Research reportHow is tactile information affected by parameters of the population such as non-uniform fiber sensitivity, innervation geometry and response variability?
Introduction
Defining the response properties of isolated single neurons is only the first step in understanding how such neurons process information. The next step, which is essential, is an examination of the characteristics of groups or populations of neurons.
Many years ago, Mountcastle and others introduced the concept of ‘reciprocal interpretation’ in the somatosensory system. The essential principle is that if the responses of a single cutaneous neuron are plotted over the receptive field, then the resulting receptive field profile can also be viewed as the idealized population response for the neurons from that region of skin. This is still the most commonly used approach to population reconstructions. For primary afferent fibers, in many cases such ideal population responses are more or less isomorphic representations of the stimulus.
For example, when a sphere contacts the fingerpad, the receptive field profiles of the SAIs and hence, by ‘reciprocal interpretation’, the ideal population response (Fig. 1) reflects the shape, position and contact force of the stimulus [7]. A cylinder contacting the finger results in a cylinder-like response in the SAI population, which changes its orientation as the orientation of the cylinder changes [5]. LaMotte and colleagues [12], [13] have shown near isomorphic population responses for ellipsoids and wavy surfaces scanned over the skin, and Johnson and colleagues [15], [1] have shown isomorphic representations of letters and patterns of raised dots scanned over the finger. Isomorphic representations have also been demonstrated in the somatosensory cortex [4].
But real primary afferent fiber population responses differ substantially from these idealized images in at least three ways. Firstly, not all afferents are identical; in particular, sensitivities vary widely among fibers of the same type so that the images are distorted [11]. Secondly, noise of some form or another produces random fluctuations in the response patterns [8]. Thirdly, the innervation density is not infinite so that such images are not continuous but are sampled, in fact fairly sparsely and perhaps non-uniformly [9]. The question that we wish to address is: what impact do these factors have on the population response and on its ability to represent or encode the various features of the stimulus?
Section snippets
Population reconstruction
In general it is not possible to record simultaneously from all of, or even most of, the responding neurons so that the only way of obtaining a population response is by modeling. Here, we model the responses to a sphere applied passively to the fingerpad; the parameters or features of the stimulus that we vary systematically are its curvature, its position on the skin and the contact force.
To determine the underlying single unit responses, we recorded from primary afferent fibers innervating
Impact of population variables
Our psychophysics experiments have shown that humans can extract the three parameters of the stimulus independently. That is, if you randomly vary curvature, position and force, subjects can independently scale each of the parameters. Moreover, their performance is impressive; for example, the difference limen for position is less than half a millimeter and the Weber fraction for curvature is about 10%.
The solid lines in Fig. 2 show the measures for position and curvature for an ideal
Acknowledgements
This work was supported by grants from the National Health and Medical Council of Australia.
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