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

An auditory feature detection circuit for sound pattern recognition.

From human language to birdsong and the chirps of insects, acoustic communication is based on amplitude and frequency modulation of sound signals. Whereas frequency processing starts at the level of the hearing organs, temporal features of the sound amplitude such as rhythms or pulse rates require processing by central auditory neurons. Besides several theoretical concepts, brain circuits that detect temporal features of a sound signal are poorly understood. We focused on acoustically communicating field crickets and show how five neurons in the brain of females form an auditory feature detector circuit for the pulse pattern of the male calling song. The processing is based on a coincidence detector mechanism that selectively responds when a direct neural response and an intrinsically delayed response to the sound pulses coincide. This circuit provides the basis for auditory mate recognition in field crickets and reveals a principal mechanism of sensory processing underlying the perception of temporal patterns.Financial support for the study was provided by the Biotechnology and Biological Sciences Research Council (BB/J01835X/1) and the Isaac Newton Trust (Trinity College, Cambridge).This is the final version of the article. It first appeared from AAAS via http://dx.doi.org/10.1126/sciadv.150032

Adaptation and Selective Information Transmission in the Cricket Auditory Neuron AN2

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

Cortical Mechanisms Of Adaptation In Auditory Processing

Adaptation is computational strategy that underlies sensory nervous systems’ ability to accurately encode stimuli in various and dynamic contexts and shapes how animals perceive their environment. Many questions remain concerning how adaptation adjusts to particular stimulus features and its underlying mechanisms. In Chapter 2, we tested how neurons in the primary auditory cortex adapt to changes in stimulus temporal correlation. We used chronically implanted tetrodes to record neuronal spiking in rat primary auditory cortex during exposure to custom made dynamic random chord stimuli exhibiting different levels of temporal correlation. We estimated linear non-linear model for each neuron at each temporal correlation level, finding that neurons compensate for temporal correlation changes through gain-control adaptation. This experiment extends our understanding of how complex stimulus statistics are encoded in the auditory nervous system. In Chapter 3 and 4, we tested how interneurons are involved in adaptation by optogenetically suppressing parvalbumin-positive (PV) and somatostatin-positive (SOM) interneurons during tone train stimuli and using silicon probes to record neuronal spiking in mouse primary auditory cortex. In Chapter 3, we found that inhibition from both PVs and SOMs contributes to stimulus-specific adaptation (SSA) through different mechanisms. SOM inhibition was stimulus-specific, suppressing responses to standard tones more strongly than responses to deviant tones, and increasing with standard tone repetition. PVs amplified SSA because inhibition was similar for standard and deviant tones and PV mediated inhibition was insensitive to tone repetition. PVs and SOMs themselves exhibit SSA, and a Wilson-Cowan dynamic model identified that PVs and SOMs can directly contribute to SSA in pyramidal neurons. In Chapter 4, we tested how SOMs and PVs inhibition is modulated with the dynamics of adaptation and across frequency tuning, during exposure to single frequency tone trains across the neuron’s tuning curve. We found that the magnitude of SOM inhibition correlated with the magnitude of adaptive suppression, while PVs inhibition was largely insensitive to stimulus conditions. Together Chapters 3 and 4 implicate SOM inhibition in actively suppressing responses in a stimulus-specific manner while PV inhibition may passively enhance stimulus-specific suppression. These experiments inform the underlying principles and mechanisms of cortical sensory adaptation

Modelling Pattern Recognition in Cricket Phonotaxis

A spiking neuron implementation of pattern recognition of the calling songs in Gryllus bimaculatus is proposed. A simplified model of the auditory interneuron AN1 has been fitted to extracellular physiological data. The model captures the aspects of AN1’s rate-response to acoustic stimulation which are believed to be sufficient for pattern recognition. Stimulation patterns can be induced into the model via current injecton of the signals envelope-shapes. The model was used as the input stage to the pattern recognition mechanisms. A biologically plausible filter mechanism for pulse-pause patterns is proposed which is based on short term synaptic plasticity. Three simple filter mechanism are described, based on either isolated synaptic depression or synaptic facilitation. These filters are able to reproduce physiological findings from the cricket’s auditory brain neurons. Further, it is argued that more complex filters can be produced by using combinations of depression and facilitation, and that a complete model of the cricket’s pattern recognition apparatus may be implemented in this way. This however is left as a subject of further studies

Optimized Parallel Coding of Second-Order Stimulus Features by Heterogeneous Neural Populations

UNLABELLED: Efficient processing of sensory input is essential to ensure an organism's survival in its natural environment. Growing evidence suggests that sensory neurons can optimally encode natural stimuli by ensuring that their tuning opposes stimulus statistics, such that the resulting neuronal response contains equal power at all frequencies (i.e., is "white"). Such temporal decorrelation or whitening has been observed across modalities, but the effects of neural heterogeneities on determining tuning and thus responses to natural stimuli have not been investigated. Here, we investigate how heterogeneities in sensory pyramidal neurons organized in three parallel maps representing the body surface determine responses to second-order electrosensory stimulus features in the weakly electric fish Apteronotus leptorhynchus While some sources of heterogeneities such as ON- and OFF-type responses to first-order did not affect responses to second-order electrosensory stimulus features, other sources of heterogeneity within and across the maps strongly determined responses. We found that these cells effectively performed a fractional differentiation operation on their input with exponents ranging from zero (no differentiation) to 0.4 (strong differentiation). Varying adaptation in a simple model explained these heterogeneities and predicted a strong correlation between fractional differentiation and adaptation. Using natural stimuli, we found that only a small fraction of neurons implemented temporal whitening. Rather, a large fraction of neurons did not perform any significant whitening and thus preserved natural input statistics in their responses. We propose that this information is needed to properly decode optimized information sent in parallel through temporally whitened responses based on context. SIGNIFICANCE STATEMENT: We demonstrate that heterogeneities in the same sensory neuron type can either have no or significant influence on their responses to second-order stimulus features. While an ON- or OFF-type response to first-order stimulus attributes has no significant influence on responses to second-order stimulus features, we found that only a small fraction of sensory neurons optimally encoded natural stimuli through high-pass filtering, thereby implementing temporal whitening. Surprisingly, a large fraction of sensory neurons performed little if no filtering of stimuli, thereby preserving natural stimulus statistics. We hypothesize that this pathway is necessary to properly decode optimized information contained in temporally whitened responses based on context

The recruitment and function of inhibitory interneurons in olfactory bulb processing

Inhibitory interneurons are the “shush”-ers of the brain—their output causes a reduction in the output of other neurons. Inhibitory interactions play a critical role in the olfactory bulb, where they shape olfactory representations that guide behavior. However, the mechanisms by which interneuron activation improves olfactory function remain debated. In particular, the relative importance neural activity over short periods of time (~tens of milliseconds) versus long periods of time (hundreds to thousands of milliseconds) has provoked significant debate. Granule cells are inhibitory interneurons in the olfactory bulb that can respond and influence olfactory bulb activity across a wide range of timescales. The first part of this dissertation investigates the physiological mechanisms driving the timing of granule cell recruitment. We found that the specific timing of recruitment depends on the timing of synaptic excitation delivered from tufted cells. Tufted cells (unlike the more commonly studied mitral cells) are able to fire at long latencies due to intrinsic membrane properties that allow them to integrate weak inputs slowly while responding rapidly to strong inputs. Computational modeling revealed that the long-latency inhibition generated by this mechanism can improve performance on stimulus discrimination tasks. The second portion of this dissertation focuses on the downstream effects of granule cell recruitment. Highly correlated spiking can be advantageous for propagating information. However, these same correlations limit encoding by introducing redundancy. We investigated how granule cell recruitment altered correlations between mitral cell pairs across timescales. We found that granule cell recruitment increased fast timescale correlations (i.e. synchronous spiking) while simultaneously decreasing slow timescale correlations (i.e. firing rate similarity). Using computational modeling, we show that timescale-dependent correlation changes are functionally advantageous because they can circumvent the tradeoff between propagation and encoding. Taken together, these studies extend our understanding of olfactory bulb physiology by providing a mechanistic description of how inhibitory circuits shape activity across timescales. Our results indicate that granule cell recruitment requires dynamic and stimulus-dependent interactions between mitral, tufted, and granule cells, and that the inhibition recruited by this mechanism works at multiple timescales to effectively encode and propagate stimulus information

Critical Song Features for Auditory Pattern Recognition in Crickets

Many different invertebrate and vertebrate species use acoustic communication for pair formation. In the cricket Gryllus bimaculatus, females recognize their species-specific calling song and localize singing males by positive phonotaxis. The song pattern of males has a clear structure consisting of brief and regular pulses that are grouped into repetitive chirps. Information is thus present on a short and a long time scale. Here, we ask which structural features of the song critically determine the phonotactic performance. To this end we employed artificial neural networks to analyze a large body of behavioral data that measured females’ phonotactic behavior under systematic variation of artificially generated song patterns. In a first step we used four non-redundant descriptive temporal features to predict the female response. The model prediction showed a high correlation with the experimental results. We used this behavioral model to explore the integration of the two different time scales. Our result suggested that only an attractive pulse structure in combination with an attractive chirp structure reliably induced phonotactic behavior to signals. In a further step we investigated all feature sets, each one consisting of a different combination of eight proposed temporal features. We identified feature sets of size two, three, and four that achieve highest prediction power by using the pulse period from the short time scale plus additional information from the long time scale