Sensory systems adapt their neural code to changes in the sensory environment, often on multiple time scales. Here, we report a new form of adaptation in a first-order auditory interneuron (AN2) of crickets. We characterize the response of the AN2 neuron to amplitude-modulated sound stimuli and find that adaptation shifts the stimulus–response curves toward higher stimulus intensities, with a time constant of 1. 5 s for adaptation and recovery. The spike responses were thus reduced for low-intensity sounds. We then address the question whether adaptation leads to an improvement of the signal' s representation and compare the experimental results with the predictions of two competing hypotheses: infomax, which predicts that information conveyed about the entire signal range should be maximized, and selective coding, which predicts that “foreground” signals should be enhanced while “background” signals should be selectively suppressed. We test how adaptation changes the input–response curve when presenting signals with two or three peaks in their amplitude distributions, for which selective coding and infomax predict conflicting changes. By means of Bayesian data analysis, we quantify the shifts of the measured response curves and also find a slight reduction of their slopes. These decreases in slopes are smaller, and the absolute response thresholds are higher than those predicted by infomax. Most remarkably, and in contrast to the infomax principle, adaptation actually reduces the amount of encoded information when considering the whole range of input signals. The response curve changes are also not consistent with the selective coding hypothesis, because the amount of information conveyed about the loudest part of the signal does not increase as predicted but remains nearly constant. Less information is transmitted about signals with lower intensity.
Efficient encoding of natural signals is one of the major tasks sensory pathways have to accomplish. In order to do this, neural representation should be matched to the relevant part of incoming signals. Statistical properties of incoming signals are highly variable in a natural environment (e. g. , the mean light level changes dramatically from a sunny region to a dark forest) but are mostly changing slowly over time [1]. Since the neural representation in sensory cells is limited to a certain range and resolution, the principle of efficient coding suggests that the nervous system should continually adapt its responses to changing statistical properties of the stimuli [2]. Firing rate adaptation changes the input-response curves of neurons in sensory pathways and has been shown to provide a mechanism for the adjustment of the encoding scheme in multiple systems [3]–[9]. How the input-response curve is altered in response to a given stimulus should depend on what the relevant information is in the given context. Here, we want to explore the response properties of a single cell (AN2) in the auditory pathway of crickets and test for two different principles that have been proposed to underlie adaptation of the input-response curve: the principle of maximum information preservation (infomax) [10] and that of selective coding [11]. The AN2 neuron provides an ideal model for studying the computational principles underlying adaptation, since (1) it receives direct input from auditory receptors and local interneurons at the first processing level [12], (2) on present evidence, it constitutes the only ascending representation of the auditory environment in the high frequency channel and thus a bottleneck for information transmission to higher centers [13]–[15], and (3) it has a clear behavioral role because it is intimately involved in evasive behavior in response to ultrasonic signals [16]–[18]. Several time constants of adaptation in the range from below 100 ms to several seconds are known for the receptor cells [19], local interneurons [20], and the ascending neurons [21], [22] in this model system. Since auditory processing at the stage of the AN2 neuron is mainly feed-forward, adaptation is likely driven by the stimulus only rather than by task-dependent top-down processes. The above mentioned principles lead to conflicting hypotheses about changes of the input-response curve when more than one ‘signal’ is present in an environment (Figure 1). Following the infomax principle, the input–output transformation (the neuronal response curve) should maximize the information transmission between the neural representation and the stimulus. The optimal response curve depends on the statistical properties of the input signals, but internal noise and constraints on the possible changes limit the amount of information that can be conveyed. The infomax principle leads to the theoretical result that the derivative of the response curve should be proportional to the probability distribution of the stimuli, so that all available signals in a given environment are represented and every possible output rate occurs with equal probability. Laughlin [23] tested this prediction and showed that contrast response curves in the fly visual system are matched to the statistics of natural images in order to maximize information transmission. Similar results have been reported for contrast response curves of retinal and LGN neurons in cat and monkey [24]. Information maximization can explain retinal coding in the spatial, temporal and chromatic domain [25]. A consequence of the infomax principle is that a change in the statistics of the sensory input must be compensated by a change in the input–response curve. Experimental evidence from the motion-sensitive H1 neuron in the fly supports this hypothesis: this neuron adapts its response curve to changing statistics of stimuli on several time scales [4], [6], in a way which is compatible with the infomax prediction. Experiments have also shown that adaptation enhances information transmission in visual cortex [26]. Sharpee et al. estimated neural filters for the responses to natural inputs and to noise inputs matched for luminance and contrast, showing that neural filters adaptively changed with higher order statistics of input signals, so as to increase the mutual information between stimulus and neural response. Theoretical work also suggested that contrast adaptation in the mammalian visual system [3], [9], [27]–[29] can be understood as a consequence of the infomax principle [30], [31]. However, it is difficult to quantify the role of adaptation in enhancing coding efficiency at higher stages in vertebrate sensory pathways since in these, coding is distributed among large populations of neurons and their responses are modulated by the activity of other neural populations or brain areas. Thus, simple sensory networks of invertebrates, whose representation are not heavily influenced by feedback signals, may provide a more suitable model to understand the principles lying behind sensory adaptation. An alternative principle that may underlie adaptation is selective coding (or ‘background suppression’), a form of temporal inhibition in which a loud sound suppresses the response to subsequent sounds. This could serve to segregate a single, most important signal from other signals or background noise. It has been shown that an auditory interneuron (ON1) represents mainly the louder part of a stimulus with a bimodal intensity distribution [20]. Calcium aggregation in the omega neuron is a possible mechanism underlying this background suppression [11], [32]. Similar findings have been made in bushcrickets: while multiple songs in choruses of singing males are present, only the most intense song was found to be represented in the auditory pathway [33]. These previous studies, however, address the phenomenon only qualitatively and not under the viewpoint of an encoding scheme and information transfer. Segregation of different auditory objects into different channels has also been studied in vertebrate hearing [34]–[36]. In vertebrates, however, modulation of carrier frequency is assumed to play a crucial role in this stream segregation [37], complicating a detailed analysis. Information conveyed by carrier frequency modulation is very limited in crickets, as they possess only two broadly tuned frequency channels, one in the range around the carrier frequency of the calling songs (∼5 kHz) and another one mainly for frequencies above 12 kHz. Thus, crickets provide an ideal model system to study object-background segregation—in this simple auditory system, for a given frequency range, an auditory object can simply be seen as the loudest peak in the entire stimulus distribution. The questions we sought to answer were: How does the neural response curve adapt to the statistics of the acoustic environment? Can this sensory system be characterized as a communication channel optimized for coding the inputs such that as much information as possible is preserved (infomax principle)? Or does the system perform a preprocessing that leads to a high fidelity representation of only the loudest part of the stimuli (selective coding)? To address these questions, we measured the neural response curve of AN2 neurons after adaptation to sound stimuli with either two or three peaks in their intensity distribution, depicted in Figure 1. The two principles studied here predict conflicting changes of the form of the input-response curve when presenting a stimulus composed of more than one signal. Optimal selective coding should lead to a shift of the response curve in a way that only the peak with the highest intensity is represented (Figure 1B). If infomax is the underlying principle, adaptation pursues the objective to maximize the information that the neuron' s output conveys about its sensory input. Adaptation should thus change the response curve in a way that the whole stimulus range is encoded reliably (Figure 1A). Firing rate adaptation can change the stimulus–response curve basically in two ways [38]: shifting the threshold to larger intensities and changing the slope of the curve. We first compare the experimentally observed changes in the slope and the shift in response curves to the optimal changes predicted by the two competing hypotheses. Differences between model prediction and data, however, do not necessarily imply that a particular hypothesis is unlikely to be true, because additional constraints may limit the potential of tuning curve changes. Therefore, in a second step, we calculate the mutual information between the sensory input and the neuronal response using the measured response curves. The infomax principle predicts that the mutual information between a particular stimulus distribution and the response should be highest for the response curve that is adapted to the stimulus distribution. The response curve adapted to the stimulus with three peaks should encode the three-peak stimulus better than the response curve adapted to the stimulus with two peaks. Selective coding, on the other hand, predicts, that the mutual information should decrease for the ‘background’ signals and should increase for the most intense peak.
Crickets of the species T. oceanicus and T. leo were used in the experiments to characterize the time course of adaptation. For the experiments with the multimodal stimuli (cf. Methods, Stimulus protocols), mainly T. leo individuals were used. All animals were laboratory reared. For preparation, both pairs of wings and the meso- and metathoracic legs were removed. The animal was fixed ventral side up to a small platform and the prothoracic legs with the ears were waxed to pins at the coxae and the tarsi in a normal walking position. Ascending and descending connectives from the prothoracic ganglion were cut in order to reduce neuronal background activity. See [12] for a more detailed description. Two extra-cellular hook-electrodes were made from tungsten wire and placed in parallel around one of the two connectives ascending from the prothoracic ganglion. These connectives contain the axon of the ascending interneuron we wanted to record from (AN2). Vaseline was placed around connectives and hooks in order to isolate the electrodes electrically and keep the connective from drying out. The voltage trace was amplified differentially (npi, EXT-10C, Tamm, Germany) and bandpass-filtered with cut-off frequencies of 300 Hz and 3 kHz (npi, DPA 2F). The trace was then digitized at 20 kHz sampling rate (National Instruments, PCI-6014, Austin, TX) and stored to the hard disk of a personal computer. Spikes of the AN2 were detected on the basis of the amplitude peaks of the voltage trace using custom Software (MATLAB 7, The MathWorks, Natick, MA). Figure 2 shows an example recording and the spike detection window. The recording set-up was lined with sound-absorbing foam to reduce echoes. Acoustic stimuli were presented through a loudspeaker positioned ipsilaterally to the recorded connective at a distance of 36 cm. The main input to the AN2 neuron comes from receptors ipsilateral to the connective that holds its axon. Stimuli were presented by analog multiplication of a generated 16 kHz sine wave (Voltacraft, FG-506, Hirschau, Germany) with an amplitude modulation envelope that was generated by the personal computer at 10 kHz sampling rate (National Instruments, PCI-6014, Austin, TX). Following this, the signal was attenuated using a programmable attenuator (Tucker-Davis, PA5, Gainesville, FL) and amplified by an audio amplifier (Blaupunkt, GTA 2100B, Hildesheim, Germany). Attenuation of the signal was calibrated using a Bruel & Kjaer microphone (type 2231, Bremen, Germany). Figure 3 shows the protocol used for characterizing the adaptation process in the ascending AN2 neuron. The different ensembles of auditory stimuli consist of an adapting stimulus, a silent interval, and a test stimulus. The intensity of the adapting stimulus was adjusted in the beginning of the recording depending on the response strength of the neuron. Normally, the base line intensity of the adapting stimulus had a sound pressure level of 84 dB or 87 dB. With the term relative intensity we refer to the stimulus intensity relative to this base line intensity. Adapting stimuli are 16 kHz signals that were amplitude-modulated by bandpass-filtered Gaussian white noise with 100 Hz cut-off frequency. The Gaussian noise had a variance σ2 = 1. 38 dB2 and a mean relative intensity μ = 0 dB. Test stimuli were pure sinusoidal tones with a frequency of 16 kHz. To characterize adaptation, we used adapting stimuli with durations 75 ms, 150 ms, 300 ms, 600 ms, 1200 ms, 2400 ms, and 4800 ms (Figure 3A). For testing recovery from adaptation, the stimuli had a 5 s adaptation phase followed by pauses of varying durations from 75 ms to 4800 ms (Figure 3B). Motivated by the competing coding hypotheses, we wanted to examine the consequences of adaptation to different ensembles of auditory stimuli demanding different changes in the stimulus-response curve. We designed multimodal noise-like stimuli whose amplitude distribution had two or three modes, mimicking auditory scenes with multiple signals. The amplitude distribution of the bimodal stimulus is composed of two Gaussian distributions, with mean relative intensity μ1 = −3 dB, μ2 = 0 dB and variance σ2 = 0. 2 dB2. The trimodal stimulus has an additional peak modeled by a third Gaussian distribution with mean μ3 = +3 dB and σ2 = 0. 2 dB2. An example of these stimuli is shown in Figure 3C, together with the respective amplitude distributions. The adaptation time was 5 s in these experiments and the silent interval before the test stimulus was 100 ms. In all experiments, the intensity response curves were determined by sinusoidal test stimuli with a frequency of 16 kHz and duration of 1 s, following the respective adaptation stimulus. The relative intensities of the test stimuli were −9 dB to +6 dB. Each stimulus was presented at least five times. We constructed intensity-response curves to quantify the neural response, as shown in Figure 4 and Figure 5. Therefore, we used the spike count in a 200 ms time window beginning 100 ms after test stimulus onset. The window was chosen such that the influence of the fast adaptation process (time constant of about 40 ms, similar to the one described for the AN1 neuron by Benda and Hennig [21]) is minimized. In the context of this separation of time scales, we are interested only in the coding of slower stimulus dynamics. Hence we consider responses to unmodulated test stimuli and measured spike counts within a 200 ms—rather than a short—time window. A common methodology to construct neuronal response curves is repeating a single experimental condition several times and then computing the mean of the observed spike counts and their variance. In a second step, a parametric model is fit to these data, typically using least-square approximation. Often it is interesting, however, how the parameters of the response curve change with different experimental conditions, but the confidence intervals for the model parameters and tests for the significance of parameter changes are difficult to establish with traditional statistical methods. Here, we use a Bayesian analysis [39], [40], to account for the statistics of each trial to estimate the parameters accurately and to quantify the confidence limits of the parameter estimation. The method allows estimating the full probability distribution of the response curve parameters rather than only the mean value as with traditional methods. Similar techniques have been applied successfully to the analysis of intracellular membrane potential recordings [41]. The mutual information I[Y; X] between the sensory signal X and the neural response Y specifies how much information is conveyed on average about all possible signals. In order to compute the mutual information numerically, taking into account the influence of discrete, Poisson distributed spike counts, we first construct the joint probability distribution (12) For each stimulus intensity xi, we calculated the corresponding average spike count ri using Equation 5. The distribution P (y|x = xi) is then given by a Poisson distribution with mean ri (Equation 1). For all simulations, the stimulus X was discretized into bins of size 0. 01 dB. At this resolution, the results did not depend on the bin size. To measure the information that is associated with specific sensory signals, we define the stimulus-specific information [42], [43]: (13) where H[X] = −Σx P (x) log2 P (x) is the entropy of the sensory signal X, and the conditional entropy of a particular response y is given by H[X|Y = y] = −Σx P (x|y) log2 P (x|y). Stimulus-specific information can be interpreted as the average reduction of uncertainty about the sensory signal gained from one measurement given the stimulus x. Taking the weighted average over the stimulus-specific information for all possible signals we obtain the mutual information between stimulus and response: (14) To determine the information associated with a certain stimulus range [x1, x2], we evaluate the sum in Equation 14 from x1 to x2.
We first studied the effects of prolonged auditory stimulation in recordings of 6 AN2 neurons of T. oceanicus and 7 AN2 neurons of T. leo. Previously, an adaptation process operating on a time scale of 40 ms had been characterized for the AN1 neuron [21]. Here, we investigate whether adaptation also occurs on a slower time-scale, better matched to changes in the acoustic environments. The infomax principle and the selective coding hypothesis both predict how the neural response curve should optimally change in response to a change in the statistics of the environment. In order to assess the response curve changes quantitatively, we first compute the parameters of the optimal response curve under either hypothesis as well as the mutual information between stimulus and neural response. Figure 9 shows example traces and the amplitude distribution of the bimodal and trimodal sound stimuli together with the corresponding neural responses of a typical AN2 cell (instantaneous firing rate). Adaptation leads to a decrease of the neural responses to 0 dB peak signals (drawn from the high amplitude and intermediate amplitude peak for the bimodal and trimodal distribution) with time. We recorded responses from 25 AN2 cells for the two stimulus paradigms, 12 cells from T. oceanicus and 13 cells from T. leo. Since we found no significant differences in the adaptation and recovery time constants between the two species, we pooled the data from both species together for further analysis. All response curves were quantified using sigmoid input-response functions (cf. Equation 5), and a Bayesian approach was used to determine the distribution of the corresponding parameters A, B50, and C (see Methods, Bayesian data analysis). Some cells did not show response saturation in the trimodal stimulus condition within the range of stimulus intensities we tested. In these cases, the uncertainty of the estimate of parameter A is high, and is reflected by a broad posterior distribution for this parameter. For most of the cells the test stimuli were strong enough to drive the cell to its maximum rate in both conditions. Although the response maximum occurs at higher stimulus intensities in the trimodal condition, we did not observe a systematic change of the saturation response. To fit the response curves, we assumed that the response maximum A has the same value for both stimulus conditions. Five cells were excluded from further analysis because the response curve corresponding to the expected parameter values (posterior means) did not provide a good fit to the data (the model accounted for less than 95% of the variability in the data; R2<0. 95). The further analysis is based on the remaining 20 cells. A representative example of adapted response curves of an AN2 neuron is shown in Figure 10A1, where the input-response function is plotted for the parameters A, B50, and C, which correspond to the expected parameter values (posterior means). After adaptation to the bimodally distributed stimulus (filled symbols), the cell fired with 50% of its maximal rate (parameter B50) at about 1. 75 dB. Adaptation to the trimodally distributed stimulus (open symbols) shifted the response curve to higher stimulus intensities while the slope of the response curve changed only slightly. In fact, the results of the Bayesian parameter estimation, depicted in Figure 10A2, revealed that the response curve parameter B50 significantly increased for the trimodal stimulus distribution. The mean of the posterior density changed from 1. 74 dB to 3. 23 dB (see Methods, Bayesian data analysis for the definition of statistical significance using Bayesian posterior intervals), while there was no significant change for the slope S50 (14% decrease from 0. 160 dB−1). Figure 10B shows data from a second cell. The mean value of the parameter B50 is 3. 47 dB for the bimodally adapted response curve, and increased by 1. 86 dB through adaptation to the trimodally distributed stimulus. The increase of B50 was again significant. The slope increased by 15% (from 0. 161 dB−1 for adaptation to the bimodal stimulus) but Bayesian analysis revealed that the increase in slope was not significant. Figure 10C shows data from a third cell. This cell showed a significant albeit less pronounced change in parameter B50 of +1. 06 dB accompanied by a significant decrease in the slope S50 (decrease of the posterior mean by 23. 5%).
In the cricket auditory system, time scales of adaptation observed at first level interneurons range from short (below 100 ms, AN1: [21]) over intermediate (ca. 300 ms; AN2: [22], receptors: [19]) to long time constants (ca. 10 s; AN2: [22], receptors: [19]). In the present study, we report firing rate adaptation with a time constant of about one second not reported before in the auditory ascending neuron AN2 of T. oceanicus and T. leo (Figure 6 and Table 1). At present the origin of adaptation in this small network is not known. There is likely a contribution to adaptation from the receptor neurons [19]. At the level of interneurons, both local cells (ON1, [11], [20]) as well as the ascending interneuron AN2 [22] exhibit long-lasting hyperpolarizations with intermediate time constants (approximately 5 s) that may reflect adaptation processes at the level of the spike-generator in these cells. The primary task of the first stage of auditory processing in crickets is to maintain and possibly to condense relevant information for object localization and recognition for higher computational centers in the brain. In this context, it is remarkable to note that already at the first synapse several time scales of adaptation can be observed, similar to those reported from vertebrate systems [36] as well as other sensory modalities [6]. The adaptation time scale we report here seems ideal for an adjustment of the coding scheme to the current sensory environment. However, we find no enhancement of information transfer in the neuron under study. We thus report the unusual case that adaptation seems to rather selectively suppress sensory coding instead of improving it. We characterized the neural response using a sigmoid response curve and estimated the model parameters from the measured spike counts using a Bayesian approach. An important feature of the analysis is that it yields the joint posterior distribution of the model parameters which allows us to calculate the posterior distribution and precise confidence limits of derived quantities such as mutual information (see [47] for the pitfalls of entropy estimation from undersampled discrete data such as ‘spike words’). The Bayesian framework can also be applied to other experimental paradigms. One limitation of the approach presented here is that the calculation of the joint posterior density at a grid of points is only feasible for models with few parameters. However, for more complex models, approximations of the posterior densities can be obtained using Markov chain simulation [39]. We measured how adaptation changes the response curves of AN2 cells depending on different stimulus conditions (Figure 10) and found that the changes are not compatible with infomax (optimal coding of the entire stimulus range). In general, our results indicate a response threshold that is too high to allow for reliable encoding of the whole stimulus range (Figures 10 and 11). Most remarkably, adaptation reduced the amount of encoded information about the stimulus when considering the whole range of input signals (Figure 13A). This is in contrast to other studies (fly visual system [6], [23]; midbrain of guinea pigs [5]; inferior colliculus of cats [48]; songbird auditory forebrain [8]; rat barrel cortex [7]) that reported that stimulus encoding is compatible with infomax. However, the infomax principle, which considers sensory systems as communication channels that are optimized for preserving all information from the sensory input, may fail to explain neural coding when considering stages where actual processing of information takes place (instead of mere transmission). Indeed, a recent study by Ringach and Malone [49] has shown that neurons in the primary visual cortex of macaque maintain an operating point that does not maximize information transmission but is tuned to the detection of signals in background noise. We also tested if the response curve changes induced by adaptation are compatible with selective coding (reliable coding of the most intense signal while suppressing the ‘background’). Selective coding can be seen as the simplest form of separation of neural representations of discrete objects in multiple channels or ‘streams’ that has been found in higher auditory processing levels in vertebrates [35]. Neurons in the inferior colliculus of cats display the same firing pattern when a stimulus composed of a signal with or without background noise is presented, indicating a representation of the signal only [50], and in auditory cortex, neurons show locking to the amplitude modulations of a low level sound but not to the noise it is embedded in [34]. In insects, the principle has been found in bushcrickets, separating single males from background choruses [33] and has been suggested to be at work in crickets as well [20]. However, the selective coding principle is not clearly defined in an information theoretical framework. Here, we formalize the hypothesis and accordingly make two predictions about the change of information transfer with adaptation: (1) information conveyed about the ‘background’ should decrease, while (2) transmission for the high-intensity signals should increase. Indeed, we find that the neural output of the AN2 conveys less information about the first two peaks (‘background’) of the stimulus distribution (Figure 13B), supporting the selective coding hypothesis. However, if we take only the mutual information between the loudest part of the signal and the neural response into account, information rate remains nearly constant (Figure 13C). This contradicts the selective coding hypothesis. Surprisingly, our results suggest that instead of improving sensory coding, adaptation in the AN2 decreases information transmission and leaves higher processing centers in the cricket brain with less (or at most equal) information, regardless of what part of the stimulus is considered. What could be the reason for this? Adaptation leads to a selective decrease in mutual information for the low-intensity sounds, mainly by shifting the stimulus-response curves towards higher stimulus intensities. As a consequence, spike counts are reduced for low-intensity signals (cf. Figure 10). Through adaptation to the trimodal stimulus, average spike counts in response to the 0 dB test stimulus decreased for 18 of 20 cells (mean decrease: 42%, standard deviation: 31%) compared to the spike counts after adaptation to the bimodal stimulus. Thus, in an ecological setting, where background signals are present and foreground signals are changing their presence dynamically, background signals transmitted to downstream neurons by the AN2 will be reduced. We speculate that this might reduce the potential interference of ‘background’ and ‘foreground’ spikes in downstream processing. However, the observed response curves do not represent an optimal solution for the task of filtering out the most intense part of the stimulus. Additionally, the algorithm behind adaptation could serve the goal of enhancing the representation of even louder signals, occurring with less probability. Examples, in which optimal coding is not used to maximize the average information gained about high probability stimuli include auditory receptors of locusts which seem to maximize the information gained about specific, but less often occurring aspects of the stimuli [51] and stimulus specific adaptation in single neurons of auditory cortex that leads to an enhanced representation of low-probability sounds deviating from the distribution of the surrounding signal [36]. This can be seen as a form of novelty detection, where part of the dynamic range is preserved for even louder sounds in a way that the sensory pathway is always able to detect brief, transient high-intensity signals [49]. In order to test if the representation of loud signals is enhanced, we calculated information transmission for a signal distribution that has an additional peak (modeled by a fourth Gaussian distribution with mean μ4 = +6 dB). Indeed, we find that adaptation increases mutual information in the stimulus range from 4. 5 dB to 7. 5 dB for all cells. This increase is significant in 6 of 20 cells, but it is not statistically significant on the population level (the 95% interval of the combined posterior density ranges from −0. 015 bit to 0. 243 bit). In this context, it should also be noted that the AN2 neuron in crickets may serve several functions. Under most stimulus conditions, relatively low firing rates will likely monitor slowly changing signals as observed in the present study (up to about 5 Hz). The AN2, however, can also operate in a burst mode with high intra-burst firing rates for the detection of bat calls [16] for which our analysis is not appropriate. Nevertheless, low firing rates are likely to transmit relevant information since input-response curves built from spike counts similar to those in the present study are maintained at somewhat higher thresholds in wingless cricket morphs that are not at risk from bat predation [18]. In addition to its relevance for slow signal features, the adaptation time course reported here is likely to adjust the operating point of the faster response dynamics (i. e. , bursts). Apart from possible physiological limitation, the findings we report here could be the result of a trade-off between setting the operating regime for the bursting mode on the one hand and suppression of background noise on slower time scales on the other hand. Generally, a neural system may achieve improved performance by means of different mechanisms as has been shown in a recent modeling study [52]. Depending on the specific physiological constraints, the resulting neural representation can be optimal or a trade-off between optimality and the flexibility of the neural circuit. Indeed, we found that, for example, the slope of the stimulus-response curve is not steep enough for optimal encoding of only the loudest peak of the bimodal or trimodal stimulus distribution. Possibly the neural gain can only increase to a limited value, leading to a decrease—rather than an increase—in information transmission for the chosen experimental paradigm. Although we cannot give a conclusive answer on what the adaptation-induced selective suppression in the AN2 serves for yet, the paradigm we propose here is rather general and should be applicable to other sensory systems as well. Importantly, our paradigm allows quantifying the improvement in neural coding without requiring that neural response curves achieve optimality. Measuring the information between the proposed relevant stimulus and the neural output allows testing for different hypotheses on what a sensory pathway actually adapts to. Ultimately, testing various hypotheses on different stimulus ensembles will yield important insights on what is or what is not the relevant part of a sensory environment for a given sensory unit.
Sensory systems have the ability to adapt to changes in the environment. In a quiet room, the nervous system is very responsive, so that even a whisper can be easily understood. In contrast, the perceived loudness on a crowded street will be reduced to prevent an overload of the nervous system. Two different hypotheses have been proposed to explain how the nervous system achieves this adaptation. According to one idea, all present sensory signals are equally enhanced, so that the whole range of input signals is reliably represented. On the other hand, the aim of the nervous system may be to extract the most important parts of the acoustic signal, for example, an approaching car, and thus abolish the irrelevant rest. To address which of these two principles is implemented in the auditory system of the cricket, we investigated the responses of a single auditory neuron, called interneuron AN2, to different sound signals. We found that adaptation actually reduces the amount of encoded information when considering the whole range of input signals. However, the changes were also not in agreement with the idea that only the most important signal is transmitted, because the amount of information conveyed about the loudest part of the signal does not increase. Thus, we here report the unusual case of a reduction of information transfer by adaptation, while in most other systems reported of so far adaptation actually enhances coding of sensory information.
neuroscience/theoretical neuroscience
neuroscience/sensory systems
computational biology/computational neuroscience
Adaptation and Selective Information Transmission in the Cricket Auditory Neuron AN2