scholarly journals A small, computationally flexible network produces the phenotypic diversity of song recognition in crickets

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Jan Clemens ◽  
Stefan Schöneich ◽  
Konstantin Kostarakos ◽  
R Matthias Hennig ◽  
Berthold Hedwig
Keyword(s):  
Pattern Recognition ◽  
Feature Detection ◽  
Phenotypic Diversity ◽  
Communication Signals ◽  
Increase Energy Efficiency ◽  
Song Recognition ◽  
Electrophysiological Recordings ◽  
Temporal Features ◽  
Flexible Networks ◽  
Species Specific

How neural networks evolved to generate the diversity of species-specific communication signals is unknown. For receivers of the signals one hypothesis is that novel recognition phenotypes arise from parameter variation in computationally flexible feature detection networks. We test this hypothesis in crickets, where males generate and females recognize the mating songs with a species-specific pulse pattern, by investigating whether the song recognition network in the cricket brain has the computational flexibility to recognize different temporal features. Using electrophysiological recordings from the network that recognizes crucial properties of the pulse pattern on the short timescale in the cricket Gryllus bimaculatus, we built a computational model that reproduces the neuronal and behavioral tuning of that species. An analysis of the model's parameter space reveals that the network can provide all recognition phenotypes for pulse duration and pause known in crickets and even other insects. Phenotypic diversity in the model is consistent with known preference types in crickets and other insects, and arise from computations that likely evolved to increase energy efficiency and robustness of pattern recognition. The model's parameter to phenotype mapping is degenerate-different network parameters can create similar changes in the phenotype-which likely supports evolutionary plasticity. Our study suggests that computationally flexible networks underlie the diverse pattern recognition phenotypes and we reveal network properties that constrain and support behavioral diversity.

scholarly journals A small, computationally flexible network produces the phenotypic diversity of song recognition in crickets

2020 ◽  
Author(s):  
Jan Clemens ◽  
Stefan Schöneich ◽  
Konstantinos Kostarakos ◽  
R. Matthias Hennig ◽  
Berthold Hedwig
Keyword(s):  
Phenotypic Diversity ◽  
Parameter Variation ◽  
Gryllus Bimaculatus ◽  
Communication Signals ◽  
Increase Energy Efficiency ◽  
Behavioral Diversity ◽  
Song Recognition ◽  
Electrophysiological Recordings ◽  
Network Properties ◽  
Species Specific

AbstractHow neural networks evolve to recognize species-specific communication signals is unknown. One hypothesis is that novel recognition phenotypes are produced by parameter variation in a computationally flexible “mother network”. We test this hypothesis in crickets, where males produce and females recognize mating songs with a species-specific pulse pattern. Whether the song recognition network in crickets is computationally flexible to recognize the diversity of pulse patterns and what network properties support and constrain this flexibility is unknown. Using electrophysiological recordings from the cricket Gryllus bimaculatus, we built a model of the song recognition network that reproduces the network dynamics as well as the neuronal and behavioral tuning for that species. An analysis of the model’s parameter space reveals that the network can produce all recognition phenotypes known in crickets and even other insects. Biases in phenotypic diversity produced by the model are consistent with the existing behavioral diversity in crickets, and arise from computations that likely evolved to increase energy efficiency and robustness of song recognition. The model’s parameter to phenotype mapping is degenerate – different network parameters can create similar changes in the phenotype – which is thought to support evolutionary plasticity. Our study suggest that a computationally flexible mother network could underlie the diversity of song recognition phenotypes in crickets and we reveal network properties that constrain and support behavioral diversity.

scholarly journals Time-Frequency Feature Analysis

2021 ◽  
Author(s):  
Behnaz Ghoraani
Keyword(s):  
Pattern Recognition ◽  
Real World ◽  
Feature Detection ◽  
Classification Systems ◽  
Time Frequency ◽  
Analysis Methodology ◽  
Stationary Signals ◽  
Time Varying Frequency ◽  
Non Stationary Signal ◽  
Discriminative Pattern

Most of the real-world signals in nature are non-stationary, i.e., their statistics are time variant. Extracting the time-varying frequency characteristics of a signal is very important in understanding the signal better, which could be of immense use in various applications such as pattern recognition and automated-decision making systems. In order to extract meaningful time-frequency (TF) features, a joint TF analysis is required. The proposed work is an attempt to develop a generalized TF analysis methodology that exploits the benefits of TF distribution (TFD) in pattern classification systems as related to discriminant feature detection and classification. Our objective is to introduce a unique and efficient way of performing non-stationary signal analysis using adaptive and discriminant TF techniques. To fulfill this objective, in the first point, we build a novel TF matrix (TFM) decomposition that increases the effectiveness of segmentation in real-world signals. Instantaneous and unique features are extracted from each segment such that they successfully represent joint TF structure of the signal. In the second point, based on the above technique, two unique and novel discriminant TF analysis methods are proposed to perform an improved and discriminant feature selection of any non-stationary signals. The first approach is a new machine learning method that identifies the clusters of the discriminant features to compute the presence of the discriminative pattern in any given signal, and classify them accordingly. The second approach is a discriminant TFM (DTFM) framework, which is a combination of TFM decomposition and the discriminant clustering techniques. The developed DTFM analysis automatically identifies the differences between different classes as the distinguishing structure, and uses the identified structure to accurately classify and locate the discriminant structure in the signal. The theoretical properties of the proposed approaches pertaining to pattern recognition and detection are examined in this dissertation. The extracted TF features provide strong and successful characterization and classification of real and synthetic non-stationary signals. The proposed TF techniques facilitate the adaptation of TF quantification to any feature detection technique in automating the identification process of discriminatory TF features, and can find applications in many different fields including biomedical and multimedia signal processing.

scholarly journals Song discrimination learning in zebra finches induces highly divergent responses to novel songs

2006 ◽  
Vol 274 (1607) ◽  
pp. 295-301 ◽  
Author(s):  
Machteld N Verzijden ◽  
Eric Etman ◽  
Caroline van Heijningen ◽  
Marianne van der Linden ◽  
Carel ten Cate
Keyword(s):  
Discrimination Learning ◽  
Peak Shift ◽  
Taeniopygia Guttata ◽  
One Dimension ◽  
Signal Evolution ◽  
Zebra Finches ◽  
Communication Signals ◽  
Song Discrimination ◽  
Species Specific ◽  
Element Sequence

Perceptual biases can shape the evolution of signal form. Understanding the origin and direction of such biases is therefore crucial for understanding signal evolution. Many animals learn about species-specific signals. Discrimination learning using simple stimuli varying in one dimension (e.g. amplitude, wavelength) can result in perceptual biases with preferences for specific novel stimuli, depending on the stimulus dimensions. We examine how this translates to discrimination learning involving complex communication signals; birdsongs. Zebra finches ( Taeniopygia guttata ) were trained to discriminate between two artificial songs, using a Go/No-Go procedure. The training songs in experiment 1 differed in the number of repeats of a particular element. The songs in experiment 2 differed in the position of an odd element in a series of repeated elements. We examined generalization patterns by presenting novel songs with more or fewer repeated elements (experiment 1), or with the odd element earlier or later in the repeated element sequence (experiment 2). Control birds were trained with only one song. The generalization curves obtained from (i) control birds, (ii) experimental birds in experiment 1, and (iii) experimental birds in experiment 2 showed large and systematic differences from each other. Birds in experiment 1, but not 2, responded more strongly to specific novel songs than to training songs, showing ‘peak shift’. The outcome indicates that learning about communication signals may give rise to perceptual biases that may drive signal evolution.

MicroRNAs of the mesothorax in Qinlingacris elaeodes, an alpine grasshopper showing a wing polymorphism with unilateral wing form

2015 ◽  
Vol 106 (2) ◽  
pp. 225-232 ◽  
Author(s):  
R. Li ◽  
G.F. Jiang ◽  
Q.P. Ren ◽  
Y.T. Wang ◽  
X.M. Zhou ◽  
...  
Keyword(s):  
Phenotypic Diversity ◽  
Wing Form ◽  
Wing Polymorphism ◽  
Insect Wings ◽  
Deep Sequencing Technology ◽  
Illumina Deep Sequencing ◽  
Polymerase Chain ◽  
Species Specific ◽  
Post Transcriptional Regulation ◽  
Development And Evolution

AbstractMicroRNAs (miRNAs) are now recognized as key post-transcriptional regulators in regulation of phenotypic diversity. Qinlingacris elaeodes is a species of the alpine grasshopper, which is endemic to China. Adult individuals have three wing forms: wingless, unilateral-winged and short-winged. This is an ideal species to investigate the phenotypic plasticity, development and evolution of insect wings because of its case of unilateral wing form in both the sexes. We sequenced a small RNA library prepared from mesothoraxes of the adult grasshoppers using the Illumina deep sequencing technology. Approximately 12,792,458 raw reads were generated, of which the 854,580 high-quality reads were used only for miRNA identification. In this study, we identified 49 conserved miRNAs belonging to 41 families and 69 species-specific miRNAs. Moreover, seven miRNA*s were detected both for conserved miRNAs and species-specific miRNAs, which were supported by hairpin forming precursors based on polymerase chain reaction. This is the first description of miRNAs in alpine grasshoppers. The results provide a useful resource for further studies on molecular regulation and evolution of miRNAs in grasshoppers. These findings not only enrich the miRNAs for insects but also lay the groundwork for the study of post-transcriptional regulation of wing forms.

scholarly journals A feature­based approach to fractal image compression

Connectivity ◽  
2020 ◽  
Vol 146 (5) ◽  
Author(s):  
G. Ya. Kis ◽  
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Video Compression ◽  
Feature Detection ◽  
Lipschitz Constant ◽  
Block Matching ◽  
Matching Task ◽  
Feature Points ◽  
Fractal Compression ◽  
Salient Points

Recently a number of researches have demonstrated performance improvement in the video fractal compression compared to the current video transmission standards (MPEG, H.263, H.264). This article describes a current problem of relatively low fractal encoding speed. Indeed, high computational complexity is a sore point of fractal compression approach. It seems almost every paper on this subject touches the problem of encoding speed. Productive ideas and algorithms can be borrowed from the pattern recognition problem. In the course of recent decades feature points approach in computer vision has been demonstrating good performance in SLAM and pattern recognition. Technology of feature detection, description and tracking is being developed successfully and has effective applications in augmented reality like Android ARCore and IOS ARKit frameworks that are real-time engines. Similarities among parts of video frames are analyzed and used for both image registration and visual scene tracking therefore it fits highly to block matching task. Statistic properties for domain/range blocks matching has been analyzed on the basis of previous investigation for fractal compression. As a result, a simple algorithm is proposed based on computer vision approach. The approach includes a visual feature points extraction, feature descriptors calculation and fast NN-search in descriptor space. The key idea of the proposed approach is as follows. Only a limited number of domain blocks around the most salient points are subject to selection. Other blocks are not essential for matching as transforms would have big Lipschitz constant and will have worse contractive properties. Salient points should be unique as well. Further the descriptors for feature point are calculated. The algorithm has O(N log N) complexity for pixel number in the frame image, however if the number of domain blocks is limited the complexity could be almost linear. Python program for the algorithm test has been developed and shows that reconstruction result is acceptable in terms of encoding speed (< 2 s on 2 GHz CPU) and quality (PSNR) ~25 dB. The result of the proposed approach could be interesting for further improvement both for image and video compression. Further steps for quality increasing are also described.

Evolution of miRNA binding sites and regulatory networks in cichlids

2021 ◽  
Author(s):  
Tarang K Mehta ◽  
Luca Penso-Dolfin ◽  
Will K Nash ◽  
Sushmita Roy ◽  
Federica Di Palma ◽  
...  
Keyword(s):  
Binding Site ◽  
Binding Sites ◽  
Regulatory Networks ◽  
Target Genes ◽  
Phenotypic Diversity ◽  
Regulatory Regions ◽  
Adaptive Trait ◽  
Regulatory Variants ◽  
Key Driver ◽  
Species Specific

The divergence of regulatory regions and gene regulatory network (GRN) rewiring is a key driver of cichlid phenotypic diversity. However, the contribution of miRNA binding site turnover has yet to be linked to GRN evolution across cichlids. Here, we extend our previous studies by analysing the selective constraints driving evolution of miRNA and transcription factor (TF) binding sites of target genes, to infer instances of cichlid GRN rewiring associated with regulatory binding site turnover. Comparative analyses identified increased species-specific networks that are functionally associated to traits of cichlid phenotypic diversity. The evolutionary rewiring is associated with differential models of miRNA snd TF binding site turnover, driven by a high proportion of fast-evolving polymorphic sites in adaptive trait genes compared to subsets of random genes. Positive selection acting upon discrete mutations in these regulatory regions is likely to be an important mechanism in rewiring GRNs in rapidly radiating cichlids. Regulatory variants of functionally associated miRNA and TF binding sites of visual opsin genes differentially segregate according to phylogeny and ecology of Lake Malawi species, identifying both rewired e.g. clade-specific and conserved network motifs of adaptive trait associated GRNs. Our approach revealed several novel candidate regulators, regulatory regions and three-node motifs across cichlid genomes with previously reported associations to known adaptive evolutionary traits.

Encoding of social signals in all three electrosensory pathways of Eigenmannia virescens

2014 ◽  
Vol 112 (9) ◽  
pp. 2076-2091 ◽  
Author(s):  
Anna Stöckl ◽  
Fabian Sinz ◽  
Jan Benda ◽  
Jan Grewe
Keyword(s):  
Weakly Electric Fish ◽  
Social Signals ◽  
Active System ◽  
Behavioral Experiments ◽  
Communication Signals ◽  
Parallel Pathways ◽  
Electrophysiological Recordings ◽  
Eigenmannia Virescens ◽  
Neuronal Representation

Extracting complementary features in parallel pathways is a widely used strategy for a robust representation of sensory signals. Weakly electric fish offer the rare opportunity to study complementary encoding of social signals in all of its electrosensory pathways. Electrosensory information is conveyed in three parallel pathways: two receptor types of the tuberous (active) system and one receptor type of the ampullary (passive) system. Modulations of the fish's own electric field are sensed by these receptors and used in navigation, prey detection, and communication. We studied the neuronal representation of electric communication signals (called chirps) in the ampullary and the two tuberous pathways of Eigenmannia virescens. We first characterized different kinds of chirps observed in behavioral experiments. Since Eigenmannia chirps simultaneously drive all three types of receptors, we studied their responses in in vivo electrophysiological recordings. Our results demonstrate that different electroreceptor types encode different aspects of the stimuli and each appears best suited to convey information about a certain chirp type. A decoding analysis of single neurons and small populations shows that this specialization leads to a complementary representation of information in the tuberous and ampullary receptors. This suggests that a potential readout mechanism should combine information provided by the parallel processing streams to improve chirp detectability.

scholarly journals Variability in echolocation call design of 26 Swiss bat species: consequences, limits and options for automated field identification with a synergetic pattern recognition approach

Mammalia ◽  
2004 ◽  
Vol 68 (4) ◽  
Author(s):  
Martin K. Obrist ◽  
Ruedi Boesch ◽  
Peter F. Flückiger
Keyword(s):  
Pattern Recognition ◽  
Species Identification ◽  
Automated Systems ◽  
Echolocation Call ◽  
Echolocation Calls ◽  
Recognition Algorithms ◽  
Field Identification ◽  
Pattern Recognition Approach ◽  
Species Specific ◽  
Independent Species

Pattern recognition algorithms offer a promising approach to recognizing bat species by their echolocation calls. Automated systems like synergetic classifiers may contribute significantly to operator-independent species identification in the field. However, it necessitates the assembling of an appropriate database of reference calls, a task far from trivial. We present data on species specific flexibility in call parameters of all Swiss bat species (except

scholarly journals Large Sequence Diversity within the Biosynthesis Locus and Common Biochemical Features of Campylobacter coli Lipooligosaccharides

2016 ◽  
Vol 198 (20) ◽  
pp. 2829-2840 ◽  
Author(s):  
Alejandra Culebro ◽  
Joana Revez ◽  
Ben Pascoe ◽  
Yasmin Friedmann ◽  
Matthew D. Hitchings ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Population Dynamics ◽  
Chemical Composition ◽  
Phenotypic Diversity ◽  
Host Adaptation ◽  
Campylobacter Coli ◽  
Genomic Information ◽  
Content Type ◽  
Species Specific ◽  
First Time

ABSTRACTDespite the importance of lipooligosaccharides (LOSs) in the pathogenicity of campylobacteriosis, little is known about the genetic and phenotypic diversity of LOS inCampylobacter coli. In this study, we investigated the distribution of LOS locus classes among a large collection of unrelatedC. coliisolates sampled from several different host species. Furthermore, we pairedC. coligenomic information and LOS chemical composition for the first time to investigate possible associations between LOS locus class sequence diversity and biochemical heterogeneity. After identifying three new LOS locus classes, only 85% of the 144 isolates tested were assigned to a class, suggesting higher genetic diversity than previously thought. This genetic diversity is at the basis of a completely unexplored LOS structural heterogeneity. Mass spectrometry analysis of the LOSs of nine isolates, representing four different LOS classes, identified two features distinguishingC. coliLOS from that ofCampylobacter jejuni. 2-Amino-2-deoxy-d-glucose (GlcN)–GlcN disaccharides were present in the lipid A backbone, in contrast to the β-1′-6-linked 3-diamino-2,3-dideoxy-d-glucopyranose (GlcN3N)–GlcN backbone observed inC. jejuni. Moreover, despite the fact that many of the genes putatively involved in 3-acylamino-3,6-dideoxy-d-glucose (Quip3NAcyl) were apparently absent from the genomes of various isolates, this rare sugar was found in the outer core of allC. coliisolates. Therefore, regardless of the high genetic diversity of the LOS biosynthesis locus inC. coli, we identified species-specific phenotypic features ofC. coliLOS that might explain differences betweenC. jejuniandC. coliin terms of population dynamics and host adaptation.IMPORTANCEDespite the importance ofC. colito human health and its controversial role as a causative agent of Guillain-Barré syndrome, little is known about the genetic and phenotypic diversity ofC. coliLOSs. Therefore, we pairedC. coligenomic information and LOS chemical composition for the first time to address this paucity of information. We identified two species-specific phenotypic features ofC. coliLOS, which might contribute to elucidating the reasons behind the differences betweenC. jejuniandC. coliin terms of population dynamics and host adaptation.

scholarly journals A robust event-driven approach to always-on object recognition

2022 ◽  
Author(s):  
Antoine Grimaldi ◽  
Victor Boutin ◽  
Sio-Hoi Ieng ◽  
Ryad Benosman ◽  
Laurent Perrinet
Keyword(s):  
Pattern Recognition ◽  
Multinomial Logistic Regression ◽  
Gain Control ◽  
Recognition Algorithm ◽  
Object Categorization ◽  
Online Classification ◽  
Temporal Features ◽  
Event Driven ◽  
Spatio Temporal ◽  
Event Based

<div> <div> <div> <p>We propose a neuromimetic architecture able to perform always-on pattern recognition. To achieve this, we extended an existing event-based algorithm [1], which introduced novel spatio-temporal features as a Hierarchy Of Time-Surfaces (HOTS). Built from asynchronous events acquired by a neuromorphic camera, these time surfaces allow to code the local dynamics of a visual scene and to create an efficient event-based pattern recognition architecture. Inspired by neuroscience, we extended this method to increase its performance. Our first contribution was to add a homeostatic gain control on the activity of neurons to improve the learning of spatio-temporal patterns [2]. A second contribution is to draw an analogy between the HOTS algorithm and Spiking Neural Networks (SNN). Following that analogy, our last contribution is to modify the classification layer and remodel the offline pattern categorization method previously used into an online and event-driven one. This classifier uses the spiking output of the network to define novel time surfaces and we then perform online classification with a neuromimetic implementation of a multinomial logistic regression. Not only do these improvements increase consistently the performances of the network, they also make this event-driven pattern recognition algorithm online and bio-realistic. Results were validated on different datasets: DVS barrel [3], Poker-DVS [4] and N-MNIST [5]. We foresee to develop the SNN version of the method and to extend this fully event-driven approach to more naturalistic tasks, notably for always-on, ultra-fast object categorization. </p> </div> </div> </div>

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