E; Wong et al., 1980). This information, which contains the bump latency distribution and possible dynamic nonlinearities in light adaptation, may be extracted by calculating the L-Cysteine site photoreceptor frequency response, T V ( f ), and coherence, 2( f ), functions at diverse mean light intensity levels. The achieve part of the frequency response function, GV (f ) (Fig. six A), resembles the corresponding 2-Phenylacetamide Epigenetics signal energy spectrum (Fig. five A) in the same adapting background, indicating that the photoreceptor is operating linearly. As the photoreceptor signal shows increased13 Juusola and Hardiecontrast obtain and broadened bandwidth with escalating imply light intensity, its 3-dB cut-off frequency (the point at which the acquire falls to half of the maximum) shifts towards larger frequencies (Fig. six B) saturating on average 25 Hz at the brightest adapting background. The corresponding phase, PV ( f ) (Fig. six C), shows that the voltage signal lags the stimulus less because the mean light intensity increases. Moreover, by comparing P V ( f ) towards the minimum phase, Pmin( f ) (Fig. 6 C), derived from the gain part of the frequency response function, it becomes apparent that the photoreceptor voltage signals include a pure time delay. This pure time delay, i.e., dead-time (Fig. 6 D), depends upon the imply light intensity. It’s largest ( 25 ms) at the dimmest adapting background of BG-4 and exponentially reduces to ten ms at BG0. Similar adaptive dead-times have already been observed in Calliphora photoreceptors (Juusola et al., 1994; de Ruyter van Steveninck and Laughlin, 1996b), but with twice as rapid dynamics as within the Drosophila eye. 2 The coherence function, exp ( f ) (Fig. six E), an index in the system’s linearity, is close to unity over the frequency variety at BG0, indicating that the photoreceptor signals are around linear below these circumstances. The low coherence values at low mean intensity levels are largely a result with the noisiness of your signal estimates when the rate of photon absorptions is low, considering that the coherence improves with enhanced averaging or picking more sensitive photoreceptors. Nonetheless, since the photoreceptor signal bandwidth is narrow at low adapting backgrounds, the coherence values are already near zero at fairly low stimulus frequencies. The higher degree of linearity at vibrant illumination, as observed within the coherence, indicates that the skewed distribution of your signals causes a compact nonlinear impact on the signal amplification in the course of dynamic stimulation. A comparable behavior has been encountered inside the blowfly (Calliphora) photoreceptors (Juusola et al., 1994). There, it was later shown that adding a nonlinearity (secondorder kernel or static polynomial element) into a dynamic linear photoreceptor model (linear impulse response) causes no real improvement as judged by the imply square error (Juusola et al., 1995). When a photoreceptor operates as a linear system, one particular can calculate the coherence function from the SNRV( f ). As shown above (Fig. four), at low adapting backgrounds, the photoreceptor voltage responses are smaller and noisy. Accordingly their linear coherence esti2 mates, SNR ( f ) (Fig. 6 F), are considerably lower than two the coherence, exp ( f ) (Fig. six E), calculated in the signal (i.e., the averaged voltage response). In the brightest adapting backgrounds, the photoreceptor voltage responses are very reproducible, getting considerably decreased noise content. The discrepancy among the two independent coherence estim.