that signals encoded by mechanoreceptors facilitate the precise object manipulation in humans, however, little is known about the underlying mechanisms. Current models range from complex- they account for skin tissue properties-to simple regression fit. These models do not describe the dynamics of neural data well. Because experimental neural data is limited to spike instances, they can be viewed as point processes. We discuss the point process framework and use it to simulate neural data possessing behaviors similar to experimental neural data. The characteristics of neural data were identified via visualization and descriptive statistics based on the experimental data. Then we fit candidate models to the simulated data and perform goodness-of fit to assess how well the models perform. This type of analysis facilitates the mapping of neural data to stimulus. Given this mapping, we can generate a population of spike trains, and infer from them in order to recover the applied stimulus. The knowledge acquired may provide insight into some fundamental sensory mechanisms that are responsible for coordinating force components during object manipulation. We envisage that the knowledge may guide the design of sensorycontrolled biomedical devices and robotic manipulators.