In the context of signal processing the impulse response provides a fundamental characterization of the behavior of a dynamical system.
Although the ideal impulse stimulus is practically not achievable, it is possible to obtain a reasonable approximation of it by using an actuator element that generates high frequency displacements. This type of light tactile-vibratory stimulation is known to target both deep skin (e.g. fast responding, fast adapting Pacinian corpuscles)2 and superficial receptors (e.g. low-threshold slowly adapting Merkel discoid structures)2.
Current stimulation devices, mainly piezoelectric actuators, are charged with a number of drawbacks, not least resonances and small displacements. To overcome these flaws, an alternative implementation of impulse-like stimulation is proposed by using a blunted tip (a cactus smoothed tip in our case) vertically mounted on the membrane center of a mid-range speaker cone. This provides the advantage of larger displacements and broader frequency spectrum.
An effective application of such a device was the study of the relevant neurophysiological problem of the LFPs to spikes dependency. Because of the subtle temporal association between these electrical events a finely regulated device was needed for delivering peripheral stimuli. The stimuli had to be as fast and spatially selective as possible in order to reduce the "background noise" and sharpen the signals of interest. To this purpose, the stimulation device and the stimulus delivery protocol were jointly optimized for the task. In this paper, we describe the technique and present some representative results.
A stimulation protocol based on randomized paired-pulses has been designed and optimized in order to avoid habituation. This protocol offered the advantage of classical paired pulses and reduced the possibility of spurious locking between stimuli and spontaneous periodical bursts of neuronal activity.
By using this randomized paired pulse it was possible to obtain fast and reliable LFP and spike responses and to capture the special feature of these responses related to the dependence of both LFPs and spikes on the stimulation history. Indeed, from the raw LFP responses, a set of three LFP features (the LFP itself, the LFP first derivative and phase of the first derivative) strongly correlating with the average spike response, was also extracted.
Few methods have been proposed to fit models that predict spikes from LFPs3,4. In general a critical point of the model fitting process, common also to the prediction of spike event from the stimulus signal, is constituted by the appropriate choice of the objective function to maximize/minimize. While a range of objective functions has been proposed (e.g. correlation and coherence)5 none of these jointly captures the whole complexity of spike responses. Accordingly, a novel framework based on multi-objective optimization is introduced. We show that by using the proposed devised and this computational framework it is possible to estimate a set of predictive models based on strong LFP to spike relationships.