Temporal processing in sensory systems Benedikt Grothe* and Georg M Klump

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1 467 Temporal processing in sensory systems Benedikt Grothe* and Georg M Klump The idea that sensory information is represented by the temporal firing patterns of neurons or entire networks, rather than by firing rates measured over long integration times, has recently gained increasing experimental support. A number of mechanisms that help to preserve temporal information in ascending sensory systems have been identified, and the role of inhibition in these processes has been characterized. Furthermore, it has become obvious that temporal processing and the representation of sensory events by temporal spike patterns are highly dependent upon the behavioral state of the animal or experimental subject. Addresses *Max-Planck-Institute of Neurobiology, Am Klopferspitz 18a, D Martinsried, Germany; bgrothe@neuro.mpg.de Institut für Zoologie, Technische Universität München, Lichtenberg Strasse 4684, D Garching, Germany; georg.klump@bio.tum.de Current Opinion in Neurobiology 2000, 10: /00/$ see front matter 2000 Elsevier Science Ltd. All rights reserved. Abbreviations GABA γ-aminobutyric acid ITD interaural time difference NA nucleus angularis NL nucleus laminaris NM nucleus magnocellularis SON superior olivary nucleus Introduction Changes in the sensory environment mark events to which an organism may need to react. Sensory systems are therefore tuned to respond particularly well to transient stimuli and to other stimuli exhibiting a distinct temporal structure. Without changes in the sensory input, neural responses are usually profoundly reduced. It has been suggested that the similarity in the demand to detect changes in the different modalities has resulted in the evolution of functionally similar neural circuits in the electroreceptive, auditory and visual systems of vertebrates [1]. These circuits diminish the response to a constant sensory environment and enhance the response to changes of environmental cues, thus resulting in the generation of temporal patterns of neural activity that may correlate closely with the changing sensory cue. Over the past few years, accumulating observations on the distinct temporal patterns of neural discharges have led to a change in the focus of analysis of the neural responses, from measuring average levels of activity to recording the temporal pattern of the activity [2 4]. Many stimuli directly lend themselves to temporal encoding in spike trains. Natural sounds, for example, usually have a time-varying amplitude, and their different frequency components change over time in a coherent fashion [5,6]. It is known from psychophysical studies that such coherent changes can enhance the separation of signals from the sensory background [7]. For example, visual targets simultaneously provide transients in the receptive fields of many neurons, resulting in correlated neural activity [8]. The structure of cortical neural networks and the response characteristics of their neuronal building blocks support temporal pattern analysis by synchronized and oscillatory activity that leads to temporally correlated responses in different areas of the brain [9,10]. These correlations in activity may be used in the binding of parts of multi-component signals that allows the formation of sensory objects, and to assist in the analysis of complex scenes (e.g. [11]). We are beginning to define the special adaptations in sensory systems through which temporal patterns of action potentials can accurately represent stimulus features at various levels of sensory analysis. The temporal resolution necessary for this task ranges from microseconds to seconds (see Figure 1). In this review, we first discuss the recent advances in our understanding of the neuronal mechanisms including the role of inhibitory projections that underlie the temporal analysis and the preservation of temporal information in the sub-millisecond domain, as found in the bird auditory brainstem. We then present evidence that neural inhibition plays a major role in temporal processing in general. Next, we discuss recent experimental evidence supporting the idea that temporal codes may provide an alternative to the ratebased representation of sensory stimuli. Finally, we address the issue of how temporal processing is modulated by attention to result in a representation of sensory stimuli that depends upon the behavioral state of the organism. Preserving temporal resolution In order to exploit temporal stimulus parameters, sensory systems have to preserve the precise timing of action potentials throughout the sensory pathways. There has been significant progress in our understanding of the biophysical membrane properties underlying the precise timing of spikes, of the morphological and biochemical specialities allowing temporally precise transmission (for example, via calyx synapses, the largest synapses in the auditory brainstem allowing a very secure and temporally precise transmission) and of neural circuits that allow improvement of the temporal acuity of spikes via coincidence detection of converging inputs [12 15]. Recent findings now provide insight into the mechanisms of gain control that are needed to avoid saturation of response patterns at high stimulus intensities that may interfere with temporal processing. A well-studied example concerns the processing of differences between the arrival times of a sound at the two ears

2 468 Sensory systems Figure 1 Sound localization in birds (e.g. barn owl) [79]. Detection of temporal gaps in broadband signals [81]. Element separation in auditory object formation [11]. 1µs 10 µs 100 µs 1 ms 10 ms 100 ms 1 s 10 s Evaluation of waveforms by electric fish [78]. Target range discrimination in echolocating bats [80]. Human temporal window in audition [82]. Discrimination of duration and rate of territorial or mating signals (e.g. birdsong) [83]. Current Opinion in Neurobiology Performance in different temporal analysis tasks and demands on the accuracy of temporal processing mechanisms for evaluating relevant stimulus parameters (see [11,78 83]). via coincidence detection by the bird auditory brainstem. These interaural time differences (ITDs) are the main cue for localizing sounds in the horizontal plane. Behavioral experiments indicate that ITDs below 10 µsec can be perceived by barn owls (Figure 1 gives orders of magnitude for temporal resolution shown in different sensory analysis tasks). Bird auditory nerve-fiber discharges reliably reflect the temporal structure of acoustic stimuli they show significant phase-locking to sine waves up to 2 4 khz in most birds and even up to 9 khz in barn owls (compare [16]). As a result of specialized synapses and the fast kinetics of glutamate receptors (for a review, see [13,14]), the high temporal acuity of auditory nerve fibers is conveyed to the cells of one of the cochlear nucleus subdivisions, nucleus magnocellularis (NM; see [17]), with only a small degree of deterioration [16]. Neurons in the second cochlear nucleus subdivision, nucleus angularis (NA), do not receive the large specialized synapses typical for NM neurons and only phase-lock to lower frequencies [17]. NM neurons project to the ipsilateral and contralateral nucleus laminaris (NL; [18 20]). Neurons in NL compare the exact arrival time of action potentials from the ipsilateral and contralateral NM. When the binaural inputs coincide an NL neuron will fire preferentially [21,22]. Considerable progress has been made in understanding how the inventory of ion channels in NM and NL creates membrane properties that enable the cell to precisely detect coincidence in the microsecond range. For instance, the inventory allows fast rising membrane potentials, reducing the jitter of spikes, and allows a fast rectification that is necessary to prohibit ongoing activity [23 26]. Only recently, however, have studies focused on the GABAergic inputs to both NM and NL that might be crucial for faithful phase-locking and for precise coincidence detection over a wide range of stimulus intensities. ITD sensitivity in NL neurons is rather robust against changes in absolute intensity [27] or interaural intensity differences [28], indicating the existence of a gain control mechanism that keeps coincidence detectors within the optimal working range. In vitro recordings from the chick NL indicate that this gain control might be achieved by GABAergic inputs [29] via the superior olivary nucleus (SON). Neurons of the NA, as well as NL neurons, project to cells in the SON. SON neurons are GABAergic and project back to NA, NL, and NM (see Figure 2; [30 32,33 ]. In in vitro studies, SON neurons exhibit a rather high input resistance and do not follow high repetition rates of electrical stimulation in a stimulus-by-stimulus fashion. Repetitive stimulation with subthreshold stimuli leads to a build-up of excitatory postsynaptic potentials and eventually causes the cells to fire [32]. This temporal integration suggests that SON neurons should not phase-lock to pure tones. Additionally, in vitro recordings show that stimulation of SON fibers causes slow inhibitory postsynaptic currents that are phase-locked only at very low stimulation rates and summate to form a current plateau at moderate rates in NL [32] and NM neurons [33,34 ]. The summation is at least partially attributable to an asynchrony in presynaptic vesicle release that increases with stimulus repetition rate. Reducing intracellular Ca 2+ levels using the specific Ca 2+ chelator EGTA partially restores the correlation of quantal release with stimulation. This finding might indicate that repetitive stimulation results in an accumulation of Ca 2+ in the presynaptic terminal that induces a rather continuous vesicle release, thus de-correlates synaptic transmission from the incoming action potentials [34 ]. Thus, SON neurons may cause a somewhat sustained stimulus-driven GABA release onto their target cells. In NM as well as NL neurons, GABAergic inputs cause a slow, bicuculline-sensitive, inhibitory depolarization [31,32,33,34,35]. A recent study shows that in NM neurons, GABA release initiates a mildly depolarizing Cl conductance that activates a low-voltage K + conductance [33 ]. This shunting causes a large reduction in the cell s input resistance, increasing membrane conductance and shortening the membrane time-constant, hence decreasing temporal summation and thereby requiring higher input currents for reaching threshold for generation of action potentials. In addition, the shunt may increase the precision of phase locking, and could also counterbalance the effects of increasing stimulus intensity. A second way that GABA can modulate NM firing is via presynaptic GABA B receptors that reduce transmitter release thereby preventing synaptic depression [36]. This

3 Temporal processing in sensory systems Grothe and Klump 469 Figure 2 Possible mechanism for gain control in the neural circuit processing interaural time differences in the bird auditory brainstem. The two subdivisions of the cochlear nucleus (NA and NM) receive direct input from the auditory nerve (N VIII). NL neurons act as coincidence detectors for the binaural excitatory inputs of NM neurons from both sides. Neurons in the SON provide a tonic, sound-driven GABAergic input to NL and NM. (a) The anatomical arrangement of the bird auditory brainstem nuclei. d, dorsal; v, ventral. (b) The connection patterns of the left NL. For full details, see text. (a) NA N VIII (b) NL NM SON magnocellularis (NM) (ii) d v To midbrain laminaris (NL) (iii) (iv) (i) Postsynaptic temporal integration (ii) Presynaptic de-correlation; postsynaptic depolarization and shunting (iii) Postsynaptic depolarization and shunting (iv) Binaural coincidence detection magnocellularis (NM) angularis (NA) (ii) GABA N VIII (i) Superior olivary nucleus (SON) left right Brainstem N VIII Excitatory projection Inhibitory projection Current Opinion in Neurobiology will help to enhance the dynamic range of auditory nerve firing rates that the NM cell will follow. Inhibition is involved in analyzing temporal cues As shown above, tonic inhibition may be crucial for some time-analyzing circuits. In other cases, however, the timing of inhibition itself plays an important role. Studies in the auditory brainstem of bat (for reviews, see [37,38]) and rabbit [39 ] have revealed several ways in which the interaction of accurately timed excitation and inhibition establishes filter properties for specific temporal stimulus features. In the auditory midbrain there is also evidence for a strong impact of inhibition on complex temporal filter properties. There is a high convergence of different excitatory and inhibitory inputs, each with different timing properties [40,41]. For example, most neurons in the bat auditory midbrain receive several sets of inhibitory inputs, GABAergic as well as glycinergic, some of which are driven by the ipsilateral ear and some by the contralateral ear [42,43]. Some inhibitory components match the timing of excitatory inputs. In most cells, however, additional inhibitory components exhibit a different arrival time and/or different duration from the excitatory component, often persisting far beyond the offset of the stimulus. These persistent inhibitory components, that have been shown to be mainly GABAergic, may function to suppress reverberations, thus improving segregation of stimuli in a complex environment [44,45]. Alternatively, the complex interaction of inputs with different timing properties could create selective filters for temporally complex sounds. Because the response of the majority of cells tested in these studies exhibits binaural inhibitory components, one might expect to find significant interactions between temporal-pattern cues and spatial cues. Several studies in the auditory midbrain show, in fact, that this is the case. For example, in the frog and bat auditory system, processing of temporal patterns depends upon the spatial position of the sound source [46 51]. Furthermore, temporal features such as stimulus repetition rate strongly influence selectivity to other stimulus characteristics, for example stimulus amplitude [52]. The interdependence of temporal and non-temporal stimulus features should not come as a surprise, as non-temporal cues have a profound impact on the timing of stimulus-driven spikes even at the level of the receptor. For instance, the delay of a neuron in response to a tone is influenced not only by the moment of arrival of the sound wave at the eardrum, but also by its intensity. The latter defines the peak velocity of the cochlea s basilar membrane, which defines the exact timing of spikes [53]. This has profound implications, for example, for the processing of interaural intensity differences [54,55]. GABAergic inhibition also seems to be crucial for the processing of motion cues; in other words, cues that indicate a change of spatial position over time. Blocking GABA transmission eliminates motion-direction selectivity at very different levels of the visual system, from the retina [56] up to the cortex [57]. Recent results indicate that the anticipation of movement, starting at the level of the retina, relies on negative feedback and, hence, on inhibition [58].

4 470 Sensory systems Kautz and Wagner [59] have shown that, in an analogous manner, motion-direction selectivity in the barn owl midbrain also depends on GABAergic inhibition. They propose that the mechanisms for motion-detection selectivity rely on GABAergic inhibition across spatial receptors very similar to a model for motion-direction selectivity in the retina [60,61]. The existence of similar mechanisms in mammals, however, is questioned by a recent study in the guinea pig inferior colliculus [62 ]. Is sensory information reflected in temporal discharge patterns? Traditionally, studies concerned with the neural representation of sensory stimuli have focused on averaged firing rates of neurons arranged in a topographic manner. More recent studies, however, indicate that sensory information might also be represented in the temporal firing pattern of cells rather than in the integrated spike counts. Moreover, these cells may be distributed over larger cortical areas and no simple topographic arrangement may be discernible. Support for this view comes from a systematic analysis of what has been considered to be spontaneous activity. Conventionally, background activity had been interpreted as random, spontaneous discharges that are not related to signal processing. However, recordings from primary areas of the visual cortex have revealed that spontaneous discharges of single neurons or their responses to optical stimulation are, in fact, correlated with the ongoing activity in the close environment [63]. This demonstrates the importance of the coherent oscillatory activity for stimulus processing that is found in large areas of the visual cortex [10,64]. It also indicates that temporally correlated activity per se may bear important information about the outside world. In addition, it follows from the neural-network organization of the cortex that the history of neural activity is an important determinant of temporal spike patterns. An analysis of temporal firing patterns of cortical neurons indicates that action potentials occurring after very short interspike intervals contribute more to stimulus encoding than do action potentials that occur after long interspike intervals [65]. Furthermore, recognizable temporal patterns of the activity of single neurons arise in neural networks as a result of their connectivity [2,4,9]. The concept that the short-term temporal pattern of spikes, rather than the integrated activity over longer time-windows, may carry important information might also help to solve the problem of how sound location is represented in the mammalian auditory cortex [3]. In the auditory cortex, clusters of neurons with specific binaural input patterns can be found, but there is no convincing evidence for a topographic space map. Moreover, based on spike rates, the spatial tuning of neurons in auditory cortex is broad [66,67,68 ], even compared to that found in the thalamus [69,70]. Although the synchrony in the response of the population of auditory cortical neurons does not seem to provide a more accurate representation of sound location than the neurons spike rate [71], spike patterns of many simultaneously recorded single neurons might do so. At least, these patterns allow a neural network to match the behavioral localization performance of cats [68 ]. This finding might indicate a distributed time-based representation of auditory space in the mammalian cortex that is independent of firing rate. Modulating temporal processing Temporal patterns, thus, may be important in representing sensory information in cortical, and other, areas. There is increasing evidence that, in addition to the stimulus itself, the behavioral state of an animal, the experimental activation of subcortical structures involved in alertness, or iontophoretic application of transmitters used by these structures, can modulate the timing of neural events. Examples of how the behavioral state of an animal can influence the processing of sensory stimuli come from experiments in which the experimental subjects are required to perform specific actions during the recordings. These experiments indicate that context-dependent behavior can evoke identifiable temporal patterns of neural activity in response to sensory stimulation [2,72]. Steinmetz et al. [73 ], for example, demonstrate a significant impact of attention on the firing patterns of neurons in the secondary somatosensory cortex of awake monkeys. Synchrony of action potentials recorded from pairs of neurons is shown to depend upon whether a visual or a somatosensory stimulus was presented. However, the most fascinating finding is that focusing attention on a specific sensory input has a direct impact on the degree of synchrony, thus supporting the idea of a direct correlation between the firing patterns over populations of cortical neurons and the resultant perception. How brain structures that are thought to be involved in focusing attention may affect the temporal processing at various levels can be demonstrated in the mammalian auditory system. Application of noradrenaline, the transmitter used by neurons in the locus coeruleus, improves the temporal acuity of auditory brainstem neurons [74]. Local application of serotonin, the transmitter used by neurons in the raphe nuclei, reversibly changes or abolishes the direction selectivity for frequency modulations in some auditory midbrain neurons [75 ]. Pairing of a repetitive stimulus presentation with electrical stimulation of the nucleus basalis, which provides acetylcholinergic projections to sensory cortex areas, considerably alters the temporal coding properties of neurons in the auditory cortex [76]. Moreover, Shulz et al. [77 ] have shown not only that repetitive whisker deflection, paired with local application of acetylcholine, modifies the response of single barrel cortex neurons, but also that the response characteristics observed after some time of pairing can later be re-evoked only in presence of acetylcholine. The latter finding might be interpreted as an analogue to state-dependent learning. Conclusions The prevalence of temporal features in the input to the neural systems of many sensory modalities suggests that

5 Temporal processing in sensory systems Grothe and Klump 471 more emphasis on temporal processing is needed in the analysis of the responses of sensory neurons. Focusing mainly on the auditory systems of birds and mammals, we have demonstrated that temporal information can be preserved up to the highest levels of sensory systems by the action of mechanisms operating at the level of the synapses (e.g. specialized synaptic morphology and specialized ion channels) or at the level of neural circuits (e.g. through mechanisms that require action-potential coincidence, or through inhibition). Thus, even in central sensory neurons, there may be the potential to faithfully represent environmental cues in terms of temporal patterns of action potentials. The observation that temporal response patterns depend critically upon attentional or behavioral states stresses the importance of studies in awake and behaving animals. 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