Complex Spike-Event Pattern of Transienton-offRetinal Ganglion Cells

The mechanism of human color vision and potential implanted devices for artificial color vision

Vision plays a major role in perceiving external stimuli and information in our daily lives. The neural mechanism of color vision is complicated, involving the co-ordinated functions of a variety of cells, such as retinal cells and lateral geniculate nucleus cells, as well as multiple levels of the visual cortex. In this work, we reviewed the history of experimental and theoretical studies on this issue, from the fundamental functions of the individual cells of the visual system to the coding in the transmission of neural signals and sophisticated brain processes at different levels. We discuss various hypotheses, models, and theories related to the color vision mechanism and present some suggestions for developing novel implanted devices that may help restore color vision in visually impaired people or introduce artificial color vision to those who need it.

Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding

Synchronization of neuronal discharges on the millisecond scale has long been recognized as a prevalent and functionally important attribute of neural activity. In this article, I review classical concepts and corresponding evidence of the mechanisms that govern the synchronization of distributed discharges in cortical networks and relate those mechanisms to their possible roles in coding and cognitive functions. To accommodate the need for a selective, directed synchronization of cells, I propose that synchronous firing of distributed neurons is a natural consequence of spike-timing-dependent plasticity (STDP) that associates cells repetitively receiving temporally coherent input: the “synchrony through synaptic plasticity” hypothesis. Neurons that are excited by a repeated sequence of synaptic inputs may learn to selectively respond to the onset of this sequence through synaptic plasticity. Multiple neurons receiving coherent input could thus actively synchronize their firing by learning to selectively respond at corresponding temporal positions. The hypothesis makes several predictions: first, the position of the cells in the network, as well as the source of their input signals, would be irrelevant as long as their input signals arrive simultaneously; second, repeating discharge patterns should get compressed until all or some part of the signals are synchronized; and third, this compression should be accompanied by a sparsening of signals. In this way, selective groups of cells could emerge that would respond to some recurring event with synchronous firing. Such a learned response pattern could further be modulated by synchronous network oscillations that provide a dynamic, flexible context for the synaptic integration of distributed signals. I conclude by suggesting experimental approaches to further test this new hypothesis.

A single retinal circuit model for multiple computations

Vision is dependent on extracting intricate features of the visual information from the outside world, and complex visual computations begin to take place as soon as at the retinal level. In multiple studies on salamander retinas, the responses of a subtype of retinal ganglion cells, i.e., fast/biphasic-OFF ganglion cells, have been shown to be able to realize multiple functions, such as the segregation of a moving object from its background, motion anticipation, and rapid encoding of the spatial features of a new visual scene. For each of these visual functions, modeling approaches using extended linear-nonlinear cascade models suggest specific preceding retinal circuitries merging onto fast/biphasic-OFF ganglion cells. However, whether multiple visual functions can be accommodated together in a certain retinal circuitry and how specific mechanisms for each visual function interact with each other have not been investigated. Here, we propose a physiologically consistent, detailed computational model of the retinal circuit based on the spatiotemporal dynamics and connections of each class of retinal neurons to implement object motion sensitivity, motion anticipation, and rapid coding in the same circuit. Simulations suggest that multiple computations can be accommodated together, thereby implying that the fast/biphasic-OFF ganglion cell has potential to output a train of spikes carrying multiple pieces of information on distinct features of the visual stimuli.

Coding Properties of Mouse Retinal Ganglion Cells with Dual-Peak Patterns with Respect to Stimulus Intervals

How visual information is encoded in spikes of retinal ganglion cells (RGCs) is essential in visual neuroscience. In the present study, we investigated the coding properties of mouse RGCs with dual-peak patterns with respect to visual stimulus intervals. We first analyzed the response properties, and observed that the latencies and spike counts of the two response peaks in the dual-peak pattern exhibited systematic changes with the preceding light-OFF interval. We then applied linear discriminant analysis (LDA) to assess the relative contributions of response characteristics of both peaks in information coding regarding the preceding stimulus interval. It was found that for each peak, the discrimination results were far better than chance level based on either latency or spike count, and were further improved by using the combination of the two parameters. Furthermore, the best discrimination results were obtained when latencies and spike counts of both peaks were considered in combination. In addition, the correct rate for stimulation discrimination was higher when RGC population activity was considered as compare to single neuron's activity, and the correct rate was increased with the group size. These results suggest that rate coding, temporal coding, and population coding are all involved in encoding the different stimulus-interval patterns, and the two response peaks in the dual-peak pattern carry complementary information about stimulus interval.

Single and Multiple Change Point Detection in Spike Trains: Comparison of Different CUSUM Methods

In a natural environment, sensory systems are faced with ever-changing stimuli that can occur, disappear or change their properties at any time. For the animal to react adequately the sensory systems must be able to detect changes in external stimuli based on its neuronal responses. Since the nervous system has no prior knowledge of the stimulus timing, changes in stimulus need to be inferred from the changes in neuronal activity, in particular increase or decrease of the spike rate, its variability, and shifted response latencies. From a mathematical point of view, this problem can be rephrased as detecting changes of statistical properties in a time series. In neuroscience, the CUSUM (cumulative sum) method has been applied to recorded neuronal responses for detecting a single stimulus change. Here, we investigate the applicability of the CUSUM approach for detecting single as well as multiple stimulus changes that induce increases or decreases in neuronal activity. Like the nervous system, our algorithm relies exclusively on previous neuronal population activities, without using knowledge about the timing or number of external stimulus changes. We apply our change point detection methods to experimental data obtained by multi-electrode recordings from turtle retinal ganglion cells, which react to changes in light stimulation with a range of typical neuronal activity patterns. We systematically examine how variations of mathematical assumptions (Poisson, Gaussian, and Gamma distributions) used for the algorithms may affect the detection of an unknown number of stimulus changes in our data and compare these CUSUM methods with the standard Rate Change method. Our results suggest which versions of the CUSUM algorithm could be useful for different types of specific data sets.

Rank Order Coding: a Retinal Information Decoding Strategy Revealed by Large-Scale Multielectrode Array Retinal Recordings

How a population of retinal ganglion cells (RGCs) encodes the visual scene remains an open question. Going beyond individual RGC coding strategies, results in salamander suggest that the relative latencies of a RGC pair encode spatial information. Thus, a population code based on this concerted spiking could be a powerful mechanism to transmit visual information rapidly and efficiently. Here, we tested this hypothesis in mouse by recording simultaneous light-evoked responses from hundreds of RGCs, at pan-retinal level, using a new generation of large-scale, high-density multielectrode array consisting of 4096 electrodes. Interestingly, we did not find any RGCs exhibiting a clear latency tuning to the stimuli, suggesting that in mouse, individual RGC pairs may not provide sufficient information. We show that a significant amount of information is encoded synergistically in the concerted spiking of large RGC populations. Thus, the RGC population response described with relative activities, or ranks, provides more relevant information than classical independent spike count- or latency- based codes. In particular, we report for the first time that when considering the relative activities across the whole population, the wave of first stimulus-evoked spikes is an accurate indicator of stimulus content. We show that this coding strategy coexists with classical neural codes, and that it is more efficient and faster. Overall, these novel observations suggest that already at the level of the retina, concerted spiking provides a reliable and fast strategy to rapidly transmit new visual scenes.

Temporal properties of dual-peak responses of mouse retinal ganglion cells and effects of inhibitory pathways

Dual-peak responses of retinal ganglion cells (RGCs) are observed in various species, previous researches suggested that both response peaks were involved in retinal information coding. In the present study, we investigated the temporal properties of the dual-peak responses recorded in mouse RGCs elicited by spatially homogeneous light flashes and the effect of the inhibitory inputs mediated by GABAergic and/or glycinergic pathways. We found that the two peaks in the dual-peak responses exhibited distinct temporal dynamics, similar to that of short-latency and long-latency single-peak responses respectively. Pharmacological studies demonstrated that the application of exogenous GABA or glycine greatly suppressed or even eliminated the second peak of the cells' firing activities, while little change was induced in the first peak. Co-application of glycine and GABA led to complete elimination of the second peak. Moreover, application of picrotoxin or strychnine induced dual-peak responses in some cells with transient responses by unmasking a second response phase. These results suggest that both GABAergic and glycinergic pathways are involved in the dual-peak responses of the mouse RGCs, and the two response peaks may arise from distinct pathways that would converge on the ganglion cells.

HFirst: A Temporal Approach to Object Recognition

This paper introduces a spiking hierarchical model for object recognition which utilizes the precise timing information inherently present in the output of biologically inspired asynchronous address event representation (AER) vision sensors. The asynchronous nature of these systems frees computation and communication from the rigid predetermined timing enforced by system clocks in conventional systems. Freedom from rigid timing constraints opens the possibility of using true timing to our advantage in computation. We show not only how timing can be used in object recognition, but also how it can in fact simplify computation. Specifically, we rely on a simple temporal-winner-take-all rather than more computationally intensive synchronous operations typically used in biologically inspired neural networks for object recognition. This approach to visual computation represents a major paradigm shift from conventional clocked systems and can find application in other sensory modalities and computational tasks. We showcase effectiveness of the approach by achieving the highest reported accuracy to date (97.5% ± 3.5%) for a previously published four class card pip recognition task and an accuracy of 84.9% ± 1.9% for a new more difficult 36 class character recognition task.

REGISTRO DE ACTIVIDAD ELÉCTRICA EN LA RETINA DE UNA RATA ALBINA EMPLEANDO UNA MATRIZ DE MICROELECTRODOS

<p>Las matrices de microelectrodos son dispositivos que permiten la detección de potenciales de acción o espigas en poblaciones de células excitables, ofreciendo varias aplicaciones en el campo de las neurociencias y la biología. Este trabajo muestra un protocolo para el registro de espigas en una población de células ganglionares retinales empleando una matriz de microelectrodos. La retina de una rata albina fue extraída y preparada para ser estimulada <em>in vitro </em>con luz led blanca, con el fin de registrar sus espigas evocadas ante estos estímulos. Cada microelectrodo puede registrar espigas de más de una célula ganglionar, razón por la cual se determinó a qué célula pertenece cada espiga aplicando un procedimiento conocido como “clasificación de espigas”. El trabajo permitió obtener el registro de un periodo de estimulación y otro de no estimulación, con el fin de representar los potenciales de acción evocados con luz y los espontáneos. Los registros fueron almacenados para visualizar las espigas de las células ganglionares y poder aplicar la herramienta de clasificación de espigas. De este modo, se almacenan los instantes de tiempo en los cuales cada célula ganglionar registrada generó potenciales de acción. Este trabajo conllevó al establecimiento de un protocolo de experimentación básico enfocado al uso de matrices MEA en el laboratorio de adquisición de potenciales extracelulares de la Universidad Antonio Nariño Sede Bogotá, no sólo para caracterizar los potenciales de acción de células ganglionares retinales, sino también para otro tipo de células que puedan ser estudiadas empleando matrices de microelectrodos.</p><p align="center"><strong>Recording of Electrical Activity in the Retina of an Albino Rat Employing a Microelectrode Array</strong></p><p>The microelectrode arrays (MEA) are devices that allow the detection of action potentials or spikes in populations of excitable cells, offering a wide spectrum of applications in topics of Neurosciences and Biology. This work describes a protocol for recording of spikes in a population of retinal ganglion cells employing a microelectrode array. The retina of an albino rat was dissected and prepared to be stimulated<em> in vitro </em>with white led light and to record their evoked spikes. Each microelectrode can record spikes from more than a ganglion cell, for which it was necessary to determine which cell fires each spike applying a procedure known as spike sorting. The work allowed to obtain the recording of a stimulation period and another of non-stimulation, representing evoked and spontaneous action potentials. The recordings were saved, in order to visualize the action potentials of the ganglion cells detected  and to apply a computational method for the spike sorting. In this way, it was saved the time stamps in which each action potential was fired by its respective cell. This work established a basic experimentation protocol focused to the use of MEA devices in the laboratory for acquisition of extracellular potentials at the Antonio Nariño University – Bogota Headquarters, not only for characterization of action potentials fired by retinal ganglion cells populations, but also for other kind of cells that can be studied employing MEA devices.</p><p> </p>

An excitatory amacrine cell detects object motion and provides feature-selective input to ganglion cells in the mouse retina

Retinal circuits detect salient features of the visual world and report them to the brain through spike trains of retinal ganglion cells. The most abundant ganglion cell type in mice, the so-called W3 ganglion cell, selectively responds to movements of small objects. Where and how object motion sensitivity arises in the retina is incompletely understood. In this study, we use 2-photon-guided patch-clamp recordings to characterize responses of vesicular glutamate transporter 3 (VGluT3)-expressing amacrine cells (ACs) to a broad set of visual stimuli. We find that these ACs are object motion sensitive and analyze the synaptic mechanisms underlying this computation. Anatomical circuit reconstructions suggest that VGluT3-expressing ACs form glutamatergic synapses with W3 ganglion cells, and targeted recordings show that the tuning of W3 ganglion cells' excitatory input matches that of VGluT3-expressing ACs' responses. Synaptic excitation of W3 ganglion cells is diminished, and responses to object motion are suppressed in mice lacking VGluT3. Object motion, thus, is first detected by VGluT3-expressing ACs, which provide feature-selective excitatory input to W3 ganglion cells.

DOI:http://dx.doi.org/10.7554/eLife.08025.001

Animals can use their eyes to detect moving objects, which helps them to avoid predators and other threats, and to spot potential prey or allies. Visual information from the eyes is sent to the brain, which processes the information to form a coherent picture of how the objects are moving. This processing has to be able to account for movements of the head, eyes, and body—which can cause the image of an object on the retina within the eye to move even if the object itself remains stationary.

Within the retina, light is converted into electrical signals by cells called rods and cones. A layer of cells called bipolar cells relay these signals to the ‘ganglion’ cells, which in turn pass them on to the brain. In mice, a type of ganglion cell called the W3 ganglion cell has been shown to respond selectively to small moving objects, but exactly how these cells acquire their motion sensitivity remained unclear.

Kim et al. now reveal that cells called amacrine cells, which regulate the transfer of signals from the bipolar cells to ganglion cells, supply the information needed for motion detection. The mouse eye contains up to 50 different types of amacrine cells. One of these—called the VG3-amacrine cell—increases its activity whenever an object moves relative to its background, but decreases its activity whenever the object and background move together. The overall effect is that the cells respond selectively to the presence of small moving objects.

Most amacrine cells regulate the transfer of signals within the retina by inhibiting the activity of ganglion cells. But, Kim et al. show that VG3-amacrine cells release a molecule called glutamate to activate W3 ganglion cells when a moving object is detected. These unusual and specialized cells are, thus, an essential component of a circuit in the nervous system that supports motion detection. It is possible that some other types of amacrine cells may also play specialized roles in the detection of other features in the visual world.

DOI:http://dx.doi.org/10.7554/eLife.08025.002

Effects of dopamine on response properties of ON-OFF RGCs in encoding stimulus durations

Single retinal ganglion cell's (RGCs) response properties, such as spike count and response latency, are known to encode some features of visual stimuli. On the other hand, neuronal response can be modulated by dopamine (DA), an important endogenous neuromodulator in the retina. In the present study, we investigated the effects of DA on the spike count and the response latency of bullfrog ON-OFF RGCs during exposure to different stimulus durations. We found that neuronal spike count and response latency were both changed with stimulus durations, and exogenous DA (10 μM) obviously attenuated the stimulus-duration-dependent response latency change. Information analysis showed that the information about light ON duration was mainly carried by the OFF response and vice versa, and the stimulation information was carried by both spike count and response latency. However, during DA application, the information carried by the response latency was greatly decreased, which suggests that dopaminergic pathway is involved in modulating the role of response latency in encoding the information about stimulus durations.

Response dynamics of bullfrog ON-OFF RGCs to different stimulus durations

Stimulus duration is an important feature of visual stimulation. In the present study, response properties of bullfrog ON-OFF retinal ganglion cells (RGCs) in exposure to different visual stimulus durations were studied. By using a multi-electrode recording system, spike discharges from ON-OFF RGCs were simultaneously recorded, and the cells' ON and OFF responses were analyzed. It was found that the ON response characteristics, including response latency, spike count, as well as correlated activity and relative latency between pair-wise cells, were modulated by different light OFF intervals, while the OFF response characteristics were modulated by different light ON durations. Stimulus information carried by the ON and OFF responses was then analyzed, and it was found that information about different light ON durations was more carried by transient OFF response, whereas information about different light OFF intervals were more carried by transient ON response. Meanwhile, more than 80 % information about stimulus durations was carried by firing rate. These results suggest that ON-OFF RGCs are sensitive to different stimulus durations, and they can efficiently encode the information about visual stimulus duration by firing rate.

Bivariate cumulative probit model for the comparison of neuronal encoding hypotheses

Understanding the way stimulus properties are encoded in the nerve cell responses of sensory organs is one of the fundamental scientific questions in neurosciences. Different neuronal coding hypotheses can be compared by use of an inverse procedure called stimulus reconstruction. Here, based on different attributes of experimentally recorded neuronal responses, the values of certain stimulus properties are estimated by statistical classification methods. Comparison of stimulus reconstruction results then allows to draw conclusions about relative importance of covariate features. Since many stimulus properties have a natural order and can therefore be considered as ordinal, we introduce a bivariate ordinal probit model to obtain classifications for the combination of light intensity and velocity of a visual dot pattern based on different covariates extracted from recorded spike trains. For parameter estimation, we develop a Bayesian Gibbs sampler and incorporate penalized splines to model nonlinear effects. We compare the classification performance of different individual cell covariates and simple features of groups of neurons and find that the combination of at least two covariates increases the classification performance significantly. Furthermore, we obtain a non-linear effect for the first spike latency. The model is compared to a naïve Bayesian stimulus estimation method where it yields comparable misclassification rates for the given dataset. Hence, the bivariate ordinal probit model is shown to be a helpful tool for stimulus reconstruction particularly thanks to its flexibility with respect to the number of covariates as well as their scale and effect type.

Rapid feedforward computation by temporal encoding and learning with spiking neurons

Primates perform remarkably well in cognitive tasks such as pattern recognition. Motivated by recent findings in biological systems, a unified and consistent feedforward system network with a proper encoding scheme and supervised temporal rules is built for solving the pattern recognition task. The temporal rules used for processing precise spiking patterns have recently emerged as ways of emulating the brain's computation from its anatomy and physiology. Most of these rules could be used for recognizing different spatiotemporal patterns. However, there arises the question of whether these temporal rules could be used to recognize real-world stimuli such as images. Furthermore, how the information is represented in the brain still remains unclear. To tackle these problems, a proper encoding method and a unified computational model with consistent and efficient learning rule are proposed. Through encoding, external stimuli are converted into sparse representations, which also have properties of invariance. These temporal patterns are then learned through biologically derived algorithms in the learning layer, followed by the final decision presented through the readout layer. The performance of the model with images of digits from the MNIST database is presented. The results show that the proposed model is capable of recognizing images correctly with a performance comparable to that of current benchmark algorithms. The results also suggest a plausibility proof for a class of feedforward models of rapid and robust recognition in the brain.

Computing complex visual features with retinal spike times

Neurons in sensory systems can represent information not only by their firing rate, but also by the precise timing of individual spikes. For example, certain retinal ganglion cells, first identified in the salamander, encode the spatial structure of a new image by their first-spike latencies. Here we explore how this temporal code can be used by downstream neural circuits for computing complex features of the image that are not available from the signals of individual ganglion cells. To this end, we feed the experimentally observed spike trains from a population of retinal ganglion cells to an integrate-and-fire model of post-synaptic integration. The synaptic weights of this integration are tuned according to the recently introduced tempotron learning rule. We find that this model neuron can perform complex visual detection tasks in a single synaptic stage that would require multiple stages for neurons operating instead on neural spike counts. Furthermore, the model computes rapidly, using only a single spike per afferent, and can signal its decision in turn by just a single spike. Extending these analyses to large ensembles of simulated retinal signals, we show that the model can detect the orientation of a visual pattern independent of its phase, an operation thought to be one of the primitives in early visual processing. We analyze how these computations work and compare the performance of this model to other schemes for reading out spike-timing information. These results demonstrate that the retina formats spatial information into temporal spike sequences in a way that favors computation in the time domain. Moreover, complex image analysis can be achieved already by a simple integrate-and-fire model neuron, emphasizing the power and plausibility of rapid neural computing with spike times.

A spike-timing-based integrated model for pattern recognition

During the past few decades, remarkable progress has been made in solving pattern recognition problems using networks of spiking neurons. However, the issue of pattern recognition involving computational process from sensory encoding to synaptic learning remains underexplored, as most existing models or algorithms target only part of the computational process. Furthermore, many learning algorithms proposed in the literature neglect or pay little attention to sensory information encoding, which makes them incompatible with neural-realistic sensory signals encoded from real-world stimuli. By treating sensory coding and learning as a systematic process, we attempt to build an integrated model based on spiking neural networks (SNNs), which performs sensory neural encoding and supervised learning with precisely timed sequences of spikes. With emerging evidence of precise spike-timing neural activities, the view that information is represented by explicit firing times of action potentials rather than mean firing rates has been receiving increasing attention. The external sensory stimulation is first converted into spatiotemporal patterns using a latency-phase encoding method and subsequently transmitted to the consecutive network for learning. Spiking neurons are trained to reproduce target signals encoded with precisely timed spikes. We show that when a supervised spike-timing-based learning is used, different spatiotemporal patterns are recognized by different spike patterns with a high time precision in milliseconds.

High speed coding for velocity by archerfish retinal ganglion cells

Background

Archerfish show very short behavioural latencies in response to falling prey. This raises the question, which response parameters of retinal ganglion cells to moving stimuli are best suited for fast coding of stimulus speed and direction.

Results

We compared stimulus reconstruction quality based on the ganglion cell response parameters latency, first interspike interval, and rate. For stimulus reconstruction of moving stimuli using latency was superior to using the other stimulus parameters. This was true for absolute latency, with respect to stimulus onset, as well as for relative latency, with respect to population response onset. Iteratively increasing the number of cells used for reconstruction decreased the calculated error close to zero.

Conclusions

Latency is the fastest response parameter available to the brain. Therefore, latency coding is best suited for high speed coding of moving objects. The quantitative data of this study are in good accordance with previously published behavioural response latencies.

Parallel coding of first- and second-order stimulus attributes by midbrain electrosensory neurons

Natural stimuli often have time-varying first-order (i.e., mean) and second-order (i.e., variance) attributes that each carry critical information for perception and can vary independently over orders of magnitude. Experiments have shown that sensory systems continuously adapt their responses based on changes in each of these attributes. This adaptation creates ambiguity in the neural code as multiple stimuli may elicit the same neural response. While parallel processing of first- and second-order attributes by separate neural pathways is sufficient to remove this ambiguity, the existence of such pathways and the neural circuits that mediate their emergence have not been uncovered to date. We recorded the responses of midbrain electrosensory neurons in the weakly electric fish Apteronotus leptorhynchus to stimuli with first- and second-order attributes that varied independently in time. We found three distinct groups of midbrain neurons: the first group responded to both first- and second-order attributes, the second group responded selectively to first-order attributes, and the last group responded selectively to second-order attributes. In contrast, all afferent hindbrain neurons responded to both first- and second-order attributes. Using computational analyses, we show how inputs from a heterogeneous population of ON- and OFF-type afferent neurons are combined to give rise to response selectivity to either first- or second-order stimulus attributes in midbrain neurons. Our study thus uncovers, for the first time, generic and widely applicable mechanisms by which parallel processing of first- and second-order stimulus attributes emerges in the brain.

Low error discrimination using a correlated population code

We explored the manner in which spatial information is encoded by retinal ganglion cell populations. We flashed a set of 36 shape stimuli onto the tiger salamander retina and used different decoding algorithms to read out information from a population of 162 ganglion cells. We compared the discrimination performance of linear decoders, which ignore correlation induced by common stimulation, with nonlinear decoders, which can accurately model these correlations. Similar to previous studies, decoders that ignored correlation suffered only a modest drop in discrimination performance for groups of up to ∼30 cells. However, for more realistic groups of 100+ cells, we found order-of-magnitude differences in the error rate. We also compared decoders that used only the presence of a single spike from each cell with more complex decoders that included information from multiple spike counts and multiple time bins. More complex decoders substantially outperformed simpler decoders, showing the importance of spike timing information. Particularly effective was the first spike latency representation, which allowed zero discrimination errors for the majority of shape stimuli. Furthermore, the performance of nonlinear decoders showed even greater enhancement compared with linear decoders for these complex representations. Finally, decoders that approximated the correlation structure in the population by matching all pairwise correlations with a maximum entropy model fit to all 162 neurons were quite successful, especially for the spike latency representation. Together, these results suggest a picture in which linear decoders allow a coarse categorization of shape stimuli, whereas nonlinear decoders, which take advantage of both correlation and spike timing, are needed to achieve high-fidelity discrimination.

Fast estimation of motion from selected populations of retinal ganglion cells

We explore how the reconstruction efficiency of fast spike population codes varies with population size, population composition and code complexity. Our study is based on experiments with moving light patterns which are projected onto the isolated retina of a turtle Pseudemys scripta elegans. The stimulus features to reconstruct are sequences of velocities kept constant throughout segments of 500 ms. The reconstruction is based on the spikes of a retinal ganglion cell (RGC) population recorded extracellularly via a multielectrode array. Subsequent spike sorting yields the parallel spike trains of 107 RGCs as input to the reconstruction method, here a discriminant analysis trained and tested in jack-knife fashion. Motivated by behavioral response times, we concentrate on fast reconstruction, i.e., within 150 ms following a trigger event defined via significant changes of the population spike rate. Therefore, valid codes involve only few (≤3) spikes per cell. Using only the latency t(1) of each cell (with reference to the trigger event) corresponds to the most parsimonious population code considered. We evaluate the gain in reconstruction efficiency when supplementing t(1) by spike times t(2) and t(3). Furthermore, we investigate whether sub-populations of smaller size benefit significantly from a selection process or whether random compilations are equally efficient. As selection criteria we try different concepts (directionality, reliability, and discriminability). Finally, we discuss the implications of a selection process and its inter-relation with code complexity for optimized reconstruction.

Encoding of spatio-temporal input characteristics by a CA1 pyramidal neuron model

The in vivo activity of CA1 pyramidal neurons alternates between regular spiking and bursting, but how these changes affect information processing remains unclear. Using a detailed CA1 pyramidal neuron model, we investigate how timing and spatial arrangement variations in synaptic inputs to the distal and proximal dendritic layers influence the information content of model responses. We find that the temporal delay between activation of the two layers acts as a switch between excitability modes: short delays induce bursting while long delays decrease firing. For long delays, the average firing frequency of the model response discriminates spatially clustered from diffused inputs to the distal dendritic tree. For short delays, the onset latency and inter-spike-interval succession of model responses can accurately classify input signals as temporally close or distant and spatially clustered or diffused across different stimulation protocols. These findings suggest that a CA1 pyramidal neuron may be capable of encoding and transmitting presynaptic spatiotemporal information about the activity of the entorhinal cortex-hippocampal network to higher brain regions via the selective use of either a temporal or a rate code.

Pyramidal neurons in the hippocampus are crucially involved in learning and memory functions, but the ways in which they contribute to the processing of sensory inputs and their internal representation remain mostly unclear. The principal neurons of the CA1 region of the hippocampus are surrounded by at least 21 different types of interneurons. This feature, together with the fact that CA1 pyramidal dendrites associate two major glutamatergic inputs arriving from the entorhinal cortex, makes it laborious to track the ‘how’ and ‘what’ of synaptic integration. The present study tries to shed light on the ‘what’, that is, the information content of the CA1 discharge pattern. Using a detailed biophysical CA1 neuron model, we show that the output of the model neuron contains spatial and temporal features of the incoming synaptic input. This information lies in the temporal pattern of the inter-spike intervals produced during the bursting activity which is induced by the temporal coincidence of the two activated synaptic streams. Our findings suggest that CA1 pyramidal neurons may be capable of capturing features of the ongoing network activity via the use of a temporal code for information transfer.

Ideal observer analysis of signal quality in retinal circuits

The function of the retina is crucial, for it must encode visual signals so the brain can detect objects in the visual world. However, the biological mechanisms of the retina add noise to the visual signal and therefore reduce its quality and capacity to inform about the world. Because an organism's survival depends on its ability to unambiguously detect visual stimuli in the presence of noise, its retinal circuits must have evolved to maximize signal quality, suggesting that each retinal circuit has a specific functional role. Here we explain how an ideal observer can measure signal quality to determine the functional roles of retinal circuits. In a visual discrimination task the ideal observer can measure from a neural response the increment threshold, the number of distinguishable response levels, and the neural code, which are fundamental measures of signal quality relevant to behavior. It can compare the signal quality in stimulus and response to determine the optimal stimulus, and can measure the specific loss of signal quality by a neuron's receptive field for non-optimal stimuli. Taking into account noise correlations, the ideal observer can track the signal-to-noise ratio available from one stage to the next, allowing one to determine each stage's role in preserving signal quality. A comparison between the ideal performance of the photon flux absorbed from the stimulus and actual performance of a retinal ganglion cell shows that in daylight a ganglion cell and its presynaptic circuit loses a factor of approximately 10-fold in contrast sensitivity, suggesting specific signal-processing roles for synaptic connections and other neural circuit elements. The ideal observer is a powerful tool for characterizing signal processing in single neurons and arrays along a neural pathway.

Temperature-controlled exposure systems for investigating possible changes of retinal ganglion cell activity in response to high-frequency electromagnetic fields

Two exposure systems were developed for the measurement of retinal ganglion cell responses to light under the influence of pulsed high-frequency electromagnetic fields. Exposure characteristics were determined numerically for the GSM standards (900/1,800 MHz) and the UMTS standard (1,966 MHz) with specific absorption rates, averaged over the region of interest, of 0.02, 0.2, 2 und 20 W kg(-1). Extracellular multi- and single unit recordings of light responses from several retinal ganglion cells per retina could be obtained in these exposure systems on a regular basis, using two recording electrodes simultaneously. With appropriate temperature control adjustment, maximal temperature deviations at exposure onset and offset were well below the range of +/-0.1 degrees C for all SAR values.

Throwing a glance at the neural code: rapid information transmission in the visual system

Our visual system can operate at fascinating speeds. Psychophysical experiments teach us that the processing of complex natural images and visual object recognition require a mere split second. Even in everyday life, our gaze seldom rests for long on any particular spot of the visual scene before a sudden movement of the eyes or the head shifts it to a new location. These observations challenge our understanding of how neurons in the visual system of the brain represent, process, and transmit the relevant visual information quickly enough. This article argues that the speed of visual processing provides an adjuvant framework for studying the neural code in the visual system. In the retina, which constitutes the first stage of visual processing, recent experiments have highlighted response features that allow for particularly rapid information transmission. This sets the stage for discussing some of the fundamental questions in the research of neural coding. How do downstream brain regions read out signals from the retina and combine them with intrinsic signals that accompany eye movements? And, how do the neural response features ultimately affect perception and behavior?

Modeling convergent ON and OFF pathways in the early visual system

For understanding the computation and function of single neurons in sensory systems, one needs to investigate how sensory stimuli are related to a neuron's response and which biological mechanisms underlie this relationship. Mathematical models of the stimulus-response relationship have proved very useful in approaching these issues in a systematic, quantitative way. A starting point for many such analyses has been provided by phenomenological "linear-nonlinear" (LN) models, which comprise a linear filter followed by a static nonlinear transformation. The linear filter is often associated with the neuron's receptive field. However, the structure of the receptive field is generally a result of inputs from many presynaptic neurons, which may form parallel signal processing pathways. In the retina, for example, certain ganglion cells receive excitatory inputs from ON-type as well as OFF-type bipolar cells. Recent experiments have shown that the convergence of these pathways leads to intriguing response characteristics that cannot be captured by a single linear filter. One approach to adjust the LN model to the biological circuit structure is to use multiple parallel filters that capture ON and OFF bipolar inputs. Here, we review these new developments in modeling neuronal responses in the early visual system and provide details about one particular technique for obtaining the required sets of parallel filters from experimental data.

Complex temporal response patterns with a simple retinal circuit

The retina can respond to a wide array of features in the visual input. It was recently reported that the retina can even recognize complicated temporal input patterns and signal violations in the patterns. When a sequence of flashes was presented, ganglion cells exhibited a variety of firing profiles and many cells showed an "omitted stimulus response" (OSR), in which they fired strongly if a flash in the sequence was omitted. We examined the synaptic origins of the OSR by recording excitatory synaptic currents from ganglion cells in the salamander retina in response to periodic flash sequences. Consistent with previous spike recordings, ganglion cells exhibited an OSR in their current response and the OSR shifted in time with a change in flash frequency such that it could predict when the next flash should have occurred. Although the behavior may seem sophisticated, we show that a simple linear-nonlinear model with a spike threshold can account for the OSR in on ganglion cells and that the variety of complex firing profiles seen in other ganglion cells can be explained by adding contributions from the off pathway. We discuss the physiological and simulation results and their implications for understanding retinal mechanisms of visual information processing.

Contribution of Individual Retinal Ganglion Cell Responses to Velocity and Acceleration Encoding

We investigate the capability of turtle retinal ganglion cell (RGC) ensembles to simultaneously encode multiple aspects of visual motion: speed, direction, and acceleration of moving patterns. Bayesian stimulus reconstruction reveals that the instantaneous firing rates of RGCs contain information about all of these stimulus properties. Stimulus velocity is mainly encoded by steady-state firing rates, whereas acceleration can be reconstructed from transient components in RGC activity induced by abrupt velocity changes. Therefore neurons in higher brain areas may in principle extract information about changing velocity from the instantaneous firing activity of RGCs, without the need to compare responses to present velocities to previous ones. However, reconstruction requires the estimation of a combined acceleration and velocity signal, indicating that RGC ensembles signal both properties simultaneously. In accordance with this conclusion, combined velocity/acceleration sensitivity enhances the similarity of artificial spike trains to experimental data by 50% compared with the case of pure velocity tuning. Decoding of motion direction in addition to speed and acceleration requires direction-sensitive cells, which generate higher firing rates for one of the motion directions and therefore show asymmetric velocity tuning. By dividing the entire ensemble of simultaneously recorded cells into one group of direction-sensitive cells and one group with symmetric tuning, we demonstrate that the population of direction-sensitive cells encodes a combination of motion speed, acceleration, and direction. However, estimation of velocity and acceleration is improved by including the larger group of RGC responses that are sensitive to speed but not to motion direction.

A model of the dynamics of retinal activity during natural visual fixation

During visual fixation, small eye movements keep the retinal image continuously in motion. It is known that neurons in the visual system are sensitive to the spatiotemporal modulations of luminance resulting from this motion. In this study, we examined the influence of fixational eye movements on the statistics of neural activity in the macaque's retina during the brief intersaccadic periods of natural visual fixation. The responses of parvocellular (P) and magnocellular (M) ganglion cells in different regions of the visual field were modeled while their receptive fields scanned natural images following recorded traces of eye movements. Immediately after the onset of fixation, wide ensembles of coactive ganglion cells extended over several degrees of visual angle, both in the central and peripheral regions of the visual field. Following this initial pattern of activity, the covariance between the responses of pairs of P and M cells and the correlation between the responses of pairs of M cells dropped drastically during the course of fixation. Cell responses were completely uncorrelated by the end of a typical 300-ms fixation. This dynamic spatial decorrelation of retinal activity is a robust phenomenon independent of the specifics of the model. We show that it originates from the interaction of three factors: the statistics of natural scenes, the small amplitudes of fixational eye movements, and the temporal sensitivities of ganglion cells. These results support the hypothesis that fixational eye movements, by shaping the statistics of retinal activity, are an integral component of early visual representations.