Variability in somatosensory cortical neuron discharge: effects on capacity to signal different stimulus conditions using a mean rate code

Abstract

1. The present study is based on the demonstration (8, 9) that the relationship between mean interval (MI) and standard deviation (SD) for stimulus-driven activity recorded from SI neurons is well fitted by the linear equation SD = a X MI + b and on the observations that the values of the slope (a) and y intercept (b) parameters of this relationship are independent of stimulus conditions and may vary widely from one neuron to the next (8). 2. A criterion for the discriminability of two different mean firing rates requiring that the mean intervals of their respective interspike interval (ISI) distributions be separated by a fixed interval (expressed in SD units) is developed and, on the basis of this criterion, a graphical display of the capacity of a neuron with a known SD-MI relationship to reflect a change in stimulus conditions with a change in mean firing rate is derived. Using this graphical approach, it is shown that the parameters of the SD-MI relationship for a single neuron determine a range of firing frequencies, within which that neuron exhibits the greatest capacity to signal differences in stimulus conditions using a frequency code. 3. The discrimination criterion is modified to incorporate the changes in the symmetry of the ISI distribution observed to accompany changes in mean firing rate. It is shown that, although the observed symmetry changes do influence the capacity of a cortical neuron to signal a change in stimulus conditions with a change in mean firing rate, they do not alter the range of firing rates (determined by the parameters of the SD-MI relationship) within which the capacity for discrimination is maximal. 4. The maximal number of firing levels that can be distinguished by a somatosensory cortical neuron (using the same discrimination criterion described above) discharging within a specified range of mean frequencies also is demonstrated to depend on the parameters of the linear equation which relates SD to MI. 5. Two approaches based on the t test for differences between two means are developed in an attempt to ascertain the minimum separation of the mean intervals of the ISI distributions necessary for two different mean firing rates to be discriminated with 80% certainty.

Different Pedunculopontine Tegmental Neurons Signal Predicted and Actual Task Rewards

“…The precision for neuronal activity to signal the reward magnitude was analyzed in four ways: (1) by evaluating the significance of the difference between the mean spike rates for large and small rewards ( p Ͻ 0.05, t test); (2) by receiver operating characteristic (ROC) analysis (significance level of p Ͻ 0.05) (Lusted, 1978) for discrimination between the small and large rewards; (3) by mutual information analysis to estimate the information contained in the spike discharges with respect to the magnitude of the reward (Werner and Mountcastle, 1963;Schreiner et al, 1978;Kitazawa et al, 1998);and (4) by regression analysis of the event parameters contributing to neuronal activities (shown below). The second and third analyses were conducted using a sliding time window of 200 ms moved in 1 ms steps.…”

“…Therefore, an ROC value of 0.5 and Ͼ0.56 imply that the answer is 50 and 95% correct, respectively. Second, the information capacity for the PPTN neuronal ensemble to signal reward magnitude during the three task periods was estimated via mutual information analysis (Werner and Mountcastle, 1963;Schreiner et al, 1978;Kitazawa et al, 1998): After fixation on the FT for 400 -1000 ms, the FT disappeared aftera0or200mstime gap and the ST was presented for 400 -600 ms. Monkeys were required to make a saccade to the ST within 500 ms after the ST onset. Rewards for successful trials (RD) were delivered 100 ms after the ST offset.…”

Effects of Nonuniform Fiber Sensitivity, Innervation Geometry, and Noise on Information Relayed by a Population of Slowly Adapting Type I Primary Afferents from the Fingerpad

“…For example, in Figure 2 of Whitsel et al (1978), the response to a stimulus repeated 25 times had a mean Ϯ SD of 39.1 Ϯ 8.8 impulses. The detailed variability analysis of Whitsel et al (1977) and Schreiner et al (1978) does not address the variability of responses to repeated stimuli, but rather the "variability" or distribution of the interspike intervals for a single stimulus.…”

Interaction of Visual and Proprioceptive Feedback During Adaptation of Human Reaching Movements

“…However, intrinsic differences in sensor filter characteristics and coordinate reference frames give rise to discrepancies in the information provided to the brain by the different senses, leading to biases and errors in the estimation of limb configuration. Discrepancies may also arise due to variability in sensory transduction and neural encoding processes (i.e., "sensor noise") (Schreiner et al 1978; van Beers et al 2002;Whitsel et al 1977; see also Cordo et al 1994) or as a result of neural approximations to the complex nonlinear computations required to map joint angles to fingertip position or vice versa (cf. Flanders et al 1992;Ghez et al 1999).…”