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Throughout different sensory systems, individual neurons integrate incoming signals over their receptive fields. The characteristics of this signal integration are crucial determinants for the neurons' functions. For ganglion cells in the vertebrate retina, receptive fields are characterized by the well-known center-surround structure and, although several studies have addressed spatial integration in the receptive field center, little is known about how visual signals are integrated in the surround. Therefore, we set out here to characterize signal integration and to identify relevant nonlinearities in the receptive field surround of ganglion cells in the isolated salamander retina by recording spiking activity with extracellular electrodes under visual stimulation of the center and surround. To quantify nonlinearities of spatial integration independently of subsequent nonlinearities of spike generation, we applied the technique of iso-response measurements as follows: using closed-loop experiments, we searched for different stimulus patterns in the surround that all reduced the center-evoked spiking activity by the same amount. The identified iso-response stimuli revealed strongly nonlinear spatial integration in the receptive field surrounds of all recorded cells. Furthermore, cell types that had been shown previously to have different nonlinearities in receptive field centers showed similar surround nonlinearities but differed systematically in the adaptive characteristics of the surround. Finally, we found that there is an optimal spatial scale of surround suppression; suppression was most effective when surround stimulation was organized into subregions of several hundred micrometers in diameter, indicating that the surround is composed of subunits that have strong center-surround organization themselves. [ABSTRACT FROM AUTHOR]

How size and shape of presynaptic active zones are regulated at the molecular level has remained elusive. Here we provide insight from studying rod photoreceptor ribbon-type active zones after disruption of CAST/ERC2, one of the cytomatrix of the active zone (CAZ) proteins. Rod photoreceptors were present in normal numbers, and the a-wave of the electroretinogram (ERG)--reflecting their physiological population response--was unchanged in CAST knock-out (CAST-/) mice. Using immunofluorescence and electron microscopy, we found that the size of the rod presynaptic active zones, their Ca2+ channel complement, and the extension of the outer plexiform layer were diminished. Moreover, we observed sprouting of horizontal and bipolar cells toward the outer nuclear layer indicating impaired rod transmitter release. However, rod synapses of CAST-/ mice, unlike in mouse mutants for the CAZ protein Bassoon, displayed anchored ribbons, normal vesicle densities, clustered Ca2+ channels, and essentially normal molecular organization. The reduction of the rod active zone size went along with diminished amplitudes of the b-wave in scotopic ERGs. Assuming, based on the otherwise intact synaptic structure, an unaltered function of the remaining release apparatus, we take our finding to suggest a scaling of release rate with the size of the active zone. Multielectrode-array recordings of retinal ganglion cells showed decreased contrast sensitivity. This was also observed by optometry, which, moreover, revealed reduced visual acuity. We conclude that CAST supports large active zone size and high rates of transmission at rod ribbon synapses, which are required for normal vision [ABSTRACT FROM AUTHOR]

Neurons in sensory systems are commonly characterized by their receptive fields. These are experimentally often obtained by reverse-correlation analyses, for example, by calculating the spike-triggered average. The reverse-correlation approach, however, generally assumes a fixed temporal relation between spike-generating stimulus features and measured spikes. Temporal jitter of spikes will therefore distort the estimated receptive fields. Here, a novel extension of widely used reverse-correlation techniques (spike-triggered average as well as spike-triggered covariance) is presented that allows accurate measurements of receptive fields even in the presence of considerable spike-time jitter. It is shown that the method correctly recovers the receptive fields from simulated spike trains. When applied to recordings from auditory receptor cells of locusts, a considerable sharpening of receptive fields as compared to standard spike-triggered averages is observed. In addition, the multiple filters that are obtained from a conventional spike-triggered covariance analysis of these data can be collapsed into a single component if spike jitter is accounted for. Finally, it is shown how further effects on spike timing, such as systematic shifts in spike latency, can be included in the approach. [ABSTRACT FROM AUTHOR]

Spike-frequency adaptation affects the response characteristics of many sensory neurons, and different biophysical processes contribute to this phenomenon. Many cellular mechanisms underlying adaptation are triggered by the spike output of the neuron in a feedback manner (e.g., specific potassium currents that are primarily activated by the spiking activity). In contrast, other components of adaptation may be caused by, in a feedforward way, the sensory or synaptic input, which the neuron receives. Examples include viscoelasticity of mechanoreceptors, transducer adaptation in hair cells, and short-term synaptic depression. For a functional characterization of spike-frequency adaptation, it is essential to understand the dependence of adaptation on the input and output of the neuron. Here, we demonstrate how an input-driven component of adaptation can be uncovered in vivo from recordings of spike trains in an insect auditory receptor neuron, even if the total adaptation is dominated by output-driven components. Our method is based on the identification of different inputs that yield the same output and sudden switches between these inputs. In particular, we determined for different sound frequencies those intensities that are required to yield a predefined steady-state firing rate of the neuron. We then found that switching between these sound frequencies causes transient deviations of the firing rate. These firing-rate deflections are evidence of input-driven adaptation and can be used to quantify how this adaptation component affects the neural activity. Based on previous knowledge of the processes in auditory transduction, we conclude that for the investigated auditory receptor neurons, this adaptation phenomenon is of mechanical origin. [ABSTRACT FROM AUTHOR]

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron.

How size and shape of presynaptic active zones are regulated at the molecular level has remained elusive. Here we provide insight from studying rod photoreceptor ribbon-type active zones after disruption of CAST/ERC2, one of the cytomatrix of the active zone (CAZ) proteins. Rod photoreceptors were present in normal numbers, and the a-wave of the electroretinogram (ERG)--reflecting their physiological population response--was unchanged in CAST knock-out (CAST(-/-)) mice. Using immunofluorescence and electron microscopy, we found that the size of the rod presynaptic active zones, their Ca(2+) channel complement, and the extension of the outer plexiform layer were diminished. Moreover, we observed sprouting of horizontal and bipolar cells toward the outer nuclear layer indicating impaired rod transmitter release. However, rod synapses of CAST(-/-) mice, unlike in mouse mutants for the CAZ protein Bassoon, displayed anchored ribbons, normal vesicle densities, clustered Ca(2+) channels, and essentially normal molecular organization. The reduction of the rod active zone size went along with diminished amplitudes of the b-wave in scotopic ERGs. Assuming, based on the otherwise intact synaptic structure, an unaltered function of the remaining release apparatus, we take our finding to suggest a scaling of release rate with the size of the active zone. Multielectrode-array recordings of retinal ganglion cells showed decreased contrast sensitivity. This was also observed by optometry, which, moreover, revealed reduced visual acuity. We conclude that CAST supports large active zone size and high rates of transmission at rod ribbon synapses, which are required for normal vision.

Sensory systems must translate incoming signals quickly and reliably so that an animal can act successfully in its environment. Even at the level of receptor neurons, however, functional aspects of the sensory encoding process are not yet fully understood. Specifically, this concerns the question how stimulus features and neural response characteristics lead to an efficient transmission of sensory information. To address this issue, we have recorded and analyzed spike trains from grasshopper auditory receptors, while systematically varying the stimulus statistics. The stimulus variations profoundly influenced the efficiency of neural encoding. This influence was largely attributable to the presence of specific stimulus features that triggered remarkably precise spikes whose trial-to-trial timing variability was as low as 0.15 ms--one order of magnitude shorter than typical stimulus time scales. Precise spikes decreased the noise entropy of the spike trains, thereby increasing the rate of information transmission. In contrast, the total spike train entropy, which quantifies the variety of different spike train patterns, hardly changed when stimulus conditions were altered, as long as the neural firing rate remained the same. This finding shows that stimulus distributions that were transmitted with high information rates did not invoke additional response patterns, but instead displayed exceptional temporal precision in their neural representation. The acoustic stimuli that led to the highest information rates and smallest spike-time jitter feature pronounced sound-pressure deflections lasting for 2-3 ms. These upstrokes are reminiscent of salient structures found in natural grasshopper communication signals, suggesting that precise spikes selectively encode particularly important aspects of the natural stimulus environment.