Sandra Wohlgemuth

Efficient transformation of an auditory population code in a small sensory system

Optimal coding principles are implemented in many large sensory systems. They include the systematic transformation of external stimuli into a sparse and decorrelated neuronal representation, enabling a flexible readout of stimulus properties. Are these principles also applicable to size-constrained systems, which have to rely on a limited number of neurons and may only have to fulfill specific and restricted tasks? We studied this question in an insect system—the early auditory pathway of grasshoppers. Grasshoppers use genetically fixed songs to recognize mates. The first steps of neural processing of songs take place in a small three-layer feed-forward network comprising only a few dozen neurons. We analyzed the transformation of the neural code within this network. Indeed, grasshoppers create a decorrelated and sparse representation, in accordance with optimal coding theory. Whereas the neuronal input layer is best read out as a summed population, a labeled-line population code for temporal features of the song is established after only two processing steps. At this stage, information about song identity is maximal for a population decoder that preserves neuronal identity. We conclude that optimal coding principles do apply to the early auditory system of the grasshopper, despite its size constraints. The inputs, however, are not encoded in a systematic, map-like fashion as in many larger sensory systems. Already at its periphery, part of the grasshopper auditory system seems to focus on behaviorally relevant features, and is in this property more reminiscent of higher sensory areas in vertebrates.

Variability of spike trains and the processing of temporal patterns of acoustic signals—problems, constraints, and solutions

Object recognition and classification by sensory pathways is rooted in spike trains provided by sensory neurons. Nervous systems had to evolve mechanisms to extract information about relevant object properties, and to separate these from spurious features. In this review, problems caused by spike train variability and counterstrategies are exemplified for the processing of acoustic signals in orthopteran insects. Due to size limitations of their nervous system we expect to find solutions that are stripped to the computational basics. A key feature of auditory systems is temporal resolution, which is likely limited by spike train variability. Basic strategies to reduce such variability are to integrate over time, or to average across several neurons. The first strategy is constrained by its possible interference with temporal resolution. Grasshoppers do not seem to explore temporal integration much, in spite of the repetitive structure of their songs, which invites for ‘multiple looks’ at the signal. The benefits of averaging across neurons depend on uncorrelated responses, a factor that may be crucial for the performance and evolution of small nervous systems. In spite of spike train variability the temporal information necessary for the recognition of conspecifics is preserved to a remarkable degree in the auditory pathway.

Evolutionarily conserved coding properties of auditory neurons across grasshopper species

We investigated encoding properties of identified auditory interneurons in two not closely related grasshopper species (Acrididae). The neurons can be homologized on the basis of their similar morphologies and physiologies. As test stimuli, we used the species-specific stridulation signals of Chorthippus biguttulus, which evidently are not relevant for the other species, Locusta migratoria. We recorded spike trains produced in response to these signals from several neuron types at the first levels of the auditory pathway in both species. Using a spike train metric to quantify differences between neuronal responses, we found a high similarity in the responses of homologous neurons: interspecific differences between the responses of homologous neurons in the two species were not significantly larger than intraspecific differences (between several specimens of a neuron in one species). These results suggest that the elements of the thoracic auditory pathway have been strongly conserved during the evolutionary divergence of these species. According to the ‘efficient coding’ hypothesis, an adaptation of the thoracic auditory pathway to the specific needs of acoustic communication could be expected. We conclude that there must have been stabilizing selective forces at work that conserved coding characteristics and prevented such an adaptation.

Nonlinear Computations Underlying Temporal and Population Sparseness in the Auditory System of the Grasshopper

Sparse coding schemes are employed by many sensory systems and implement efficient coding principles. Yet, the computations yielding sparse representations are often only partly understood. The early auditory system of the grasshopper produces a temporally and population-sparse representation of natural communication signals. To reveal the computations generating such a code, we estimated 1D and 2D linear-nonlinear models. We then used these models to examine the contribution of different model components to response sparseness. 2D models were better able to reproduce the sparseness measured in the system: while 1D models only captured 55% of the population sparseness at the networks output, 2D models accounted for 88% of it. Looking at the model structure, we could identify two types of computation, which increase sparseness. First, a sensitivity to the derivative of the stimulus and, second, the combination of a fast, excitatory and a slow, suppressive feature. Both were implemented in different classes of cells and increased the specificity and diversity of responses. The two types produced more transient responses and thereby amplified temporal sparseness. Additionally, the second type of computation contributed to population sparseness by increasing the diversity of feature selectivity through a wide range of delays between an excitatory and a suppressive feature. Both kinds of computation can be implemented through spike-frequency adaptation or slow inhibition—mechanisms found in many systems. Our results from the auditory system of the grasshopper are thus likely to reflect general principles underlying the emergence of sparse representations.

FoxP2 in songbirds

Humans with mutations in the transcription factor FOXP2 display a severe speech disorder. Songbirds are a powerful model system to study FoxP2. Like humans, songbirds communicate via vocalizations that are imitatively learned during critical periods and this learning is influenced by social factors and relies on functionally lateralized neural circuits. During the past five years significant progress has been made moving from a descriptive to a more mechanistic understanding of how FoxP2 functions in songbirds. Current evidence from molecular and electrophysiological studies indicates that FoxP2 is important for shaping synaptic plasticity of specific neuron populations. One future goal will be to identify the transcriptional regulation orchestrated by FoxP2 and its associated molecular network that brings about these physiological effects. This will be key to further unravel how FoxP2 influences synaptic function and thereby contributes to auditory guided vocal motor behavior in the songbird model.

Encoding of amplitude modulations by auditory neurons of the locust: influence of modulation frequency, rise time, and modulation depth.

Using modulation transfer functions (MTF), we investigated how sound patterns are processed within the auditory pathway of grasshoppers. Spike rates of auditory receptors and primary-like local neurons did not depend on modulation frequencies while other local and ascending neurons had lowpass, bandpass or bandstop properties. Local neurons exhibited broader dynamic ranges of their rate MTF that extended to higher modulation frequencies than those of most ascending neurons. We found no indication that a filter bank for modulation frequencies may exist in grasshoppers as has been proposed for the auditory system of mammals. The filter properties of half of the neurons changed to an allpass type with a 50% reduction of modulation depths. Contrasting to reports for mammals, the sensitivity to small modulation depths was not enhanced at higher processing stages. In ascending neurons, a focus on the range of low modulation frequencies was visible in the temporal MTFs, which describe the temporal locking of spikes to the signal envelope. To investigate the influence of stimulus rise time, we used rectangularly modulated stimuli instead of sinusoidally modulated ones. Unexpectedly, steep stimulus onsets had only small influence on the shape of MTF curves of 70% of neurons in our sample.

Discrimination of acoustic communication signals by grasshoppers (Chorthippus biguttulus): Temporal resolution, temporal integration, and the impact of intrinsic noise.

A characteristic feature of hearing systems is their ability to resolve both fast and subtle amplitude modulations of acoustic signals. This applies also to grasshoppers, which for mate identification rely mainly on the characteristic temporal patterns of their communication signals. Usually the signals arriving at a receiver are contaminated by various kinds of noise. In addition to extrinsic noise, intrinsic noise caused by stochastic processes within the nervous system contributes to making signal recognition a difficult task. The authors asked to what degree intrinsic noise affects temporal resolution and, particularly, the discrimination of similar acoustic signals. This study aims at exploring the neuronal basis for sexual selection, which depends on exploiting subtle differences between basically similar signals. Applying a metric, by which the similarities of spike trains can be assessed, the authors investigated how well the communication signals of different individuals of the same species could be discriminated and correctly classified based on the responses of auditory neurons. This spike train metric yields clues to the optimal temporal resolution with which spike trains should be evaluated.

Timescale-Invariant Representation of Acoustic Communication Signals by a Bursting Neuron

Acoustic communication often involves complex sound motifs in which the relative durations of individual elements, but not their absolute durations, convey meaning. Decoding such signals requires an explicit or implicit calculation of the ratios between time intervals. Using grasshopper communication as a model, we demonstrate how this seemingly difficult computation can be solved in real time by a small set of auditory neurons. One of these cells, an ascending interneuron, generates bursts of action potentials in response to the rhythmic syllable–pause structure of grasshopper calls. Our data show that these bursts are preferentially triggered at syllable onset; the number of spikes within the burst is linearly correlated with the duration of the preceding pause. Integrating the number of spikes over a fixed time window therefore leads to a total spike count that reflects the characteristic syllable-to-pause ratio of the species while being invariant to playing back the call faster or slower. Such a timescale-invariant recognition is essential under natural conditions, because grasshoppers do not thermoregulate; the call of a sender sitting in the shade will be slower than that of a grasshopper in the sun. Our results show that timescale-invariant stimulus recognition can be implemented at the single-cell level without directly calculating the ratio between pulse and interpulse durations.

Timescale-invariant pattern recognition by feedforward inhibition and parallel signal processing

The timescale-invariant recognition of temporal stimulus sequences is vital for many species and poses a challenge for their sensory systems. Here we present a simple mechanistic model to address this computational task, based on recent observations in insects that use rhythmic acoustic communication signals for mate finding. In the model framework, feedforward inhibition leads to burst-like response patterns in one neuron of the circuit. Integrating these responses over a fixed time window by a readout neuron creates a timescale-invariant stimulus representation. Only two additional processing channels, each with a feature detector and a readout neuron, plus one final coincidence detector for all three parallel signal streams, are needed to account for the behavioral data. In contrast to previous solutions to the general time-warp problem, no time delay lines or sophisticated neural architectures are required. Our results suggest a new computational role for feedforward inhibition and underscore the power of parallel signal processing.

Optimal sparse coding of song in a size-constrained auditory system?

Optimal coding theory has been successfully applied to understand the principles of organization in many sensory systems. However, these systems were usually large and relatively generic stimulus encoders, like the early visual and auditory system of vertebrates or the early olfactory system in insects [1-3]. Do the principles of optimal coding also apply to small sensory systems with a specialized task and a restricted set of relevant stimuli? We studied the early auditory system of grasshoppers as an example for such a sensory system. These insects use genetically fixed songs to recognize mates with high fidelity. First steps of the processing of song take place in a 3-layer feed-forward network consisting of only a few dozen neurons. Analyzing the transformation of the neural code for song in grasshoppers, we find that a temporally sparse representation of song is created. Additionally, responses of populations of cells in each of the three network layers get more diverse due to a higher diversity in the stimulus selectivity. That is, while neurons in the first two layers are selective for very similar temporal features of the song, each neuron in the network’s output stage responds to a specific temporal feature. This transformation has implications for the population code in the network: By asking whether a population decoder needs to incorporate information about which neuron fired which spike to discriminate stimuli optimally, we find that neuronal identity becomes more and more important for an effective read out of the population the higher one ascen^gds in the network [4]. Thereby, an explicit, labeled-line like population code for temporal features of the song is created: This means that each neuron in the output layer signals the presence or absence of a specific temporal feature by the presence or absence of spikes in its response. In contrart, preceding layers encode temporal features implicitly by the temporal patterns of spikes. Although the creation of a sparse, labeled-line like code resembles the transformations happening in large sensory systems, the small size and restricted task of the early auditory system of grasshoppers leads us to a different conclusion about the objective of these transformations. Early sensory areas in mammals like V1 encode the stimulus largely comprehensively – they filter the stimulus only little according to behavioral relevance as these networks serve as hubs which distribute information to more specific processing stages. In contrast, the grasshoppers’ songs have a genetically fixed structure, freeing the animals from the task to learn the significance of a stimulus feature during life time. This allows grasshoppers to hard-wire and specialize their representation early in the sensory pathway, leading to a compression of the stimulus representation based on behavioral relevance. We have evidence that some of the neurons at the output of the network indeed do encode stimulus features directly related to behavior. Despite being at the very beginning in the grasshopper’s auditory pathway, this representation might thus be similar to higher order areas in vertebrates, as it produces specific representations of behaviorally relevant features.