MODULACIÓN CRUZADA DE FASE (XPM, CROSS PHASE

In the previous chapter we reviewed evidence o f the adaptation to motion o f wide-field, direction-selective neurons in the fly lobula plate (M addess & Laughlin, 1985; de Ruyter van Steveninck et al, 1986; Borst & Egelhaaf, 1987). A daptation o f m otion-sensitive neurons over the course o f a few seconds has also been reported in the optic lobes o f the butterfly (M addess et al, 1991), in area 17 o f the cat visual cortex (Vautin and Berkley, 1977; H am m ond et al. 1988; M addess et al, 1988; Giaschi et al, 1993), and in the nucleus o f the optic tract (NOT) o f the wallaby (Ibbotson & M ark, 1996). Here, physiological data on m otion adaptation is recorded from cells found in the N O T o f adult wallabies. M acropus

eugenii (m ethods as in Ibbotson et al, 1994). N eurons in the N OT provide the signals that

control the slow phases o f horizontal optokinetic nystagm us (e.g. Collewijn, 1975a,b; H offm ann & Schoppm ann, 1981; Schiff et al, 1988; Simpson, 1984; Soodak & Sim pson,

1988). They are directional and respond preferentially to wide-field stimulation.

M arked sim ilarities are revealed between the adaptation to motion observed in wide-field directional neurons found in the mammalian nucleus o f the optic tract and cells in the insect lobula plate. It is found here that the velocity im pulse response (Srinivasan 1983) in wallaby N O T neurons depends on stimulus history within local regions o f the neuron's receptive field, as with cells in the fly lobula plate (M addess & Laughlin, 1985; de Ruyter van Steveninck et al, 1986). The corresponding tem poral frequency response functions are found to be shifted laterally and com pressed by m otion adaptation. The lateral shift serves to enhance dynam ic range and differential motion sensitivity. It will be argued that the

com pression is not caused by fatigue but is an intrinsic property o f the adaptive process, resulting from the interdependence o f temporal frequency tuning and gain in the tem poral filters o f the m otion detectors. However, while the form and time scale o f adaptation is com parable in the tw o systems, there is a difference in the directional properties o f the effect

in that w allaby N O T cells appear to adapt much less strongly to anti-preferred m otion and flicker than do cells in the fly lobula plate (Borst & Egelhaaf, 1987).

The response to m otion o f both fly lobula plate neurons (Egelhaaf et al, 1989) and cells in the N O T o f the w allaby (Ibbotson et al, 1994) are consistent with the com putation o f m otion by a correlation-based R eichardt detector (Reichardt, 1961). In chapter 2, a model based on the R eichardt detector was proposed to describe m otion adaptation in the insect visual system . In this chapter, the model is reform ulated to account for the key features o f m otion adaptation in cells found in the mammalian NOT and the insect lobula plate, with only minor m odifications required to account for the observed differences in directionality between the two phyla.

M odel

In chapter 2, the increase in both temporal resolution and sensitivity to velocity change of neurons in the fly lobula plate following motion adaptation were m odelled by adjusting the delay filters in an array o f Reichardt detectors. H owever, there were three points on w hich the previous m odel's perform ance was not satisfactory: (a) only adaptive steady-state behaviour was accounted for, not transient responses; (b) data on directionality and flicker sensitivity (Borst & Egelhaaf, 1987) were not predicted; and (c) a degree o f spatial

integration o f m otion detector responses was required prior to obtaining the signal upon w hich adaptation was based, while experimental evidence shows that such integration could only operate over a very limited range (M addess & Laughlin, 1985; de Ruyter van

Steveninck et al, 1986).

transients from the m odel’s response and focus on its adaptive behaviour (Fig. 3.1a). The lim itations o f the m odel proposed in chapter 2 are largely due to the idealised nature o f the detector im plem ented. E gelhaaf et al (1989) described a version o f the R eichardt detector w hich, while not adaptive, accurately described other aspects o f lobula plate physiology. H ere we extend the adaptive Reichardt detector m odel to incorporate the realistic m otion detector characteristics described by Egelhaaf et al (1989), and to predict the spatially localised nature o f adaptation. Three significant modifications to the m odel are made which im prove its predictive pow er and increase its scope to account for data from neurons found in the w allaby N O T as well as the fly lobula plate. W hile the revised m odel can be used to predict the m ajority o f effects observed in both species, it was necessary to introduce slight differences in the form o f the model to account for observed differences in the directionality and flicker-sensitivity o f adaptation. These differences are detailed later in the context of results on the directionality o f adaptation. The m odifications to the m odel are;

1. First-order low -pass temporal filters (A ppendix 3.1) are used in the detectors' input channels (Reichardt, 1961; Egelhaaf et al, 1989). The use o f low -pass filters (Fig. 3.1b) enables transient responses to be modelled. In chapter 2 pure delay filters were used (Fig. 3.1a) and, as a consequence,*only adapting steady-state behaviour was shown. In order to capture fully the dynam ic nature o f the adaptive process, a recursive im plem entation of the temporal filters is now em ployed (A ppendix 3.2). 2. The directionally-opponent subtraction stage is not perfectly balanced (Fig. 3.1b).

The subunits in a Reichardt detector do not respond exclusively to m otion signals, they also give some motion-independent responses (Egelhaaf et al. 1989). If the subtraction stage is balanced, these m otion-independent responses are removed. H ow ever, w hen the subtraction stage is unbalanced, some m otion-independent signals are transmitted. As a result, a flickering stimulus induces a response in the

a

Local Spatial Integration

Spatial Integration