The classic model of saccade generation assumes that the burst generator is driven by a motor-error signal, the difference between the actual eye position and the final 'desired' eye position in the orbit. Here we evaluate objectively, using system identification techniques, the dynamic relationship between motor-error signals and palmate inhibitory burst neuron (IBN) discharges (upstream analysis). The IBNs presented here are the same neurons whose downstream relationships were characterized during head-fixed saccades and head-free gaze shifts in our companion papers. In our analysis of head- fixed saccades we determined bow well IBN discharges encode eye motor error (ε(e)) compared with downstream saccadic eye movement dynamics and whether long-lead IBN (LLIBN) discharges encode ε(e) better than short-lead IBNs (SLIBNs), given that it is commonly assumed that short-lead burst neurons (BNs) are closer than long-lead BNs to the motor output and thus further from the ε(e) signal. In the ε(e)-based models tested, IBN firing frequency B(t) was represented by one of the following: 1) model 1u, a nonlinear function of ε(e); 2) model 2u, a linear function of ε(e) [B(t) = r(k) + a1ε0(t)] where the bias term r(k) was estimated separately for each saccade; 3) model 3u, a version of model 2u wherein the bias term was a function of saccade amplitude; or 4) model 4u, a linear function of ε(e) with an added pole term (the derivative of firing rate). Models based on ε(e) consistently produced worse predictions of IBN activity than models of comparable complexity based on eye movement dynamics (e.g., eye velocity). Hence, the simple two parameter downstream model 2d [B(t) = r + b1Ė(t)] was much better than any upstream (ε(e)-based) model with a comparable number of parameters. The link between B(t) and ε(e) is due primarily to the correlation between the declining phases of B(t) and ε(e); motor-error models did not predict well the rising phase of the discharge. We could improve substantially the performance of upstream models by adding an ε̇(e) term. Because ε̇(e) = - Ė, this process was equivalent to incorporating Ė terms into upstream models thereby erasing the distinction between upstream and downstream analyses. Adding an ε̇(e) term to the upstream models made them as good as downstream ones in predicting B(t). However, the ε(e) term now became redundant because its removal did not affect model accuracy. Thus, when Ė is available as a parameter, ε(e) becomes irrelevant. In the head-free monkey the ability of upstream models to predict IBN firing during head-free gaze shirks when gaze, eye, or head motor-error signals were model inputs was poor and similar to the upstream analysis of the head-fixed condition. We conclude that during saccades (head-fixed) or gaze shifts (head-free) the activity of both SLIBNs and LLIBNs is more closely linked to downstream events (i.e., the dynamics of ongoing movements) than to the coincident upstream motor-error signal. Furthermore, SLIBNs and LLIBNs do not differ in their characteristics; the latter are not, as is usually hypothesized, closer to a motor-error signal than the former.
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