Slow Na + Inactivation and Variance Adaptation in Salamander Retinal Ganglion Cells

114

Retinal ganglion cells fire spikes to an appropriate contrast presented over their receptive field center. These center responses undergo dynamic changes in sensitivity depending on the ongoing level of contrast, a process known as "contrast gain control." Extracellular recordings suggested that gain control is driven by a single wide-field mechanism, extending across the center and beyond, that depends on inhibitory interneurons: amacrine cells. However, recordings in salamander suggested that the excitatory bipolar cells, which drive the center, may themselves show gain control independently of amacrine cell mechanisms. Here, we tested in mammalian ganglion cells whether amacrine cells are critical for gain control over the receptive field center. We made extracellular and whole-cell recordings of guinea pig Y-type cells in vitro and quantified the gain change between contrasts using a linear-nonlinear analysis. For spikes, tripling contrast reduced gain by ϳ40%. With spikes blocked, ganglion cells showed similar levels of gain control in membrane currents and voltages and under conditions of low and high calcium buffering: tripling contrast reduced gain by ϳ20 -25%. Gain control persisted under voltage-clamp conditions that minimize inhibitory conductances and pharmacological conditions that block inhibitory neurotransmitter receptors. Gain control depended on adequate stimulation, not of ganglion cells but of presynaptic bipolar cells. Furthermore, horizontal cell measurements showed a lack of gain control in photoreceptor synaptic release. Thus, the mechanism for gain control over the ganglion cell receptive field center, as measured in the subthreshold response, originates in the presynaptic bipolar cells and does not require amacrine cell signaling.

Section: Two Spatial Mechanisms For Contrast Gain Control In Ganglionmentioning

confidence: 94%

Cellular Basis for Contrast Gain Control over the Receptive Field Center of Mammalian Retinal Ganglion Cells

Beaudoin¹,

Borghuis²,

Demb³

2007

114

“…Experiments have shown that slow inactivation of sodium channels induces slow modulations in the responsiveness of neurons to inputs (Fleidervish et al, 1996;Kim and Rieke, 2003). In general, excitability depends on many variables, for example, the availability of many different ion channel types or the level of calcium.…”

Section: Defining the Neuronal Excitability Variablementioning

confidence: 99%

“…Several underlying molecular mechanisms support these modulations in an activity-dependent manner (LeMasson et al, 1993;van Ooyen, 1994;Marom, 1998;Wang, 1998;Carr et al, 2003). Such mechanisms are usually understood to operate by adding uniquely defined slow time scales (Marom and Abbot, 1994;Fleidervish et al, 1996;Kim and Rieke, 2003) and cannot capture multiple-time-scale dynamics.…”

Section: Introductionmentioning

confidence: 99%

History-Dependent Multiple-Time-Scale Dynamics in a Single-Neuron Model

Gilboa¹,

Chen²,

Brenner³

2005

History-dependent characteristic time scales in dynamics have been observed at several levels of organization in neural systems. Such dynamics can provide powerful means for computation and memory. At the level of the single neuron, several microscopic mechanisms, including ion channel kinetics, can support multiple-time-scale dynamics. How the temporally complex channel kinetics gives rise to dynamical properties of the neuron is not well understood. Here, we construct a model that captures some features of the connection between these two levels of organization. The model neuron exhibits history-dependent multiple-time-scale dynamics in several effects: first, after stimulation, the recovery time scale is related to the stimulation duration by a power-law scaling; second, temporal patterns of neural activity in response to ongoing stimulation are modulated over time; finally, the characteristic time scale for adaptation after a step change in stimulus depends on the duration of the preceding stimulus. All these effects have been observed experimentally and are not explained by current single-neuron models. The model neuron here presented is composed of an ensemble of ion channels that can wander in a large pool of degenerate inactive states and thus exhibits multiple-time-scale dynamics at the molecular level. Channel inactivation rate depends on recent neural activity, which in turn depends through modulations of the neural response function on the fraction of active channels. This construction produces a model that robustly exhibits nonexponential history-dependent dynamics, in qualitative agreement with experimental results.

“…Adaptation induced by slow intrinsic ionic currents of the spike generator is, however, also commonly observed in neurons and may enhance their response to highfrequency input French et al, 2001), mask low-intensity stimuli (Sobel and Tank, 1994;Wang, 1998), induce contrast adaptation (Sanchez-Vives et al, 2000), remove temporal correlations from the input (Wang et al, 2003), or affect network synchrony and rhythms (Crook et al, 1998;Ermentrout et al, 2001;van Vreeswijk and Hansel, 2001;Fuhrmann et al, 2002). Because adaptation usually operates within complex neural circuits and on many different time scales (Fairhall et al, 2001;Baccus and Meister, 2002;Kohn and Whitsel, 2002), it has been difficult to determine how these different cellular mechanisms contribute to network-level computations [e.g., sensory adaptation (Chung et al, 2002;Castro-Alamancos, 2004) or contrast adaptation (Sanchez-Vives et al, 2000;Fairhall et al, 2001;Kim and Rieke, 2003) in the visual system]. There are few studies on the functional role of adaptation or synaptic depression within a behavioral context (Sobel and Tank, 1994;Cook et al, 2003;Luksch et al, 2004;Ronacher and Hennig, 2004).…”

Section: Introductionmentioning

confidence: 99%

Spike-Frequency Adaptation Separates Transient Communication Signals from Background Oscillations

Benda¹,

Longtin²,

Maler³

2005

188

223

Spike-frequency adaptation is a prominent feature of many neurons. However, little is known about its computational role in processing behaviorally relevant natural stimuli beyond filtering out slow changes in stimulus intensity. Here, we present a more complex example in which we demonstrate how spike-frequency adaptation plays a key role in separating transient signals from slower oscillatory signals. We recorded in vivo from very rapidly adapting electroreceptor afferents of the weakly electric fish Apteronotus leptorhynchus. The firing-frequency response of electroreceptors to fast communication stimuli ("small chirps") is strongly enhanced compared with the response to slower oscillations ("beats") arising from interactions of same-sex conspecifics. We are able to accurately predict the electroreceptor afferent response to chirps and beats, using a recently proposed general model for spike-frequency adaptation. The parameters of the model are determined for each neuron individually from the responses to step stimuli. We conclude that the dynamics of the rapid spike-frequency adaptation is sufficient to explain the data. Analysis of additional data from step responses demonstrates that spike-frequency adaptation acts subtractively rather than divisively as expected from depressing synapses. Therefore, the adaptation dynamics is linear and creates a high-pass filter with a cutoff frequency of 23 Hz that separates fast signals from slower changes in input. A similar critical frequency is seen in behavioral data on the probability of a fish emitting chirps as a function of beat frequency. These results demonstrate how spike-frequency adaptation in general can facilitate extraction of signals of different time scales, specifically high-frequency signals embedded in slower oscillations.