Rate and timing of cortical responses driven by separate sensory channels

Abstract

The sense of touch comprises multiple sensory channels that each conveys characteristic signals during interactions with objects. These neural signals must then be integrated in such a way that behaviorally relevant information about the objects is preserved. To understand the process of integration, we implement a simple computational model that describes how the responses of neurons in somatosensory cortex—recorded from awake, behaving monkeys—are shaped by the peripheral input, reconstructed using simulations of neuronal populations that reproduce natural spiking responses in the nerve with millisecond precision. First, we find that the strength of cortical responses is driven by one population of nerve fibers (rapidly adapting) whereas the timing of cortical responses is shaped by the other (Pacinian). Second, we show that input from these sensory channels is integrated in an optimal fashion that exploits the disparate response behaviors of different fiber types.DOI: http://dx.doi.org/10.7554/eLife.10450.001

Simulating tactile signals from the whole hand with millisecond precision

“…ref. 57). Also, the model will be valuable in neuroprosthetic applications that aim to restore the sense of touch through peripheral nerve interfaces (58)(59)(60).…”

“…We fit the parameters of these models on electrophysiological recordings obtained previously (18). These models were similar to earlier ones (57,70,71), with some key differences. First, rather than simply using skin indentation depth (along with derivatives) as IF model input, we used the static and dynamic pressure as calculated from the skin mechanics model.…”

Neural Basis of Touch and Proprioception in Primate Cortex

“…The presence of both features in the majority of APC neurons strongly suggests that convergent input is the rule rather than the exception. Furthermore, the time varying firing rates of APC neurons to simple and complex vibrations are better predicted from the combined responses of multiple afferent classes than from those of a single class (322). These observations indicate that touch does not comprise distinct sensory channels, each serving a different function, but rather relies on the integration of sensory signals across tactile submodalities to extract behaviorally relevant information during manual interactions with objects (321).…”

“…Moreover, the frequency profile of APC neurons is shaped by how this input is processed. Indeed, APC responses to vibrations—particularly those of neurons in area 3b—can be well approximated as a linear function of the afferent input passed through a temporal filter and these filters vary from neuron to neuron (322). Interestingly, filters tend to differ systematically depending on the input modality: RA input tends to be excitatory whereas PC input tends to be balanced or inhibitory.…”

Decision making under uncertainty in a spiking neural network model of the basal ganglia

“…Yet, if we accept as true that the behavior of a system emerges from the interactions of its fundamental processing units, the model has sooner or later to be downscaled to this level to provide a more faithful and reliable description. This point has been very often discussed in the past and is still a matter of debate [Brette, 2015] but it seems that individual or relative spike-timing plays a major role in neural information encoding in different ways [Portelli et al, 2016, Jacobs et al, 2009, Saal et al, 2015, Moyer et al, 2014. Models relying on individual spiking neurons and of which construction is constrained by anatomical and physiological evidence of complex connectivity architectures often provide fruitful results because they directly link a detailed circuitry to its assumed function or behavior [Chersi et al, 2013, Mandali et al, 2015, Medina et al, 2001, Yang et al, 2015.…”