RESUMEN
Nearby retinal ganglion cells often fire action potentials in near synchrony. We have investigated the circuit mechanisms that underlie these correlations by recording simultaneously from many ganglion cells in the salamander retina. During spontaneous activity in darkness, three types of correlations were distinguished: broad (firing synchrony within 40-100 ms), medium (10-50 ms), and narrow (<1 ms). When chemical synaptic transmission was blocked, the broad correlations disappeared, but the medium and narrow correlations persisted. Further analysis of the strength and time course of synchronous firing suggests that nearby ganglion cells share inputs from photoreceptors conveyed through interneurons via chemical synapses (broad correlations), share excitation from amacrine cells via electrical junctions (medium), and excite each other via electrical junctions (narrow). It appears that the firing patterns in the optic nerve are strongly shaped by electrical coupling in the inner retina.
Asunto(s)
Comunicación Celular/fisiología , Células Ganglionares de la Retina/fisiología , Potenciales de Acción/fisiología , Ambystoma , Animales , Adaptación a la Oscuridad/fisiología , Conductividad Eléctrica , Células Fotorreceptoras/citología , Células Fotorreceptoras/fisiología , Células Ganglionares de la Retina/citología , Transmisión Sináptica/fisiologíaRESUMEN
Decoding visual information from a population of retinal ganglion cells. J. Neurophysiol. 78: 2336-2350, 1997. This work investigates how a time-dependent visual stimulus is encoded by the collective activity of many retinal ganglion cells. Multiple ganglion cell spike trains were recorded simultaneously from the isolated retina of the tiger salamander using a multielectrode array. The stimulus consisted of photopic, spatially uniform, temporally broadband flicker. From the recorded spike trains, an estimate was obtained of the stimulus intensity as a function of time. This was compared with the actual stimulus to assess the quality and quantity of visual information conveyed by the ganglion cell population. Two algorithms were used to decode the spike trains: an optimized linear filter in which each action potential made an additive contribution to the stimulus estimate and an artificial neural network trained by back-propagation to match spike trains with stimuli. The two methods performed indistinguishably, suggesting that most of the information about this stimulus can be extracted by linear operations on the spike trains. Individual ganglion cells conveyed information at a rate of 3.2 +/- 1.7 bits/s (mean +/- SD), with an average information content per spike of 1.6 bits. The maximal possible rate of information transmission compatible with the measured spiking statistics was 13.9 +/- 6.3 bits/s. On average, ganglion cells used 22% of this capacity to encode visual information. When a decoder received two spike trains of the same response type, the reconstruction improved only marginally over that obtained from a single cell. However, a decoder using an ON and an OFF cell extracted as much information as the sum of that obtained from each cell alone.Thus cells of opposite response type encode different and nonoverlapping features of the stimulus. As more spike trains were provided to the decoder, the total information rate rapidly saturated, with 79% of the maximal value obtained from a local cluster of just four neurons of different functional types. The decoding filter applied to a given neuron's spikes within such a multiunit decoder differed substantially from the filter applied to that same neuron in a single-unit decoder. This shows that the optimal interpretation of a ganglion cell's action potential depends strongly on the simultaneous activity of other nearby cells. The quality of the stimulus reconstruction varied greatly with frequency: flicker components below 1 Hz and above 10 Hz were reconstructed poorly, and the performance was optimal near 2.5 Hz. Further analysis suggests that temporal encoding by ganglion cell spike trains is limited by slow phototransduction in the cone photoreceptors and a corrupting noise source proximal to the cones.
Asunto(s)
Células Ganglionares de la Retina/fisiología , Visión Ocular/fisiología , Percepción Visual/fisiología , Potenciales de Acción , Algoritmos , Ambystoma , Animales , Simulación por Computador , Entropía , Técnicas In Vitro , Larva , Modelos Neurológicos , Células Ganglionares de la Retina/citologíaRESUMEN
Assessing the reliability of neuronal spike trains is fundamental to an understanding of the neural code. We measured the reproducibility of retinal responses to repeated visual stimuli. In both tiger salamander and rabbit, the retinal ganglion cells responded to random flicker with discrete, brief periods of firing. For any given cell, these firing events covered only a small fraction of the total stimulus time, often less than 5%. Firing events were very reproducible from trial to trial: the timing jitter of individual spikes was as low as 1 msec, and the standard deviation in spike count was often less than 0.5 spikes. Comparing the precision of spike timing to that of the spike count showed that the timing of a firing event conveyed several times more visual information than its spike count. This sparseness and precision were general characteristics of ganglion cell responses, maintained over the broad ensemble of stimulus waveforms produced by random flicker, and over a range of contrasts. Thus, the responses of retinal ganglion cells are not properly described by a firing probability that varies continuously with the stimulus. Instead, these neurons elicit discrete firing events that may be the fundamental coding symbols in retinal spike trains.
Asunto(s)
Retina/fisiología , Células Ganglionares de la Retina/fisiología , Potenciales de Acción , Ambystoma , Animales , Electrofisiología/métodos , Técnicas In Vitro , Larva , Estimulación Luminosa , Conejos , Reproducibilidad de los Resultados , Factores de TiempoRESUMEN
Owing to the limited dynamic range of a neuron's output, neural circuits are faced with a trade-off between encoding the full range of their inputs and resolving gradations among those inputs. For example, the ambient light level varies daily over more than nine orders of magnitude, whereas the firing rate of optic nerve fibres spans less than two. This discrepancy is alleviated by light adaptation: as the mean intensity increases, the retina becomes proportionately less sensitive. However, image statistics other than the mean intensity also vary drastically during routine visual processing. Theory predicts that an efficient visual encoder should adapt its strategy not only to the mean, but to the full shape of the intensity distribution. Here we report that retinal ganglion cells, the output neurons of the retina, adapt to both image contrast-the range of light intensities-and to spatial correlations within the scene, even at constant mean intensity. The adaptation occurs on a scale of seconds, one hundred times more slowly than the immediate light response, and involves 2-5-fold changes in the firing rate. It is mediated within the retinal network: two independent sites of modulation after the photoreceptor cells appear to be involved. Our results demonstrate a remarkable plasticity in retinal processing that may contribute to the contrast adaptation of human vision.
Asunto(s)
Adaptación Fisiológica , Sensibilidad de Contraste/fisiología , Retina/fisiología , Percepción Espacial/fisiología , Potenciales de Acción , Ambystoma , Animales , Técnicas In Vitro , Modelos Neurológicos , Plasticidad Neuronal , Estimulación Luminosa , Conejos , Tiempo de Reacción , Células Ganglionares de la Retina/fisiologíaRESUMEN
Traditional approaches to neural coding characterize the encoding of known stimuli in average neural responses. Organisms face nearly the opposite task--extracting information about an unknown time-dependent stimulus from short segments of a spike train. Here the neural code was characterized from the point of view of the organism, culminating in algorithms for real-time stimulus estimation based on a single example of the spike train. These methods were applied to an identified movement-sensitive neuron in the fly visual system. Such decoding experiments determined the effective noise level and fault tolerance of neural computation, and the structure of the decoding algorithms suggested a simple model for real-time analog signal processing with spiking neurons.