Adaptation without parameter change: Dynamic gain control in motion detection

Abstract

Many sensory systems adapt their input-output relationship to changes in the statistics of the ambient stimulus. Such adaptive behavior has been measured in a motion detection sensitive neuron of the fly visual system, H1. The rapid adaptation of the velocity response gain has been interpreted as evidence of optimal matching of the H1 response to the dynamic range of the stimulus, thereby maximizing its information transmission. Here, we show that correlation-type motion detectors, which are commonly thought to underlie fly motion vision, intrinsically possess adaptive properties. Increasing the amplitude of the velocity fluctuations leads to a decrease of the effective gain and the time constant of the velocity response without any change in the parameters of these detectors. The seemingly complex property of this adaptation turns out to be a straightforward consequence of the multidimensionality of the stimulus and the nonlinear nature of the system. insect ͉ model ͉ motion vision A daptation is a widespread phenomenon in biological systems. Generally it may be defined as a change in the sensitivity of the system to the current stimulus after a change in the statistics of the input signal, making the system better suited to cope with the present environment. In particular, the gain of the input-output relation of sensory systems is often modified, enabling them to convey information about the relevant stimulus parameters despite changes in the statistics of the sensory environment. Such adaptation has been observed in the well studied motion detection system that underlies fly motion vision (1-3). In the motion sensitive neuron, H1, the variance of a band-limited Gaussian velocity waveform affects the neuron's velocity response relationship. This response exhibits a rather steep slope around zero velocity for small velocity fluctuations, whereas for large velocity fluctuations this slope was found to be substantially reduced (4-6). Gain control in H1 has been interpreted as adaptive rescaling set to match the dynamic range of the response to that of the stimulus and used to maximize the system's information transmission. However, the mechanism underlying this adaptation has not been elucidated.Adaptation in sensory systems often occurs on a much slower time scale than the duration of the system's impulse response, indicating the presence of a special mechanism that slowly changes the system's response parameters integrating information about the stimulus history. H1 gain adaptation takes place on a surprisingly fast time scale: it occurs within 1 s after switching from one stimulus condition to another (5). Fast adaptation has also been observed in other neuronal sensory systems (7-9), not only in the response gain, but also in its time course (7). When the adaptation operates on the same time scale as the response itself, the separation between the mechanisms underlying the adaptation and those that give rise to the response becomes ambiguous, suggesting that the adaptation emerges naturally from the salient ...

The Consequences of Response Nonlinearities for Interpretation of Spectrotemporal Receptive Fields

“…Another (or additional) possibility is that stimulus-dependent changes in STRFs may arise not from changes in RF parameters but instead from a stationary nonlinearity in the response function (e.g., as suggested by Theunissen et al, 2000;Escabí and Schreiner, 2002;Valentine and Eggermont, 2004). [A similar point has been raised recently in studies of adaptation of motion detection in the fly visual system (Borst et al, 2005).] Adaptation is ubiquitous in the brain and surely plays an important role in Figure 9.…”

Adaptation of Velocity Encoding in Synaptically Coupled Neurons in the Fly Visual System

“…A fast alignment of the input-output gain of TCs with the SD of velocity fluctuations can be attributed to inherent properties of correlationbased motion detectors, which are thought to form the computational principle of fly motion detection (Borst et al, 2005). Because these inherent processes evolve very fast after a change in stimulus parameters, they will probably not contribute much to the rather slow adaptation phenomena observed with our stimulation paradigm (Figs.…”

“…Although several changes in response properties of fly TCs with ongoing visual motion stimulation have been described (Maddess and Laughlin, 1985;Brenner et al, 2000;Harris et al, 2000;Borst et al, 2005), comparatively little is known about the underlying cellular mechanisms of motion adaptation. A fast alignment of the input-output gain of TCs with the SD of velocity fluctuations can be attributed to inherent properties of correlationbased motion detectors, which are thought to form the computational principle of fly motion detection (Borst et al, 2005).…”

“…Similar to many neurons in other sensory systems, fly TCs decrease their response magnitude during sustained stimulation, along with more complex changes in response properties (Brenner et al, 2000;Fairhall et al, 2001). Time-evolved response changes have generally been called adaptation, although some of these changes result directly from the properties of the motion detection mechanism (Borst et al, 2005). These effects interact with adaptation processes that require activity-dependent changes in system parameters (Maddess and Laughlin, 1985;Harris et al, 2000).…”

Adaptive encoding of motion information in the fly visual system

“…Intriguingly, it has been shown that even more complex changes in response proper es are emergent proper es of the EMD. In par cular, both in recordings from TCs as well as in EMD models with fi xed parameters, it was observed that the slope of the neuronal input-output funcon changes when random velocity fl uctuaons of diff erent modula on depths are presented (Borst et al 2005). This phenomenon was termed "adapta on without parameter change" to diff eren ate it from "adapta on" in a strict sense, for which genuine changes in system parameters have to be present.…”