Increase of neuronal response variability at higher processing levels as revealed by simultaneous recordings

Lateral axonal modulation is required for stimulus-specific olfactory conditioning in Drosophila

Effective and stimulus-specific learning is essential for animals' survival. Two major mechanisms are known to aid stimulus specificity of associative learning. One is accurate stimulus-specific representations in neurons. The second is a limited effective temporal window for the reinforcing signals to induce neuromodulation after sensory stimuli. However, these mechanisms are often imperfect in preventing unspecific associations; different sensory stimuli can be represented by overlapping populations of neurons, and more importantly, the reinforcing signals alone can induce neuromodulation even without coincident sensory-evoked neuronal activity. Here, we report a crucial neuromodulatory mechanism that counteracts both limitations and is thereby essential for stimulus specificity of learning. In Drosophila, olfactory signals are sparsely represented by cholinergic Kenyon cells (KCs), which receive dopaminergic reinforcing input. We find that KCs have numerous axo-axonic connections mediated by the muscarinic type-B receptor (mAChR-B). By using functional imaging and optogenetic approaches, we show that these axo-axonic connections suppress both odor-evoked calcium responses and dopamine-evoked cAMP signals in neighboring KCs. Strikingly, behavior experiments demonstrate that mAChR-B knockdown in KCs impairs olfactory learning by inducing undesired changes to the valence of an odor that was not associated with the reinforcer. Thus, this local neuromodulation acts in concert with sparse sensory representations and global dopaminergic modulation to achieve effective and accurate memory formation.

Tracking the Effect of Therapy With Single-Trial Based Classification After Stroke

Stroke is a debilitating disease that leads, in the 50% of cases, to permanent motor or cognitive impairments. The effectiveness of therapies that promote recovery after stroke depends on indicators of the disease state that can measure the degree of recovery or predict treatment response or both. Here, we propose to use single-trial classification of task dependent neural activity to assess the disease state and track recovery after stroke. We tested this idea on calcium imaging data of the dorsal cortex of healthy, spontaneously recovered and rehabilitated mice while performing a forelimb retraction task. Results show that, at a single-trial level for the three experimental groups, neural activation during the reward pull can be detected with high accuracy with respect to the background activity in all cortical areas of the field of view and this activation is quite stable across trials and subjects of the same group. Moreover, single-trial responses during the reward pull can be used to discriminate between healthy and stroke subjects with areas closer to the injury site displaying higher discrimination capability than areas closer to this site. Finally, a classifier built to discriminate between controls and stroke at the single-trial level can be used to generate an index of the disease state, the therapeutic score, which is validated on the group of rehabilitated mice. In conclusion, task-related neural activity can be used as an indicator of disease state and track recovery without selecting a peculiar feature of the neural responses. This novel method can be used in both the development and assessment of different therapeutic strategies.

Neurophysiology goes wild: from exploring sensory coding in sound proof rooms to natural environments

To perform adaptive behaviours, animals have to establish a representation of the physical "outside" world. How these representations are created by sensory systems is a central issue in sensory physiology. This review addresses the history of experimental approaches toward ideas about sensory coding, using the relatively simple auditory system of acoustic insects. I will discuss the empirical evidence in support of Barlow's "efficient coding hypothesis", which argues that the coding properties of neurons undergo specific adaptations that allow insects to detect biologically important acoustic stimuli. This hypothesis opposes the view that the sensory systems of receivers are biased as a result of their phylogeny, which finally determine whether a sound stimulus elicits a behavioural response. Acoustic signals are often transmitted over considerable distances in complex physical environments with high noise levels, resulting in degradation of the temporal pattern of stimuli, unpredictable attenuation, reduced signal-to-noise levels, and degradation of cues used for sound localisation. Thus, a more naturalistic view of sensory coding must be taken, since the signals as broadcast by signallers are rarely equivalent to the effective stimuli encoded by the sensory system of receivers. The consequences of the environmental conditions for sensory coding are discussed.

Modified synaptic dynamics predict neural activity patterns in an auditory field within the frontal cortex

Frontal areas of the mammalian cortex are thought to be important for cognitive control and complex behaviour. These areas have been studied mostly in humans, non-human primates and rodents. In this article, we present a quantitative characterization of response properties of a frontal auditory area responsive to sound in the brain of Carollia perspicillata, the frontal auditory field (FAF). Bats are highly vocal animals, and they constitute an important experimental model for studying the auditory system. We combined electrophysiology experiments and computational simulations to compare the response properties of auditory neurons found in the bat FAF and auditory cortex (AC) to simple sounds (pure tones). Anatomical studies have shown that the latter provides feedforward inputs to the former. Our results show that bat FAF neurons are responsive to sounds, and however, when compared to AC neurons, they presented sparser, less precise spiking and longer-lasting responses. Based on the results of an integrate-and-fire neuronal model, we suggest that slow, subthreshold, synaptic dynamics can account for the activity pattern of neurons in the FAF. These properties reflect the general function of the frontal cortex and likely result from its connections with multiple brain regions, including cortico-cortical projections from the AC to the FAF.

Overexpression of cypin alters dendrite morphology, single neuron activity, and network properties via distinct mechanisms

OBJECTIVE

This study investigates the effect that overexpression of cytosolic PSD-95 interactor (cypin), a regulator of synaptic PSD-95 protein localization and a core regulator of dendrite branching, exerts on the electrical activity of rat hippocampal neurons and networks.

APPROACH

We cultured rat hippocampal neurons and used lipid-mediated transfection and lentiviral gene transfer to achieve high levels of cypin or cypin mutant (cypinΔPDZ; PSD-95 non-binding) expression cellularly and network-wide, respectively.

MAIN RESULTS

Our analysis revealed that although overexpression of cypin and cypinΔPDZ increase dendrite numbers and decrease spine density, cypin and cypinΔPDZ distinctly regulate neuronal activity. At the single cell level, cypin promotes decreases in bursting activity while cypinΔPDZ reduces sEPSC frequency and further decreases bursting compared to cypin. At the network level, by using the Fano factor as a measure of spike count variability, cypin overexpression results in an increase in variability of spike count, and this effect is abolished when cypin cannot bind PSD-95. This variability is also dependent on baseline activity levels and on mean spike rate over time. Finally, our spike sorting data show that overexpression of cypin results in a more complex distribution of spike waveforms and that binding to PSD-95 is essential for this complexity.

SIGNIFICANCE

Our data suggest that dendrite morphology does not play a major role in cypin action on electrical activity.

Innate releasing mechanisms and fixed action patterns: basic ethological concepts as drivers for neuroethological studies on acoustic communication in Orthoptera

This review addresses the history of neuroethological studies on acoustic communication in insects. One objective is to reveal how basic ethological concepts developed in the 1930s, such as innate releasing mechanisms and fixed action patterns, have influenced the experimental and theoretical approaches to studying acoustic communication systems in Orthopteran insects. The idea of innateness of behaviors has directly fostered the search for central pattern generators that govern the stridulation patterns of crickets, katydids or grasshoppers. A central question pervading 50 years of research is how the essential match between signal features and receiver characteristics has evolved and is maintained during evolution. As in other disciplines, the tight interplay between technological developments and experimental and theoretical advances becomes evident throughout this review. While early neuroethological studies focused primarily on proximate questions such as the implementation of feature detectors or central pattern generators, later the interest shifted more towards ultimate questions. Orthoptera offer the advantage that both proximate and ultimate questions can be tackled in the same system. An important advance was the transition from laboratory studies under well-defined acoustic conditions to field studies that allowed to measure costs and benefits of acoustic signaling as well as constraints on song evolution.

Temporal integration of conflicting directional cues in sound localization

Sound localization is fundamental to hearing. In nature, sound degradation and noise erode directional cues and can generate conflicting directional perceptions across different subcomponents of sounds. Little is known about how sound localization is achieved in the face of conflicting directional cues in non-human animals, although this is relevant for many species in which sound localization in noisy conditions mediates mate finding or predator avoidance. We studied the effects of conflicting directional cues in male grasshoppers, Chorthippus biguttulus, which orient towards signaling females. We presented playbacks varying in the number and temporal position of song syllables providing directional cues in the form of either time or amplitude differences between two speakers. Males oriented towards the speaker broadcasting a greater number of leading or louder syllables. For a given number of syllables providing directional information, syllables with timing differences at the song's beginning were weighted most heavily, while syllables with intensity differences were weighted most heavily when they were in the middle of the song. When timing and intensity cues conflicted, the magnitude and temporal position of each cue determined their relative influence on lateralization, and males sometimes quickly corrected their directional responses. We discuss our findings with respect to similar results from humans.

Brain regions for sound processing and song release in a small grasshopper

We investigated brain regions - mostly neuropils - that process auditory information relevant for the initiation of response songs of female grasshoppers Chorthippus biguttulus during bidirectional intraspecific acoustic communication. Male-female acoustic duets in the species Ch. biguttulus require the perception of sounds, their recognition as a species- and gender-specific signal and the initiation of commands that activate thoracic pattern generating circuits to drive the sound-producing stridulatory movements of the hind legs. To study sensory-to-motor processing during acoustic communication we used multielectrodes that allowed simultaneous recordings of acoustically stimulated electrical activity from several ascending auditory interneurons or local brain neurons and subsequent electrical stimulation of the recording site. Auditory activity was detected in the lateral protocerebrum (where most of the described ascending auditory interneurons terminate), in the superior medial protocerebrum and in the central complex, that has previously been implicated in the control of sound production. Neural responses to behaviorally attractive sound stimuli showed no or only poor correlation with behavioral responses. Current injections into the lateral protocerebrum, the central complex and the deuto-/tritocerebrum (close to the cerebro-cervical fascicles), but not into the superior medial protocerebrum, elicited species-typical stridulation with high success rate. Latencies and numbers of phrases produced by electrical stimulation were different between these brain regions. Our results indicate three brain regions (likely neuropils) where auditory activity can be detected with two of these regions being potentially involved in song initiation.

Flexible models for spike count data with both over- and under- dispersion

A key observation in systems neuroscience is that neural responses vary, even in controlled settings where stimuli are held constant. Many statistical models assume that trial-to-trial spike count variability is Poisson, but there is considerable evidence that neurons can be substantially more or less variable than Poisson depending on the stimuli, attentional state, and brain area. Here we examine a set of spike count models based on the Conway-Maxwell-Poisson (COM-Poisson) distribution that can flexibly account for both over- and under-dispersion in spike count data. We illustrate applications of this noise model for Bayesian estimation of tuning curves and peri-stimulus time histograms. We find that COM-Poisson models with group/observation-level dispersion, where spike count variability is a function of time or stimulus, produce more accurate descriptions of spike counts compared to Poisson models as well as negative-binomial models often used as alternatives. Since dispersion is one determinant of parameter standard errors, COM-Poisson models are also likely to yield more accurate model comparison. More generally, these methods provide a useful, model-based framework for inferring both the mean and variability of neural responses.

Response recovery in the locust auditory pathway

Temporal resolution and the time courses of recovery from acute adaptation of neurons in the auditory pathway of the grasshopper Locusta migratoria were investigated with a response recovery paradigm. We stimulated with a series of single click and click pair stimuli while performing intracellular recordings from neurons at three processing stages: receptors and first and second order interneurons. The response to the second click was expressed relative to the single click response. This allowed the uncovering of the basic temporal resolution in these neurons. The effect of adaptation increased with processing layer. While neurons in the auditory periphery displayed a steady response recovery after a short initial adaptation, many interneurons showed nonlinear effects: most prominent a long-lasting suppression of the response to the second click in a pair, as well as a gain in response if a click was preceded by a click a few milliseconds before. Our results reveal a distributed temporal filtering of input at an early auditory processing stage. This set of specified filters is very likely homologous across grasshopper species and thus forms the neurophysiological basis for extracting relevant information from a variety of different temporal signals. Interestingly, in terms of spike timing precision neurons at all three processing layers recovered very fast, within 20 ms. Spike waveform analysis of several neuron types did not sufficiently explain the response recovery profiles implemented in these neurons, indicating that temporal resolution in neurons located at several processing layers of the auditory pathway is not necessarily limited by the spike duration and refractory period.

Multielectrode recordings from auditory neurons in the brain of a small grasshopper

BACKGROUND

Grasshoppers have been used as a model system to study the neuronal basis of insect acoustic behavior. Auditory neurons have been described from intracellular recordings. The growing interest to study population activity of neurons has been satisfied so far with artificially combining data from different individuals.

NEW METHOD

We for the first time used multielectrode recordings from a small grasshopper brain. We used three 12μm tungsten wires (combined in a multielectrode) to record from local brain neurons and from a population of auditory neurons entering the brain from the thorax. Spikes of the recorded units were separated by sorting algorithms and spike collision analysis.

RESULTS

The tungsten wires enabled stable recordings with high signal to noise ratio. Due to the tight temporal coupling of auditory activity to the stimulus spike collisions were frequent and collision analysis retrieved 10-15% of additional spikes. Marking the electrode position was possible using a fluorescent dye or electrocoagulation with high current. Physiological identification of units described from intracellular recordings was hard to achieve.

COMPARISON WITH EXISTING METHODS

12μm tungsten wires gave a better signal to noise ratio than 15μm copper wires previously used in recordings from bees' brains. Recording the population activity of auditory neurons in one individual prevents interindividual and trial-to-trial variability which otherwise reduce the validity of the analysis. Double intracellular recordings have quite low success rate and therefore are rarely achieved and their stability is much lower than that of multielectrode recordings which allows sampling of data for 30min or more.

Temporal integration at consecutive processing stages in the auditory pathway of the grasshopper

Temporal integration in the auditory system of locusts was quantified by presenting single clicks and click pairs while performing intracellular recordings. Auditory neurons were studied at three processing stages, which form a feed-forward network in the metathoracic ganglion. Receptor neurons and most first-order interneurons ("local neurons") encode the signal envelope, while second-order interneurons ("ascending neurons") tend to extract more complex, behaviorally relevant sound features. In different neuron types of the auditory pathway we found three response types: no significant temporal integration (some ascending neurons), leaky energy integration (receptor neurons and some local neurons), and facilitatory processes (some local and ascending neurons). The receptor neurons integrated input over very short time windows (<2 ms). Temporal integration on longer time scales was found at subsequent processing stages, indicative of within-neuron computations and network activity. These different strategies, realized at separate processing stages and in parallel neuronal pathways within one processing stage, could enable the grasshopper's auditory system to evaluate longer time windows and thus to implement temporal filters, while at the same time maintaining a high temporal resolution.

Neural representation of calling songs and their behavioral relevance in the grasshopper auditory system

Acoustic communication plays a key role for mate attraction in grasshoppers. Males use songs to advertise themselves to females. Females evaluate the song pattern, a repetitive structure of sound syllables separated by short pauses, to recognize a conspecific male and as proxy to its fitness. In their natural habitat females often receive songs with degraded temporal structure. Perturbations may, for example, result from the overlap with other songs. We studied the response behavior of females to songs that show different signal degradations. A perturbation of an otherwise attractive song at later positions in the syllable diminished the behavioral response, whereas the same perturbation at the onset of a syllable did not affect song attractiveness. We applied naïve Bayes classifiers to the spike trains of identified neurons in the auditory pathway to explore how sensory evidence about the acoustic stimulus and its attractiveness is represented in the neuronal responses. We find that populations of three or more neurons were sufficient to reliably decode the acoustic stimulus and to predict its behavioral relevance from the single-trial integrated firing rate. A simple model of decision making simulates the female response behavior. It computes for each syllable the likelihood for the presence of an attractive song pattern as evidenced by the population firing rate. Integration across syllables allows the likelihood to reach a decision threshold and to elicit the behavioral response. The close match between model performance and animal behavior shows that a spike rate code is sufficient to enable song pattern recognition.

Computational themes of peripheral processing in the auditory pathway of insects

Hearing in insects serves to gain information in the context of mate finding, predator avoidance or host localization. For these goals, the auditory pathways of insects represent the computational substrate for object recognition and localization. Before these higher level computations can be executed in more central parts of the nervous system, the signals need to be preprocessed in the auditory periphery. Here, we review peripheral preprocessing along four computational themes rather than discussing specific physiological mechanisms: (1) control of sensitivity by adaptation, (2) recoding of amplitude modulations of an acoustic signal into a labeled-line code (3) frequency processing and (4) conditioning for binaural processing. Along these lines, we review evidence for canonical computations carried out in the peripheral auditory pathway and show that despite the vast diversity of insect hearing, signal processing is governed by common computational motifs and principles.

Computational principles underlying recognition of acoustic signals in grasshoppers and crickets

Grasshoppers and crickets independently evolved hearing organs and acoustic communication. They differ considerably in the organization of their auditory pathways, and the complexity of their songs, which are essential for mate attraction. Recent approaches aimed at describing the behavioral preference functions of females in both taxa by a simple modeling framework. The basic structure of the model consists of three processing steps: (1) feature extraction with a bank of 'LN models'-each containing a linear filter followed by a nonlinearity, (2) temporal integration, and (3) linear combination. The specific properties of the filters and nonlinearities were determined using a genetic learning algorithm trained on a large set of different song features and the corresponding behavioral response scores. The model showed an excellent prediction of the behavioral responses to the tested songs. Most remarkably, in both taxa the genetic algorithm found Gabor-like functions as the optimal filter shapes. By slight modifications of Gabor filters several types of preference functions could be modeled, which are observed in different cricket species. Furthermore, this model was able to explain several so far enigmatic results in grasshoppers. The computational approach offered a remarkably simple framework that can account for phenotypically rather different preference functions across several taxa.

Time and timing in the acoustic recognition system of crickets

The songs of many insects exhibit precise timing as the result of repetitive and stereotyped subunits on several time scales. As these signals encode the identity of a species, time and timing are important for the recognition system that analyzes these signals. Crickets are a prominent example as their songs are built from sound pulses that are broadcast in a long trill or as a chirped song. This pattern appears to be analyzed on two timescales, short and long. Recent evidence suggests that song recognition in crickets relies on two computations with respect to time; a short linear-nonlinear (LN) model that operates as a filter for pulse rate and a longer integration time window for monitoring song energy over time. Therefore, there is a twofold role for timing. A filter for pulse rate shows differentiating properties for which the specific timing of excitation and inhibition is important. For an integrator, however, the duration of the time window is more important than the precise timing of events. Here, we first review evidence for the role of LN-models and integration time windows for song recognition in crickets. We then parameterize the filter part by Gabor functions and explore the effects of duration, frequency, phase, and offset as these will correspond to differently timed patterns of excitation and inhibition. These filter properties were compared with known preference functions of crickets and katydids. In a comparative approach, the power for song discrimination by LN-models was tested with the songs of over 100 cricket species. It is demonstrated how the acoustic signals of crickets occupy a simple 2-dimensional space for song recognition that arises from timing, described by a Gabor function, and time, the integration window. Finally, we discuss the evolution of recognition systems in insects based on simple sensory computations.

Static frequency tuning accounts for changes in neural synchrony evoked by transient communication signals

Although communication signals often vary continuously on the underlying signal parameter, they are perceived as distinct categories. We here report the opposite case where an electrocommunication signal is encoded in four distinct regimes, although the behavior described to date does not show distinct categories. In particular, we studied the encoding of chirps by P-unit afferents in the weakly electric fish Apteronotus leptorhynchus. These fish generate an electric organ discharge that oscillates at a certain individual-specific frequency. The interaction of two fish in communication contexts leads to the emergence of a beating amplitude modulation (AM) at the frequency difference between the two individual signals. This frequency difference represents the social context of the encounter. Chirps are transient increases of the fish's frequency leading to transient changes in the frequency of the AM. We stimulated the cells with the same chirp on different, naturally occurring backgrounds beats. The P-units responded either by synchronization or desynchronization depending on the background. Although the duration of a chirp is often shorter than a full cycle of the AM it elicits, the distinct responses of the P-units to the chirp can be predicted solely from the frequency of the AM based on the static frequency tuning of the cells.

Patterns of interval correlations in neural oscillators with adaptation

Neural firing is often subject to negative feedback by adaptation currents. These currents can induce strong correlations among the time intervals between spikes. Here we study analytically the interval correlations of a broad class of noisy neural oscillators with spike-triggered adaptation of arbitrary strength and time scale. Our weak-noise theory provides a general relation between the correlations and the phase-response curve (PRC) of the oscillator, proves anti-correlations between neighboring intervals for adapting neurons with type I PRC and identifies a single order parameter that determines the qualitative pattern of correlations. Monotonically decaying or oscillating correlation structures can be related to qualitatively different voltage traces after spiking, which can be explained by the phase plane geometry. At high firing rates, the long-term variability of the spike train associated with the cumulative interval correlations becomes small, independent of model details. Our results are verified by comparison with stochastic simulations of the exponential, leaky, and generalized integrate-and-fire models with adaptation.

Channel noise from both slow adaptation currents and fast currents is required to explain spike-response variability in a sensory neuron

Spike-timing variability has a large effect on neural information processing. However, for many systems little is known about the noise sources causing the spike-response variability. Here we investigate potential sources of spike-response variability in auditory receptor neurons of locusts, a classic insect model system. At low-spike frequencies, our data show negative interspike-interval (ISI) correlations and ISI distributions that match the inverse Gaussian distribution. These findings can be explained by a white-noise source that interacts with an adaptation current. At higher spike frequencies, more strongly peaked distributions and positive ISI correlations appear, as expected from a canonical model of suprathreshold firing driven by temporally correlated (i.e., colored) noise. Simulations of a minimal conductance-based model of the auditory receptor neuron with stochastic ion channels exclude the delayed rectifier as a possible noise source. Our analysis suggests channel noise from an adaptation current and the receptor or sodium current as main sources for the colored and white noise, respectively. By comparing the ISI statistics with generic models, we find strong evidence for two distinct noise sources. Our approach does not involve any dendritic or somatic recordings that may harm the delicate workings of many sensory systems. It could be applied to various other types of neurons, in which channel noise dominates the fluctuations that shape the neuron's spike statistics.

Impact and sources of neuronal variability in the fly's motion vision pathway

Nervous systems encode information about dynamically changing sensory input by changes in neuronal activity. Neuronal activity changes, however, also arise from noise sources within and outside the nervous system or from changes of the animal's behavioral state. The resulting variability of neuronal responses in representing sensory stimuli limits the reliability with which animals can respond to stimuli and may thus even affect the chances for survival in certain situations. Relevant sources of noise arising at different stages along the motion vision pathway have been investigated from the sensory input to the initiation of behavioral reactions. Here, we concentrate on the reliability of processing visual motion information in flies. Flies rely on visual motion information to guide their locomotion. They are among the best established model systems for the processing of visual motion information allowing us to bridge the gap between behavioral performance and underlying neuronal computations. It has been possible to directly assess the consequences of noise at major stages of the fly's visual motion processing system on the reliability of neuronal signals. Responses of motion sensitive neurons and their variability have been related to optomotor movements as indicators for the overall performance of visual motion computation. We address whether and how noise already inherent in the stimulus, e.g. photon noise for the visual system, influences later processing stages and to what extent variability at the output level of the sensory system limits behavioral performance. Recent advances in circuit analysis and the progress in monitoring neuronal activity in behaving animals should now be applied to understand how the animal meets the requirements of fast and reliable manoeuvres in naturalistic situations.

Processing of acoustic signals in grasshoppers - a neuroethological approach towards female choice

Acoustic communication is a major factor for mate attraction in many grasshopper species and thus plays a vital role in a grasshopper's life. First of all, the recognition of the species-specific sound patterns is crucial for preventing hybridization with other species, which would result in a drastic fitness loss. In addition, there is evidence that females are choosy with respect to conspecific males and prefer or reject the songs of some individuals, thereby exerting a sexual selection on males. Remarkably, the preferences of females are preserved even under masking noise. To discriminate between the basically similar signals of conspecifics is obviously a challenge for small nervous systems. We therefore ask how the acoustic signals are processed and represented in the grasshopper's nervous system, to allow for a fine discrimination and assessment of individual songs. The discrimination of similar signals may be impeded not only by signal masking due to external noise sources, but also by intrinsic noise due to the inherent variability of spike trains. Using a spike train metric we could estimate how well, in principle, the songs of different individuals can be discriminated on the basis of neuronal responses, and found a remarkable potential for discrimination performance at the first stage, but not on higher stages of the auditory pathway. Next, we ask which benefits a grasshopper female may earn from being choosy. New results, which revealed correlations between specific song features and the size and immunocompetence of the males, suggest that females may derive from acoustic signals clues about condition and health of the sending male. However, we observed substantial differences between the preference functions of individual females and it may be particularly rewarding to relate the variations in female preferences to individual differences in the responses of identified neurons.

Relating neuronal to behavioral performance: variability of optomotor responses in the blowfly

Behavioral responses of an animal vary even when they are elicited by the same stimulus. This variability is due to stochastic processes within the nervous system and to the changing internal states of the animal. To what extent does the variability of neuronal responses account for the overall variability at the behavioral level? To address this question we evaluate the neuronal variability at the output stage of the blowfly's (Calliphora vicina) visual system by recording from motion-sensitive interneurons mediating head optomotor responses. By means of a simple modelling approach representing the sensory-motor transformation, we predict head movements on the basis of the recorded responses of motion-sensitive neurons and compare the variability of the predicted head movements with that of the observed ones. Large gain changes of optomotor head movements have previously been shown to go along with changes in the animals' activity state. Our modelling approach substantiates that these gain changes are imposed downstream of the motion-sensitive neurons of the visual system. Moreover, since predicted head movements are clearly more reliable than those actually observed, we conclude that substantial variability is introduced downstream of the visual system.

Trial-to-trial variability in the responses of neurons carries information about stimulus location in the rat whisker thalamus

From the perspective of neural coding, the considerable trial-to-trial variability in the responses of neurons to sensory stimuli is puzzling. Trial-to-trial response variability is typically interpreted in terms of "noise" (i.e., it represents either intrinsic noise of the system or information unrelated to the stimuli). However, trial-to-trial response variability can be considerably different across stimuli, suggesting that it could also provide an important contribution to the information conveyed by the neural responses about the stimuli. To test this hypothesis, we addressed the problem of discriminating stimulus location from the spike-count responses of neurons recorded in the ventro-postero-medial (VPM) nucleus of the thalamus in anesthetized rats. Using a recently developed information theory approach, we verified that differences between stimuli in the trial-to-trial spike-count variability of the responses provided an important contribution to the overall information carried by the neurons. In addition, we found that the relatively reliable (sub-Poisson) firing regime of our VPM neurons was not only more informative, but also more redundant between neurons compared with a more variable (Poisson) firing regime with the same total number of spikes. The typical increase in trial-to-trial response variability from the periphery to the cortex could therefore serve as a strategy to reduce redundancy between neurons and promote efficient sparse coding distributed in large populations of neurons. Overall, our data suggest that the trial-to-trial response variability plays a critical role in establishing the trade-off between total information and redundancy between neurons in population codes.

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.

Neuronal precision and the limits for acoustic signal recognition in a small neuronal network

Recognition of acoustic signals may be impeded by two factors: extrinsic noise, which degrades sounds before they arrive at the receiver’s ears, and intrinsic neuronal noise, which reveals itself in the trial-to-trial variability of the responses to identical sounds. Here we analyzed how these two noise sources affect the recognition of acoustic signals from potential mates in grasshoppers. By progressively corrupting the envelope of a female song, we determined the critical degradation level at which males failed to recognize a courtship call in behavioral experiments. Using the same stimuli, we recorded intracellularly from auditory neurons at three different processing levels, and quantified the corresponding changes in spike train patterns by a spike train metric, which assigns a distance between spike trains. Unexpectedly, for most neurons, intrinsic variability accounted for the main part of the metric distance between spike trains, even at the strongest degradation levels. At consecutive levels of processing, intrinsic variability increased, while the sensitivity to external noise decreased. We followed two approaches to determine critical degradation levels from spike train dissimilarities, and compared the results with the limits of signal recognition measured in behaving animals.

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.

Sparse but specific temporal coding by spikes in an insect sensory-motor ocellar pathway

We investigate coding in a locust brain neuron, DNI, which transforms graded synaptic input from ocellar L-neurons into axonal spikes that travel to excite particular thoracic flight neurons. Ocellar neurons are naturally stimulated by fluctuations in light collected from a wide field of view, for example when the visual horizon moves up and down. We used two types of stimuli: fluctuating light from a light-emitting diode (LED), and a visual horizon displayed on an electrostatic monitor. In response to randomly fluctuating light stimuli delivered from the LED, individual spikes in DNI occur sparsely but are timed to sub-millisecond precision, carrying substantial information: 4.5–7 bits per spike in our experiments. In response to these light stimuli, the graded potential signal in DNI carries considerably less information than in presynaptic L-neurons. DNI is excited in phase with either sinusoidal light from an LED or a visual horizon oscillating up and down at 20 Hz, and changes in mean light level or mean horizon level alter the timing of excitation for each cycle. DNI is a multimodal interneuron, but its ability to time spikes precisely in response to ocellar stimulation is not degraded by additional excitation. We suggest that DNI is part of an optical proprioceptor system, responding to the optical signal induced in the ocelli by nodding movements of the locust head during each wing-beat.

Intensity invariance properties of auditory neurons compared to the statistics of relevant natural signals in grasshoppers

The temporal pattern of amplitude modulations (AM) is often used to recognize acoustic objects. To identify objects reliably, intensity invariant representations have to be formed. We approached this problem within the auditory pathway of grasshoppers. We presented AM patterns modulated at different time scales and intensities. Metric space analysis of neuronal responses allowed us to determine how well, how invariantly, and at which time scales AM frequency is encoded. We find that in some neurons spike-count cues contribute substantially (20-60%) to the decoding of AM frequency at a single intensity. However, such cues are not robust when intensity varies. The general intensity invariance of the system is poor. However, there exists a range of AM frequencies around 83 Hz where intensity invariance of local interneurons is relatively high. In this range, natural communication signals exhibit much variation between species, suggesting an important behavioral role for this frequency band. We hypothesize, just as has been proposed for human speech, that the communication signals might have evolved to match the processing properties of the receivers. This contrasts with optimal coding theory, which postulates that neuronal systems are adapted to the statistics of the relevant signals.

Quantifying neural coding of event timing

Single-neuron firing is often analyzed relative to an external event, such as successful task performance or the delivery of a stimulus. The perievent time histogram (PETH) examines how, on average, neural firing modulates before and after the alignment event. However, the PETH contains no information about the single-trial reliability of the neural response, which is important from the perspective of a target neuron. In this study, we propose the concept of using the neural activity to predict the timing of the occurrence of an event, as opposed to using the event to predict the neural response. We first estimate the likelihood of an observed spike train, under the assumption that it was generated by an inhomogeneous gamma process with rate profile similar to the PETH shifted by a small time. This is used to generate a probability distribution of the event occurrence, using Bayes’ rule. By an information theoretic approach, this method yields a single value (in bits) that quantifies the reduction in uncertainty regarding the time of an external event following observation of the spike train. We show that the approach is sensitive to the amplitude of a response, to the level of baseline firing, and to the consistency of a response between trials, all of which are factors that will influence a neuron's ability to code for the time of the event. The technique can provide a useful means not only of determining which of several behavioral events a cell encodes best, but also of permitting objective comparison of different cell populations.

Influence of sound pressure level on the processing of amplitude modulations by auditory neurons of the locust

Typical features of natural sounds are amplitude changes at different time scales. In many species, amplitude modulations constitute decisive cues to recognize communication signals. Since these signals should be recognizable over a broad intensity range, we investigated how the encoding of amplitude modulations by auditory neurons depends on sound pressure level. Identified neurons that represent different processing stages in the locusts' auditory pathway were stimulated with sinusoidal modulations of a broad band noise carrier, at different intensities, and characteristic parameters of modulation transfer functions (MTFs) were determined. The corner frequencies of temporal MTFs turned out to be independent of intensity for all neurons except one. Furthermore, for none of the neurons investigated corner frequencies were significantly correlated with spike rate, indicating a remarkable intensity invariance of the upper limits of temporal resolution. The shape of the tMTFs changed with increasing intensity from a low-pass to a band-pass for receptors and local neurons, while no consistent change was observed for ascending neurons. The best modulation frequency depended on intensity and spike rate, especially for receptors and local neurons. Remarkably, the adaptation state of some neurons turned out to be independent of the spike rate during the modulation part of the stimulus.

Sensory processing in the Drosophila antennal lobe increases reliability and separability of ensemble odor representations

Here we describe several fundamental principles of olfactory processing in the Drosophila melanogaster antennal lobe (the analog of the vertebrate olfactory bulb), through the systematic analysis of input and output spike trains of seven identified glomeruli. Repeated presentations of the same odor elicit more reproducible responses in second-order projection neurons (PNs) than in their presynaptic olfactory receptor neurons (ORNs). PN responses rise and accommodate rapidly, emphasizing odor onset. Furthermore, weak ORN inputs are amplified in the PN layer but strong inputs are not. This nonlinear transformation broadens PN tuning and produces more uniform distances between odor representations in PN coding space. In addition, portions of the odor response profile of a PN are not systematically related to their direct ORN inputs, which probably indicates the presence of lateral connections between glomeruli. Finally, we show that a linear discriminator classifies odors more accurately using PN spike trains than using an equivalent number of ORN spike trains.

Simulation of the neural response to the odour stimulus

The paper is aimed at the description of the newly introduced model used to simulate the neural response to the chemical stimulus. A potential difference across the living cell or tissue depends usually on the chemical excitation of this living entity. The peculiar case of chemical excitation is the odour recognition across animal kingdom, in particular by various insects. A potential difference is characterised by relatively rapid variation with time elapsed since the application of the stimulus. A complete mathematical model giving results similar to the real ones is outlined and discussed in light of the potential application to the various experimental patterns recognition. The saturation effects due to overlapping spikes are discussed in some detail. It is proposed to use semi-invariants as the semi-quantitative method to compare various data sets obtained either in response to various stimuli or to the same stimulus applied to various species.

Neural Correlations Increase Between Consecutive Processing Levels in the Auditory System of Locusts

Nervous systems may encode information about sensory stimuli using the temporal relations in spiking patterns between neurons. By conducting simultaneous intracellular recordings on pairs of auditory neurons we measured the strength of correlations between elements of the first three processing levels in the metathoracic auditory network of the locust. We quantified the degree of spike synchronization and rate covariations that occur among auditory neurons during acoustic stimulation. In addition to the acoustic stimulation, current pulses were injected into both neurons to study the connectivity within this network. Our findings support the view that the metathoracic auditory system is a hierarchically organized feedforward network. Strong synaptic connections were observed only between consecutive processing levels, whereas there was no indication for strong connections between elements of the same processing level. Both spike synchronization and rate covariations were increased among neurons on higher processing levels. We further investigated the consequences that correlations may have on the common estimates of neuronal variability. For example, rate covariations caused by strong synaptic coupling between two neurons may lead to an overestimation if the variability is measured trial by trial with respect to only single neurons. For the vast majority of cell pairs tested, however, no strong synaptic coupling could be demonstrated. Thus we could show that in most cases no serious errors are made if one determines variability by following the usual procedure on the basis of single-cell recordings.

Auditory discrimination of amplitude modulations based on metric distances of spike trains

Sound envelope cues play a crucial role for the recognition and discrimination of communication signals in diverse taxa, such as vertebrates and arthropods. Using a classification based on metric similarities of spike trains we investigate how well amplitude modulations (AMs) of sound signals can be distinguished at three levels of the locust's auditory pathway: receptors and local and ascending neurons. The spike train metric has the advantage of providing information about the necessary evaluation time window and about the optimal temporal resolution of processing, thereby yielding clues to possible coding principles. It further allows one to disentangle the respective contributions of spike count and spike timing to the fidelity of discrimination. These results are compared with the traditional paradigm using modulation transfer functions. Spike trains of receptors and two primary-like local interneurons enable an excellent discrimination of different AM frequencies, up to about 150 Hz. In these neurons discriminability depends almost completely on the timing of spikes, which must be evaluated with a temporal resolution of <5 ms. Even short spike-train segments of 150 ms, equivalent to five to eight spikes, suffice for a high (70%) discrimination performance. For the third level of processing, the ascending interneurons, the overall discrimination accuracy is reduced. Spike count differences become more important for the discrimination whereas the exact timing of spikes contributes less. This shift in temporal resolution does not primarily depend on the investigated stimulus space. Rather it appears to reflect a transformation of how amplitude modulations are represented at more central stages of processing.