Isometric Coding of Spiking Haptic Signals by Peripheral Somatosensory Neurons

We study how primary tactile afferents encode relevant contact features to mediate early processing of haptic information. In this paper, we apply metrical information theory to perform temporal decoding of human microneurography data. First, we enrich the theory by deriving a novel spike train metrics inspired by neuronal computation. This spike train metrics can be interpreted biologically and its behaviour is not influenced by spontaneous activity, which decreases the ability of other spike metrics to separate input patterns. Second, we employ our metrical information tools to demonstrate that primary spiking signals allow a putative neural decoder to go beyond stimulus discrimination. They transmit information about geometrical properties of the input space. We show that first-spike latencies are enough to guarantee maximum information transmission of tactile stimuli. However, entire primary spike trains are necessary to encode isometric representations of the stimulus space, a likely basis for generalisation in haptic perception.