1
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Koparkar A, Warren TL, Charlesworth JD, Shin S, Brainard MS, Veit L. Lesions in a songbird vocal circuit increase variability in song syntax. eLife 2024; 13:RP93272. [PMID: 38635312 PMCID: PMC11026095 DOI: 10.7554/elife.93272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024] Open
Abstract
Complex skills like speech and dance are composed of ordered sequences of simpler elements, but the neuronal basis for the syntactic ordering of actions is poorly understood. Birdsong is a learned vocal behavior composed of syntactically ordered syllables, controlled in part by the songbird premotor nucleus HVC (proper name). Here, we test whether one of HVC's recurrent inputs, mMAN (medial magnocellular nucleus of the anterior nidopallium), contributes to sequencing in adult male Bengalese finches (Lonchura striata domestica). Bengalese finch song includes several patterns: (1) chunks, comprising stereotyped syllable sequences; (2) branch points, where a given syllable can be followed probabilistically by multiple syllables; and (3) repeat phrases, where individual syllables are repeated variable numbers of times. We found that following bilateral lesions of mMAN, acoustic structure of syllables remained largely intact, but sequencing became more variable, as evidenced by 'breaks' in previously stereotyped chunks, increased uncertainty at branch points, and increased variability in repeat numbers. Our results show that mMAN contributes to the variable sequencing of vocal elements in Bengalese finch song and demonstrate the influence of recurrent projections to HVC. Furthermore, they highlight the utility of species with complex syntax in investigating neuronal control of ordered sequences.
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Affiliation(s)
- Avani Koparkar
- Neurobiology of Vocal Communication, Institute for Neurobiology, University of TübingenTübingenGermany
| | - Timothy L Warren
- Howard Hughes Medical Institute and Center for Integrative Neuroscience, University of California San FranciscoSan FranciscoUnited States
- Departments of Horticulture and Integrative Biology, Oregon State UniversityCorvallisUnited States
| | - Jonathan D Charlesworth
- Howard Hughes Medical Institute and Center for Integrative Neuroscience, University of California San FranciscoSan FranciscoUnited States
| | - Sooyoon Shin
- Howard Hughes Medical Institute and Center for Integrative Neuroscience, University of California San FranciscoSan FranciscoUnited States
| | - Michael S Brainard
- Howard Hughes Medical Institute and Center for Integrative Neuroscience, University of California San FranciscoSan FranciscoUnited States
| | - Lena Veit
- Neurobiology of Vocal Communication, Institute for Neurobiology, University of TübingenTübingenGermany
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2
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Berthet M, Coye C, Dezecache G, Kuhn J. Animal linguistics: a primer. Biol Rev Camb Philos Soc 2023; 98:81-98. [PMID: 36189714 PMCID: PMC10091714 DOI: 10.1111/brv.12897] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 08/10/2022] [Accepted: 08/12/2022] [Indexed: 01/12/2023]
Abstract
The evolution of language has been investigated by several research communities, including biologists and linguists, striving to highlight similar linguistic capacities across species. To date, however, no consensus exists on the linguistic capacities of non-human species. Major controversies remain on the use of linguistic terminology, analysis methods and behavioural data collection. The field of 'animal linguistics' has emerged to overcome these difficulties and attempt to reach uniform methods and terminology. This primer is a tutorial review of 'animal linguistics'. It describes the linguistic concepts of semantics, pragmatics and syntax, and proposes minimal criteria to be fulfilled to claim that a given species displays a particular linguistic capacity. Second, it reviews relevant methods successfully applied to the study of communication in animals and proposes a list of useful references to detect and overcome major pitfalls commonly observed in the collection of animal behaviour data. This primer represents a step towards mutual understanding and fruitful collaborations between linguists and biologists.
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Affiliation(s)
- Mélissa Berthet
- Institut Jean Nicod, Département d'études cognitives, ENS, EHESS, CNRS, PSL University, 75005, Paris, France.,Center for the Interdisciplinary Study of Language Evolution, University of Zürich, Affolternstrasse 56, 8050, Zurich, Switzerland.,Department of Comparative Language Science, University of Zürich, Affolternstrasse 56, 8050, Zurich, Switzerland
| | - Camille Coye
- Institut Jean Nicod, Département d'études cognitives, ENS, EHESS, CNRS, PSL University, 75005, Paris, France.,Center for Ecology and Conservation, Bioscience Department, University of Exeter, Penryn Campus, Penryn, TR10 9FE, UK
| | | | - Jeremy Kuhn
- Institut Jean Nicod, Département d'études cognitives, ENS, EHESS, CNRS, PSL University, 75005, Paris, France
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3
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Ivanitskii VV, Marova IM. The Syntactic Organization of Bird Song. BIOL BULL+ 2022. [DOI: 10.1134/s1062359022080076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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4
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Éltető N, Nemeth D, Janacsek K, Dayan P. Tracking human skill learning with a hierarchical Bayesian sequence model. PLoS Comput Biol 2022; 18:e1009866. [PMID: 36449550 PMCID: PMC9744313 DOI: 10.1371/journal.pcbi.1009866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 12/12/2022] [Accepted: 10/31/2022] [Indexed: 12/03/2022] Open
Abstract
Humans can implicitly learn complex perceptuo-motor skills over the course of large numbers of trials. This likely depends on our becoming better able to take advantage of ever richer and temporally deeper predictive relationships in the environment. Here, we offer a novel characterization of this process, fitting a non-parametric, hierarchical Bayesian sequence model to the reaction times of human participants' responses over ten sessions, each comprising thousands of trials, in a serial reaction time task involving higher-order dependencies. The model, adapted from the domain of language, forgetfully updates trial-by-trial, and seamlessly combines predictive information from shorter and longer windows onto past events, weighing the windows proportionally to their predictive power. As the model implies a posterior over window depths, we were able to determine how, and how many, previous sequence elements influenced individual participants' internal predictions, and how this changed with practice. Already in the first session, the model showed that participants had begun to rely on two previous elements (i.e., trigrams), thereby successfully adapting to the most prominent higher-order structure in the task. The extent to which local statistical fluctuations in trigram frequency influenced participants' responses waned over subsequent sessions, as participants forgot the trigrams less and evidenced skilled performance. By the eighth session, a subset of participants shifted their prior further to consider a context deeper than two previous elements. Finally, participants showed resistance to interference and slow forgetting of the old sequence when it was changed in the final sessions. Model parameters for individual participants covaried appropriately with independent measures of working memory and error characteristics. In sum, the model offers the first principled account of the adaptive complexity and nuanced dynamics of humans' internal sequence representations during long-term implicit skill learning.
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Affiliation(s)
- Noémi Éltető
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- * E-mail:
| | - Dezső Nemeth
- Lyon Neuroscience Research Center, Université de Lyon, Lyon, France
- Institute of Psychology, ELTE Eötvös Loránd University, Budapest, Hungary
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | - Karolina Janacsek
- Institute of Psychology, ELTE Eötvös Loránd University, Budapest, Hungary
- Centre for Thinking and Learning, Institute for Lifecourse Development, Universtiy of Greenwich, London, United Kingdom
| | - Peter Dayan
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- University of Tübingen, Tübingen, Germany
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5
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From collocations to call-ocations: using linguistic methods to quantify animal call combinations. Behav Ecol Sociobiol 2022; 76:122. [PMID: 36034316 PMCID: PMC9395491 DOI: 10.1007/s00265-022-03224-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 07/27/2022] [Accepted: 08/01/2022] [Indexed: 11/02/2022]
Abstract
Abstract
Emerging data in a range of non-human animal species have highlighted a latent ability to combine certain pre-existing calls together into larger structures. Currently, however, the quantification of context-specific call combinations has received less attention. This is problematic because animal calls can co-occur with one another simply through chance alone. One common approach applied in language sciences to identify recurrent word combinations is collocation analysis. Through comparing the co-occurrence of two words with how each word combines with other words within a corpus, collocation analysis can highlight above chance, two-word combinations. Here, we demonstrate how this approach can also be applied to non-human animal signal sequences by implementing it on artificially generated data sets of call combinations. We argue collocation analysis represents a promising tool for identifying non-random, communicatively relevant call combinations and, more generally, signal sequences, in animals.
Significance statement
Assessing the propensity for animals to combine calls provides important comparative insights into the complexity of animal vocal systems and the selective pressures such systems have been exposed to. Currently, however, the objective quantification of context-specific call combinations has received less attention. Here we introduce an approach commonly applied in corpus linguistics, namely collocation analysis, and show how this method can be put to use for identifying call combinations more systematically. Through implementing the same objective method, so-called call-ocations, we hope researchers will be able to make more meaningful comparisons regarding animal signal sequencing abilities both within and across systems.
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6
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Cohen Y, Nicholson DA, Sanchioni A, Mallaber EK, Skidanova V, Gardner TJ. Automated annotation of birdsong with a neural network that segments spectrograms. eLife 2022; 11:63853. [PMID: 35050849 PMCID: PMC8860439 DOI: 10.7554/elife.63853] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 01/19/2022] [Indexed: 11/13/2022] Open
Abstract
Songbirds provide a powerful model system for studying sensory-motor learning. However, many analyses of birdsong require time-consuming, manual annotation of its elements, called syllables. Automated methods for annotation have been proposed, but these methods assume that audio can be cleanly segmented into syllables, or they require carefully tuning multiple statistical models. Here we present TweetyNet: a single neural network model that learns how to segment spectrograms of birdsong into annotated syllables. We show that TweetyNet mitigates limitations of methods that rely on segmented audio. We also show that TweetyNet performs well across multiple individuals from two species of songbirds, Bengalese finches and canaries. Lastly, we demonstrate that using TweetyNet we can accurately annotate very large datasets containing multiple days of song, and that these predicted annotations replicate key findings from behavioral studies. In addition, we provide open-source software to assist other researchers, and a large dataset of annotated canary song that can serve as a benchmark. We conclude that TweetyNet makes it possible to address a wide range of new questions about birdsong.
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Affiliation(s)
- Yarden Cohen
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
| | | | - Alexa Sanchioni
- Department of Biology, Boston University, Boston, United States
| | | | | | - Timothy J Gardner
- Phil and Penny Knight Campus for Accelerating Scientific Impact, University of Oregon, Eugene, United States
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7
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Sainburg T, Gentner TQ. Toward a Computational Neuroethology of Vocal Communication: From Bioacoustics to Neurophysiology, Emerging Tools and Future Directions. Front Behav Neurosci 2021; 15:811737. [PMID: 34987365 PMCID: PMC8721140 DOI: 10.3389/fnbeh.2021.811737] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 11/29/2021] [Indexed: 11/23/2022] Open
Abstract
Recently developed methods in computational neuroethology have enabled increasingly detailed and comprehensive quantification of animal movements and behavioral kinematics. Vocal communication behavior is well poised for application of similar large-scale quantification methods in the service of physiological and ethological studies. This review describes emerging techniques that can be applied to acoustic and vocal communication signals with the goal of enabling study beyond a small number of model species. We review a range of modern computational methods for bioacoustics, signal processing, and brain-behavior mapping. Along with a discussion of recent advances and techniques, we include challenges and broader goals in establishing a framework for the computational neuroethology of vocal communication.
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Affiliation(s)
- Tim Sainburg
- Department of Psychology, University of California, San Diego, La Jolla, CA, United States
- Center for Academic Research & Training in Anthropogeny, University of California, San Diego, La Jolla, CA, United States
| | - Timothy Q. Gentner
- Department of Psychology, University of California, San Diego, La Jolla, CA, United States
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, United States
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, United States
- Kavli Institute for Brain and Mind, University of California, San Diego, La Jolla, CA, United States
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8
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Sawant S, Arvind C, Joshi V, Robin VV. Spectrogram cross‐correlation can be used to measure the complexity of bird vocalizations. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Suyash Sawant
- Department of Biology Indian Institute of Science Education and Research (IISER) Tirupati Tirupati India
| | - Chiti Arvind
- Department of Biology Indian Institute of Science Education and Research (IISER) Tirupati Tirupati India
| | - Viral Joshi
- Department of Biology Indian Institute of Science Education and Research (IISER) Tirupati Tirupati India
| | - V. V. Robin
- Department of Biology Indian Institute of Science Education and Research (IISER) Tirupati Tirupati India
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9
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Veit L, Tian LY, Monroy Hernandez CJ, Brainard MS. Songbirds can learn flexible contextual control over syllable sequencing. eLife 2021; 10:61610. [PMID: 34060473 PMCID: PMC8169114 DOI: 10.7554/elife.61610] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 04/25/2021] [Indexed: 11/23/2022] Open
Abstract
The flexible control of sequential behavior is a fundamental aspect of speech, enabling endless reordering of a limited set of learned vocal elements (syllables or words). Songbirds are phylogenetically distant from humans but share both the capacity for vocal learning and neural circuitry for vocal control that includes direct pallial-brainstem projections. Based on these similarities, we hypothesized that songbirds might likewise be able to learn flexible, moment-by-moment control over vocalizations. Here, we demonstrate that Bengalese finches (Lonchura striata domestica), which sing variable syllable sequences, can learn to rapidly modify the probability of specific sequences (e.g. ‘ab-c’ versus ‘ab-d’) in response to arbitrary visual cues. Moreover, once learned, this modulation of sequencing occurs immediately following changes in contextual cues and persists without external reinforcement. Our findings reveal a capacity in songbirds for learned contextual control over syllable sequencing that parallels human cognitive control over syllable sequencing in speech. Human speech and birdsong share numerous parallels. Both humans and birds learn their vocalizations during critical phases early in life, and both learn by imitating adults. Moreover, both humans and songbirds possess specific circuits in the brain that connect the forebrain to midbrain vocal centers. Humans can flexibly control what they say and how by reordering a fixed set of syllables into endless combinations, an ability critical to human speech and language. Birdsongs also vary depending on their context, and melodies to seduce a mate will be different from aggressive songs to warn other males to stay away. However, so far it was unclear whether songbirds are also capable of modifying songs independent of social or other naturally relevant contexts. To test whether birds can control their songs in a purposeful way, Veit et al. trained adult male Bengalese finches to change the sequence of their songs in response to random colored lights that had no natural meaning to the birds. A specific computer program was used to detect different variations on a theme that the bird naturally produced (for example, “ab-c” versus “ab-d”), and rewarded birds for singing one sequence when the light was yellow, and the other when it was green. Gradually, the finches learned to modify their songs and were able to switch between the appropriate sequences as soon as the light cues changed. This ability persisted for days, even without any further training. This suggests that songbirds can learn to flexibly and purposefully modify the way in which they sequence the notes in their songs, in a manner that parallels how humans control syllable sequencing in speech. Moreover, birds can learn to do this ‘on command’ in response to an arbitrarily chosen signal, even if it is not something that would impact their song in nature. Songbirds are an important model to study brain circuits involved in vocal learning. They are one of the few animals that, like humans, learn their vocalizations by imitating conspecifics. The finding that they can also flexibly control vocalizations may help shed light on the interactions between cognitive processing and sophisticated vocal learning abilities.
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Affiliation(s)
- Lena Veit
- Center for Integrative Neuroscience and Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, United States
| | - Lucas Y Tian
- Center for Integrative Neuroscience and Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, United States
| | - Christian J Monroy Hernandez
- Center for Integrative Neuroscience and Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, United States
| | - Michael S Brainard
- Center for Integrative Neuroscience and Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, United States
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10
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Cohen Y, Shen J, Semu D, Leman DP, Liberti WA, Perkins LN, Liberti DC, Kotton DN, Gardner TJ. Hidden neural states underlie canary song syntax. Nature 2020; 582:539-544. [PMID: 32555461 PMCID: PMC7380505 DOI: 10.1038/s41586-020-2397-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Accepted: 03/26/2020] [Indexed: 01/12/2023]
Abstract
Coordinated skills such as speech or dance involve sequences of actions that follow syntactic rules in which transitions between elements depend on the identity and order of past actions. Canary songs are comprised of repeated syllables, called phrases, and the ordering of these phrases follows long-range rules1, where the choice of what to sing depends on song structure many seconds prior. The neural substrates that support these long-range correlations are unknown. Using miniature head-mounted microscopes and cell-type-specific genetic tools, we observed neural activity in the premotor nucleus HVC2–4 as canaries explore various phrase sequences in their repertoire. We find neurons that encode past transitions, extending over 4 phrases and spanning up to 4 seconds and 40 syllables. These neurons preferentially encode past actions rather than future actions, can reflect more than a single song history, and occur mostly during the rare phrases that involve history-dependent transitions in song. These findings demonstrate that HVC dynamics includes “hidden states” not reflected in ongoing behavior – states that carry information about prior actions. These states provide a possible substrate to control syntax transitions governed by long-range rules.
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Affiliation(s)
- Yarden Cohen
- Department of Biology, Boston University, Boston, MA, USA.
| | - Jun Shen
- Boston University Center for Systems Neuroscience, Boston, MA, USA
| | - Dawit Semu
- Department of Biology, Boston University, Boston, MA, USA
| | - Daniel P Leman
- Department of Biology, Boston University, Boston, MA, USA
| | - William A Liberti
- Department of Biology, Boston University, Boston, MA, USA.,Department of Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, CA, USA
| | | | - Derek C Liberti
- Center for Regenerative Medicine of Boston University and Boston Medical Center, Boston, MA, USA.,The Pulmonary Center, Boston University School of Medicine, Boston, MA, USA.,Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Darrell N Kotton
- Center for Regenerative Medicine of Boston University and Boston Medical Center, Boston, MA, USA.,The Pulmonary Center, Boston University School of Medicine, Boston, MA, USA.,Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Timothy J Gardner
- Department of Biology, Boston University, Boston, MA, USA. .,Phil and Penny Knight Campus for Accelerating Scientific Impact, University of Oregon, Eugene, OR, USA.
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11
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Mizuhara T, Okanoya K. Do songbirds hear songs syllable by syllable? Behav Processes 2020; 174:104089. [PMID: 32105758 DOI: 10.1016/j.beproc.2020.104089] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 01/30/2020] [Accepted: 02/23/2020] [Indexed: 12/29/2022]
Abstract
Songbirds as vocal learners have been one of the most popular model species to investigate the biological prerequisite to human language. Their songs consist of syllables, which appear as pulse trains in sound spectrograms. When describing the song sequence, researchers consider the syllable to be the unit of the song. Moreover, artificial grammar learning studies asking whether songbirds recognize structural regularities observed in human language often design stimuli using song syllables as components. However, whether syllables are perceptual units is yet to be determined. We found that Bengalese finches, a species of songbird, responded significantly less to one specific syllable when it was temporally placed close to the preceding syllable. The proximity, or silent interval was within the range of what is produced in the natural songs of both Bengalese and zebra finches, and what has been used in other artificial grammar learning studies using zebra finches. Our results suggest the need for a reinterpretation of the description of birdsong structure and of previous artificial grammar learning studies.
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Affiliation(s)
- Tomoko Mizuhara
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Meguro, Tokyo 153-8902, Japan.
| | - Kazuo Okanoya
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Meguro, Tokyo 153-8902, Japan.
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12
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Statistical learning for vocal sequence acquisition in a songbird. Sci Rep 2020; 10:2248. [PMID: 32041978 PMCID: PMC7010765 DOI: 10.1038/s41598-020-58983-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Accepted: 01/17/2020] [Indexed: 01/31/2023] Open
Abstract
Birdsong is a learned communicative behavior that consists of discrete acoustic elements (“syllables”) that are sequenced in a controlled manner. While the learning of the acoustic structure of syllables has been extensively studied, relatively little is known about sequence learning in songbirds. Statistical learning could contribute to the acquisition of vocal sequences, and we investigated the nature and extent of sequence learning at various levels of song organization in the Bengalese finch, Lonchura striata var. domestica. We found that, under semi-natural conditions, pupils (sons) significantly reproduced the sequence statistics of their tutor’s (father’s) songs at multiple levels of organization (e.g., syllable repertoire, prevalence, and transitions). For example, the probability of syllable transitions at “branch points” (relatively complex sequences that are followed by multiple types of transitions) were significantly correlated between the songs of tutors and pupils. We confirmed the contribution of learning to sequence similarities between fathers and sons by experimentally tutoring juvenile Bengalese finches with the songs of unrelated tutors. We also discovered that the extent and fidelity of sequence similarities between tutors and pupils were significantly predicted by the prevalence of sequences in the tutor’s song and that distinct types of sequence modifications (e.g., syllable additions or deletions) followed distinct patterns. Taken together, these data provide compelling support for the role of statistical learning in vocal production learning and identify factors that could modulate the extent of vocal sequence learning.
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13
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Sainburg T, Theilman B, Thielk M, Gentner TQ. Parallels in the sequential organization of birdsong and human speech. Nat Commun 2019; 10:3636. [PMID: 31406118 PMCID: PMC6690877 DOI: 10.1038/s41467-019-11605-y] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Accepted: 07/09/2019] [Indexed: 11/08/2022] Open
Abstract
Human speech possesses a rich hierarchical structure that allows for meaning to be altered by words spaced far apart in time. Conversely, the sequential structure of nonhuman communication is thought to follow non-hierarchical Markovian dynamics operating over only short distances. Here, we show that human speech and birdsong share a similar sequential structure indicative of both hierarchical and Markovian organization. We analyze the sequential dynamics of song from multiple songbird species and speech from multiple languages by modeling the information content of signals as a function of the sequential distance between vocal elements. Across short sequence-distances, an exponential decay dominates the information in speech and birdsong, consistent with underlying Markovian processes. At longer sequence-distances, the decay in information follows a power law, consistent with underlying hierarchical processes. Thus, the sequential organization of acoustic elements in two learned vocal communication signals (speech and birdsong) shows functionally equivalent dynamics, governed by similar processes.
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Affiliation(s)
- Tim Sainburg
- Department of Psychology, University of California, UC San Diego, La Jolla, CA, 92093, USA
- Center for Academic Research & Training in Anthropogeny, UC San Diego, La Jolla, CA, 92093, USA
| | - Brad Theilman
- Neurosciences Graduate Program, University of California, UC San Diego, La Jolla, CA, 92093, USA
| | - Marvin Thielk
- Neurosciences Graduate Program, University of California, UC San Diego, La Jolla, CA, 92093, USA
| | - Timothy Q Gentner
- Department of Psychology, University of California, UC San Diego, La Jolla, CA, 92093, USA.
- Neurosciences Graduate Program, University of California, UC San Diego, La Jolla, CA, 92093, USA.
- Neurobiology Section, Division of Biological Sciences, UC San Diego, La Jolla, CA, 92093, USA.
- Kavli Institute for Brain and Mind, La Jolla, CA, 92093, USA.
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14
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Koumura T, Okanoya K. Distributed representation of discrete sequential vocalization in the Bengalese finch ( Lonchura striata var. domestica). BIOACOUSTICS 2019. [DOI: 10.1080/09524622.2019.1607558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Takuya Koumura
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
| | - Kazuo Okanoya
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
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15
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Berthet M, Mesbahi G, Pajot A, Cäsar C, Neumann C, Zuberbühler K. Titi monkeys combine alarm calls to create probabilistic meaning. SCIENCE ADVANCES 2019; 5:eaav3991. [PMID: 31123704 PMCID: PMC6527438 DOI: 10.1126/sciadv.aav3991] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 04/09/2019] [Indexed: 05/10/2023]
Abstract
Previous work suggested that titi monkeys Callicebus nigrifrons combine two alarm calls, the A- and B-calls, to communicate about predator type and location. To explore how listeners process these sequences, we recorded alarm call sequences of six free-ranging groups exposed to terrestrial and aerial predator models, placed on the ground or in the canopy, and used multimodel inference to assess the information encoded in the sequences. We then carried out playback experiments to identify the features used by listeners to react to the available information. Results indicated that information about predator type and location were encoded by the proportion of B-call pairs relative to all call pairs of the sequence (i.e., proportion of BB-grams). The results suggest that the meaning of the sequence is not conveyed in a categorical but probabilistic manner. We discuss the implications of these findings for current theories of animal communication and language evolution.
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Affiliation(s)
- Mélissa Berthet
- Institute of Biology, University of Neuchâtel, Neuchâtel, Switzerland
- Institut Jean Nicod, Département d’études cognitives, ENS, EHESS, CNRS, PSL Research University, Paris, France
| | - Geoffrey Mesbahi
- Institute of Biology, University of Neuchâtel, Neuchâtel, Switzerland
| | - Aude Pajot
- Institute of Biology, University of Neuchâtel, Neuchâtel, Switzerland
| | - Cristiane Cäsar
- School of Psychology and Neuroscience, University of St Andrews, St Andrews, UK
- Natural Sciences Museum PUC Minas, Belo Horizonte, Brazil
- Bicho do Mato Research Institute, Belo Horizonte, Brazil
| | - Christof Neumann
- Institute of Biology, University of Neuchâtel, Neuchâtel, Switzerland
- Laboratoire de sciences cognitives et de psycholinguistique, Département d’études cognitives, ENS, EHESS, CNRS, PSL University, Paris, France
| | - Klaus Zuberbühler
- Institute of Biology, University of Neuchâtel, Neuchâtel, Switzerland
- School of Psychology and Neuroscience, University of St Andrews, St Andrews, UK
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Hedley RW, Logue DM, Benedict L, Mennill DJ. Assessing the similarity of song-type transitions among birds: evidence for interspecies variation. Anim Behav 2018. [DOI: 10.1016/j.anbehav.2018.04.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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17
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Temperature Manipulation in Songbird Brain Implicates the Premotor Nucleus HVC in Birdsong Syntax. J Neurosci 2017; 37:2600-2611. [PMID: 28159910 DOI: 10.1523/jneurosci.1827-16.2017] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Revised: 01/03/2017] [Accepted: 01/17/2017] [Indexed: 01/04/2023] Open
Abstract
Variable motor sequences of animals are often structured and can be described by probabilistic transition rules between action elements. Examples include the songs of many songbird species such as the Bengalese finch, which consist of stereotypical syllables sequenced according to probabilistic rules (song syntax). The neural mechanisms behind such rules are poorly understood. Here, we investigate where the song syntax is encoded in the brain of the Bengalese finch by rapidly and reversibly manipulating the temperature in the song production pathway. Cooling the premotor nucleus HVC (proper name) slows down the song tempo, consistent with the idea that HVC controls moment-to-moment timings of acoustic features in the syllables. More importantly, cooling HVC alters the transition probabilities between syllables. Cooling HVC reduces the number of repetitions of long-repeated syllables and increases the randomness of syllable sequences. In contrast, cooling the downstream motor area RA (robust nucleus of the acropallium), which is critical for singing, does not affect the song syntax. Unilateral cooling of HVC shows that control of syllables is mostly lateralized to the left HVC, whereas transition probabilities between the syllables can be affected by cooling HVC in either hemisphere to varying degrees. These results show that HVC is a key site for encoding song syntax in the Bengalese finch. HVC is thus involved both in encoding timings within syllables and in sequencing probabilistic transitions between syllables. Our finding suggests that probabilistic selections and fine-grained timings of action elements can be integrated within the same neural circuits.SIGNIFICANCE STATEMENT Many animal behaviors such as birdsong consist of variable sequences of discrete actions. Where and how the probabilistic rules of such sequences are encoded in the brain is poorly understood. We locally and reversibly cooled brain areas in songbirds during singing. Mild cooling of area HVC in the Bengalese finch brain-a premotor area homologous to the mammalian premotor cortex-alters the statistics of the syllable sequences, suggesting that HVC is critical for birdsong sequences. HVC is also known for controlling moment-to-moment timings within syllables. Our results show that timing and probabilistic sequencing of actions can share the same neural circuits in local brain areas.
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18
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Berman GJ, Bialek W, Shaevitz JW. Predictability and hierarchy in Drosophila behavior. Proc Natl Acad Sci U S A 2016; 113:11943-11948. [PMID: 27702892 DOI: 10.1101/052928] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023] Open
Abstract
Even the simplest of animals exhibit behavioral sequences with complex temporal dynamics. Prominent among the proposed organizing principles for these dynamics has been the idea of a hierarchy, wherein the movements an animal makes can be understood as a set of nested subclusters. Although this type of organization holds potential advantages in terms of motion control and neural circuitry, measurements demonstrating this for an animal's entire behavioral repertoire have been limited in scope and temporal complexity. Here, we use a recently developed unsupervised technique to discover and track the occurrence of all stereotyped behaviors performed by fruit flies moving in a shallow arena. Calculating the optimally predictive representation of the fly's future behaviors, we show that fly behavior exhibits multiple time scales and is organized into a hierarchical structure that is indicative of its underlying behavioral programs and its changing internal states.
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Affiliation(s)
- Gordon J Berman
- Joseph Henry Laboratories of Physics, Princeton University, Princeton, NJ 08544; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544
| | - William Bialek
- Joseph Henry Laboratories of Physics, Princeton University, Princeton, NJ 08544; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544
| | - Joshua W Shaevitz
- Joseph Henry Laboratories of Physics, Princeton University, Princeton, NJ 08544; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544
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19
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Abstract
Even the simplest of animals exhibit behavioral sequences with complex temporal dynamics. Prominent among the proposed organizing principles for these dynamics has been the idea of a hierarchy, wherein the movements an animal makes can be understood as a set of nested subclusters. Although this type of organization holds potential advantages in terms of motion control and neural circuitry, measurements demonstrating this for an animal's entire behavioral repertoire have been limited in scope and temporal complexity. Here, we use a recently developed unsupervised technique to discover and track the occurrence of all stereotyped behaviors performed by fruit flies moving in a shallow arena. Calculating the optimally predictive representation of the fly's future behaviors, we show that fly behavior exhibits multiple time scales and is organized into a hierarchical structure that is indicative of its underlying behavioral programs and its changing internal states.
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20
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Sasahara K, Tchernichovski O, Takahasi M, Suzuki K, Okanoya K. A rhythm landscape approach to the developmental dynamics of birdsong. J R Soc Interface 2016; 12:rsif.2015.0802. [PMID: 26538559 PMCID: PMC4685852 DOI: 10.1098/rsif.2015.0802] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Unlike simple biological rhythms, the rhythm of the oscine bird song is a learned time series of diverse sounds that change dynamically during vocal ontogeny. How to quantify rhythm development is one of the most important challenges in behavioural biology. Here, we propose a simple method, called ‘rhythm landscape’, to visualize and quantify how rhythm structure, which is measured as durational patterns of sounds and silences, emerges and changes over development. Applying this method to the development of Bengalese finch songs, we show that the rhythm structure begins with a broadband rhythm that develops into diverse rhythms largely through branching from precursors. Furthermore, an information-theoretic measure, the Jensen–Shannon divergence, was used to characterize the crystallization process of birdsong rhythm, which started with a high rate of rhythm change and progressed to a stage of slow refinement. This simple method provides a useful description of rhythm development, thereby helping to reveal key temporal constraints on complex biological rhythms.
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Affiliation(s)
- Kazutoshi Sasahara
- Department of Complex Systems Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan Laboratory for Biolinguistics, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
| | - Ofer Tchernichovski
- Department of Psychology, Hunter College, City University of New York, 695 Park Avenue, New York, NY 10065, USA
| | - Miki Takahasi
- Laboratory for Biolinguistics, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
| | - Kenta Suzuki
- Laboratory for Biolinguistics, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan Faculty of Health Sciences, Nihon Institute of Medical Science, 1276 Shimogawara, Moroyama-machi, Iruma-gun, Saitama 350-0435, Japan
| | - Kazuo Okanoya
- Laboratory for Biolinguistics, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan Department of Life Sciences, University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
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Abstract
Complex vocal signals, such as birdsong, contain acoustic elements that differ in both order and duration. These elements may convey socially relevant meaning, both independently and through their interactions, yet statistical methods that combine order and duration data to extract meaning have not, to our knowledge, been fully developed. Here we design novel semi-Markov methods, Bayesian estimation and classification trees to extract order and duration information from behavioural sequences and apply these methods to songs produced by male European starlings, Sturnus vulgaris, in two social contexts in which the function of song differs: a spring (breeding) and autumn (nonbreeding) context. Additionally, previous data indicate that damage to the medial preoptic nucleus (POM), a brain area known to regulate male sexually motivated behaviour, affects structural aspects of starling song such that males in a sexually relevant context (i.e. spring) sing shorter songs than appropriate for this context. We further test the utility of our statistical approach by comparing attributes of song structure in POM-lesioned males to song produced by control spring and autumn males. Spring and autumn songs were statistically separable based on the duration and order of phrase types. Males produced more structurally complex aspects of song in spring than in autumn. Spring song was also longer and more stereotyped than autumn song, both attributes used by females to select mates. Songs produced by POM-lesioned males in some cases fell between measures of spring and autumn songs but differed most from songs produced by autumn males. Overall, these statistical methods can effectively extract biologically meaningful information contained in many behavioural sequences given sufficient sample sizes and replication numbers.
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Affiliation(s)
- Sarah J. Alger
- Department of Biology, University of Wisconsin-Stevens Point, WI, U.S.A., Department of Zoology, University of Wisconsin-Madison, WI, U.S.A., Department of Statistics, University of Wisconsin-Madison, WI, U.S.A
| | - Bret R. Larget
- Department of Statistics, University of Wisconsin-Madison, WI, U.S.A., Department of Botany, University of Wisconsin-Madison, WI, U.S.A
| | - Lauren V. Riters
- Department of Zoology, University of Wisconsin-Madison, WI, U.S.A., Correspondence: L. V. Riters, Department of Zoology, 428 Birge Hall, 430 Lincoln Drive, University of Wisconsin-Madison, Madison, WI 53706, U.S.A. (L. V. Riters)
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22
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Abstract
Complex vocal signals, such as birdsong, contain acoustic elements that differ in both order and duration. These elements may convey socially relevant meaning, both independently and through their interactions, yet statistical methods that combine order and duration data to extract meaning have not, to our knowledge, been fully developed. Here we design novel semi-Markov methods, Bayesian estimation and classification trees to extract order and duration information from behavioural sequences and apply these methods to songs produced by male European starlings, Sturnus vulgaris, in two social contexts in which the function of song differs: a spring (breeding) and autumn (nonbreeding) context. Additionally, previous data indicate that damage to the medial preoptic nucleus (POM), a brain area known to regulate male sexually motivated behaviour, affects structural aspects of starling song such that males in a sexually relevant context (i.e. spring) sing shorter songs than appropriate for this context. We further test the utility of our statistical approach by comparing attributes of song structure in POM-lesioned males to song produced by control spring and autumn males. Spring and autumn songs were statistically separable based on the duration and order of phrase types. Males produced more structurally complex aspects of song in spring than in autumn. Spring song was also longer and more stereotyped than autumn song, both attributes used by females to select mates. Songs produced by POM-lesioned males in some cases fell between measures of spring and autumn songs but differed most from songs produced by autumn males. Overall, these statistical methods can effectively extract biologically meaningful information contained in many behavioural sequences given sufficient sample sizes and replication numbers.
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Perinatally Influenced Autonomic System Fluctuations Drive Infant Vocal Sequences. Curr Biol 2016; 26:1249-60. [PMID: 27068420 DOI: 10.1016/j.cub.2016.03.023] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Revised: 02/29/2016] [Accepted: 03/08/2016] [Indexed: 11/22/2022]
Abstract
The variable vocal behavior of human infants is the scaffolding upon which speech and social interactions develop. It is important to know what factors drive this developmentally critical behavioral output. Using marmoset monkeys as a model system, we first addressed whether the initial conditions for vocal output and its sequential structure are perinatally influenced. Using dizygotic twins and Markov analyses of their vocal sequences, we found that in the first postnatal week, twins had more similar vocal sequences to each other than to their non-twin siblings. Moreover, both twins and their siblings had more vocal sequence similarity with each other than with non-sibling infants. Using electromyography, we then investigated the physiological basis of vocal sequence structure by measuring respiration and arousal levels (via changes in heart rate). We tested the hypothesis that early-life influences on vocal output are via fluctuations of the autonomic nervous system (ANS) mediated by vocal biomechanics. We found that arousal levels fluctuate at ∼0.1 Hz (the Mayer wave) and that this slow oscillation modulates the amplitude of the faster, ∼1.0 Hz respiratory rhythm. The systematic changes in respiratory amplitude result in the different vocalizations that comprise infant vocal sequences. Among twins, the temporal structure of arousal level changes was similar and therefore indicates why their vocal sequences were similar. Our study shows that vocal sequences are tightly linked to respiratory patterns that are modulated by ANS fluctuations and that the temporal structure of ANS fluctuations is perinatally influenced.
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Complexity, Predictability and Time Homogeneity of Syntax in the Songs of Cassin's Vireo (Vireo cassinii). PLoS One 2016; 11:e0150822. [PMID: 27050537 PMCID: PMC4822860 DOI: 10.1371/journal.pone.0150822] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2015] [Accepted: 02/20/2016] [Indexed: 11/19/2022] Open
Abstract
Many species of animals deliver vocalizations in sequences presumed to be governed by internal rules, though the nature and complexity of these syntactical rules have been investigated in relatively few species. Here I present an investigation into the song syntax of fourteen male Cassin's Vireos (Vireo cassinii), a species whose song sequences are highly temporally structured. I compare their song sequences to three candidate models of varying levels of complexity-zero-order, first-order and second-order Markov models-and employ novel methods to interpolate between these three models. A variety of analyses, including sequence simulations, Fisher's exact tests, and model likelihood analyses, showed that the songs of this species are too complex to be described by a zero-order or first-order Markov model. The model that best fit the data was intermediate in complexity between a first- and second-order model, though I also present evidence that some transition probabilities are conditioned on up to three preceding phrases. In addition, sequences were shown to be predictable with more than 54% accuracy overall, and predictability was positively correlated with the rate of song delivery. An assessment of the time homogeneity of syntax showed that transition probabilities between phrase types are largely stable over time, but that there was some evidence for modest changes in syntax within and between breeding seasons, a finding that I interpret to represent changes in breeding stage and social context rather than irreversible, secular shifts in syntax over time. These findings constitute a valuable addition to our understanding of bird song syntax in free-living birds, and will contribute to future attempts to understand the evolutionary importance of bird song syntax in avian communication.
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25
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Disentangling canid howls across multiple species and subspecies: Structure in a complex communication channel. Behav Processes 2016; 124:149-57. [DOI: 10.1016/j.beproc.2016.01.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2015] [Revised: 01/15/2016] [Accepted: 01/20/2016] [Indexed: 11/22/2022]
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Kershenbaum A, Blumstein DT, Roch MA, Akçay Ç, Backus G, Bee MA, Bohn K, Cao Y, Carter G, Cäsar C, Coen M, DeRuiter SL, Doyle L, Edelman S, Ferrer-i-Cancho R, Freeberg TM, Garland EC, Gustison M, Harley HE, Huetz C, Hughes M, Bruno JH, Ilany A, Jin DZ, Johnson M, Ju C, Karnowski J, Lohr B, Manser MB, McCowan B, Mercado E, Narins PM, Piel A, Rice M, Salmi R, Sasahara K, Sayigh L, Shiu Y, Taylor C, Vallejo EE, Waller S, Zamora-Gutierrez V. Acoustic sequences in non-human animals: a tutorial review and prospectus. Biol Rev Camb Philos Soc 2016; 91:13-52. [PMID: 25428267 PMCID: PMC4444413 DOI: 10.1111/brv.12160] [Citation(s) in RCA: 138] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2014] [Revised: 10/02/2014] [Accepted: 10/15/2014] [Indexed: 11/30/2022]
Abstract
Animal acoustic communication often takes the form of complex sequences, made up of multiple distinct acoustic units. Apart from the well-known example of birdsong, other animals such as insects, amphibians, and mammals (including bats, rodents, primates, and cetaceans) also generate complex acoustic sequences. Occasionally, such as with birdsong, the adaptive role of these sequences seems clear (e.g. mate attraction and territorial defence). More often however, researchers have only begun to characterise - let alone understand - the significance and meaning of acoustic sequences. Hypotheses abound, but there is little agreement as to how sequences should be defined and analysed. Our review aims to outline suitable methods for testing these hypotheses, and to describe the major limitations to our current and near-future knowledge on questions of acoustic sequences. This review and prospectus is the result of a collaborative effort between 43 scientists from the fields of animal behaviour, ecology and evolution, signal processing, machine learning, quantitative linguistics, and information theory, who gathered for a 2013 workshop entitled, 'Analysing vocal sequences in animals'. Our goal is to present not just a review of the state of the art, but to propose a methodological framework that summarises what we suggest are the best practices for research in this field, across taxa and across disciplines. We also provide a tutorial-style introduction to some of the most promising algorithmic approaches for analysing sequences. We divide our review into three sections: identifying the distinct units of an acoustic sequence, describing the different ways that information can be contained within a sequence, and analysing the structure of that sequence. Each of these sections is further subdivided to address the key questions and approaches in that area. We propose a uniform, systematic, and comprehensive approach to studying sequences, with the goal of clarifying research terms used in different fields, and facilitating collaboration and comparative studies. Allowing greater interdisciplinary collaboration will facilitate the investigation of many important questions in the evolution of communication and sociality.
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Affiliation(s)
- Arik Kershenbaum
- National Institute for Mathematical and Biological Synthesis, 1122 Volunteer Blvd., Suite 106, University of Tennessee, Knoxville, TN 37996-3410, USA
- Department of Zoology, University of Cambridge, Downing Street, Cambridge, CB2 3EJ, UK
| | - Daniel T. Blumstein
- Department of Ecology and Evolutionary Biology, University of California Los Angeles, 621 Charles E. Young Drive South, Los Angeles, CA 90095-1606, USA
| | - Marie A. Roch
- Department of Computer Science, San Diego State University, 5500 Campanile Dr, San Diego, CA 92182, USA
| | - Çağlar Akçay
- Lab of Ornithology, Cornell University, 159 Sapsucker Woods Rd, Ithaca, NY 14850, USA
| | - Gregory Backus
- Department of Biomathematics, North Carolina State University, Raleigh, NC 27607, USA
| | - Mark A. Bee
- Department of Ecology, Evolution and Behavior, University of Minnesota, 100 Ecology Building, 1987 Upper Buford Cir, Falcon Heights, MN 55108, USA
| | - Kirsten Bohn
- Integrated Science, Florida International University, Modesto Maidique Campus, 11200 SW 8th Street, AHC-4, 351, Miami, FL 33199, USA
| | - Yan Cao
- Department of Mathematical Sciences, University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX 75080, USA
| | - Gerald Carter
- Biological Sciences Graduate Program, University of Maryland, College Park, MD 20742, USA
| | - Cristiane Cäsar
- Department of Psychology & Neuroscience, University of St. Andrews, St Mary’s Quad South Street, St Andrews, Fife, KY16 9JP, UK
| | - Michael Coen
- Department of Biostatistics and Medical Informatics, University of Wisconsin, K6/446 Clinical Sciences Center, 600 Highland Avenue, Madison, WI 53792-4675, USA
| | - Stacy L. DeRuiter
- School of Mathematics and Statistics, University of St. Andrews, St Andrews, KY16 9SS, UK
| | - Laurance Doyle
- Carl Sagan Center for the Study of Life in the Universe, SETI Institute, 189 Bernardo Ave, Suite 100, Mountain View, CA 94043, USA
| | - Shimon Edelman
- Department of Psychology, Cornell University, 211 Uris Hall, Ithaca, NY 14853-7601, USA
| | - Ramon Ferrer-i-Cancho
- Department of Computer Science, Universitat Politecnica de Catalunya, (Catalonia), Calle Jordi Girona, 31, 08034 Barcelona, Spain
| | - Todd M. Freeberg
- Department of Psychology, University of Tennessee, Austin Peay Building, Knoxville, Tennessee 37996, USA
| | - Ellen C. Garland
- National Marine Mammal Laboratory, AFSC/NOAA, 7600 Sand Point Way N.E., Seattle, Washington 98115, USA
| | - Morgan Gustison
- Department of Psychology, University of Michigan, 530 Church St, Ann Arbor, MI 48109, USA
| | - Heidi E. Harley
- Division of Social Sciences, New College of Florida, 5800 Bay Shore Rd, Sarasota, FL 34243, USA
| | - Chloé Huetz
- CNPS, CNRS UMR 8195, Université Paris-Sud, UMR 8195, Batiments 440-447, Rue Claude Bernard, 91405 Orsay, France
| | - Melissa Hughes
- Department of Biology, College of Charleston, 66 George St, Charleston, SC 29424, USA
| | - Julia Hyland Bruno
- Department of Psychology, Hunter College and the Graduate Center, The City University of New York, 365 Fifth Avenue, New York, NY 10016, USA
| | - Amiyaal Ilany
- National Institute for Mathematical and Biological Synthesis, 1122 Volunteer Blvd., Suite 106, University of Tennessee, Knoxville, TN 37996-3410, USA
| | - Dezhe Z. Jin
- Department of Physics, Pennsylvania State University, 104 Davey Lab, University Park, PA 16802-6300, USA
| | - Michael Johnson
- Department of Electrical and Computer Engineering, Marquette University, 1515 W. Wisconsin Ave., Milwaukee, WI 53233, USA
| | - Chenghui Ju
- Department of Biology, Queen College, The City Univ. of New York, 65-30 Kissena Blvd., Flushing, New York 11367, USA
| | - Jeremy Karnowski
- Department of Cognitive Science, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0515, USA
| | - Bernard Lohr
- Department of Biological Sciences, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
| | - Marta B. Manser
- Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Brenda McCowan
- Department of Veterinary Medicine, University of California Davis, 1 Peter J Shields Ave, Davis, CA 95616, USA
| | - Eduardo Mercado
- Department of Psychology; Evolution, Ecology, & Behavior, University at Buffalo, The State University of New York, Park Hall Room 204, Buffalo, NY 14260-4110, USA
| | - Peter M. Narins
- Department of Integrative Biology & Physiology, University of California Los Angeles, 612 Charles E. Young Drive East, Los Angeles, CA 90095-7246, USA
| | - Alex Piel
- Division of Biological Anthropology, University of Cambridge, Pembroke Street Cambridge, CB2 3QG, UK
| | - Megan Rice
- Department of Psychology, California State University San Marcos, 333 S. Twin Oaks Valley Rd., San Marcos, CA 92096-0001, USA
| | - Roberta Salmi
- Department of Anthropology, University of Georgia at Athens, 355 S Jackson St, Athens, GA 30602, USA
| | - Kazutoshi Sasahara
- Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan
| | - Laela Sayigh
- Biology Department, Woods Hole Oceanographic Institution, 86 Water St, Woods Hole, MA 02543, USA
| | - Yu Shiu
- Lab of Ornithology, Cornell University, 159 Sapsucker Woods Rd, Ithaca, NY 14850, USA
| | - Charles Taylor
- Department of Ecology and Evolutionary Biology, University of California Los Angeles, 621 Charles E. Young Drive South, Los Angeles, CA 90095-1606, USA
| | - Edgar E. Vallejo
- Department of Computer Science, Monterrey Institute of Technology, Ave. Eugenio Garza Sada 2501 Sur Col. Tecnológico C.P. 64849, Monterrey, Nuevo León, Mexico
| | - Sara Waller
- Department of Philosophy, Montana State University, 2-155 Wilson Hall, Bozeman, Montana 59717, USA
| | - Veronica Zamora-Gutierrez
- Department of Zoology, University of Cambridge, Downing Street, Cambridge, CB2 3EJ, UK
- Centre for Biodiversity and Environmental Research, University College London, London WC1H 0AG, UK
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Wittenbach JD, Bouchard KE, Brainard MS, Jin DZ. An Adapting Auditory-motor Feedback Loop Can Contribute to Generating Vocal Repetition. PLoS Comput Biol 2015; 11:e1004471. [PMID: 26448054 PMCID: PMC4598084 DOI: 10.1371/journal.pcbi.1004471] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2015] [Accepted: 07/21/2015] [Indexed: 12/27/2022] Open
Abstract
Consecutive repetition of actions is common in behavioral sequences. Although integration of sensory feedback with internal motor programs is important for sequence generation, if and how feedback contributes to repetitive actions is poorly understood. Here we study how auditory feedback contributes to generating repetitive syllable sequences in songbirds. We propose that auditory signals provide positive feedback to ongoing motor commands, but this influence decays as feedback weakens from response adaptation during syllable repetitions. Computational models show that this mechanism explains repeat distributions observed in Bengalese finch song. We experimentally confirmed two predictions of this mechanism in Bengalese finches: removal of auditory feedback by deafening reduces syllable repetitions; and neural responses to auditory playback of repeated syllable sequences gradually adapt in sensory-motor nucleus HVC. Together, our results implicate a positive auditory-feedback loop with adaptation in generating repetitive vocalizations, and suggest sensory adaptation is important for feedback control of motor sequences. Repetitions are common in animal vocalizations. Songs of many songbirds contain syllables that repeat a variable number of times, with non-Markovian distributions of repeat counts. The neural mechanism underlying such syllable repetitions is unknown. In this work, we show that auditory feedback plays an important role in sustaining syllable repetitions in the Bengalese finch. Deafening reduces syllable repetitions and skews the repeat number distribution towards short repeats. These effects are explained with our computational model, which suggests that syllable repeats are initially sustained by auditory feedback to the neural networks that drive the syllable production. The feedback strength weakens as the syllable repeats, increasing the likelihood that the syllable repetition stops. Neural recordings confirm such adaptation of auditory feedback to the auditory-motor circuit in the Bengalese finch. Our results suggests that sensory feedback can directly impact repetitions in motor sequences, and may provide insights into neural mechanisms of speech disorders such as stuttering.
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Affiliation(s)
- Jason D. Wittenbach
- Department of Physics and Center for Neural Engineering, the Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Kristofer E. Bouchard
- Department of Physiology and Center for Integrative Neuroscience, University of California at San Francisco, San Francisco, California, United States of America
- Department of Neurosurgery and Center for Neural Engineering and Prosthesis, University of California at San Francisco, San Francisco, California, United States of America
| | - Michael S. Brainard
- Department of Physiology and Center for Integrative Neuroscience, University of California at San Francisco, San Francisco, California, United States of America
- Howard Hughes Medical Institute, San Francisco, California, United States of America
| | - Dezhe Z. Jin
- Department of Physics and Center for Neural Engineering, the Pennsylvania State University, University Park, Pennsylvania, United States of America
- * E-mail:
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28
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Kershenbaum A, Garland EC. Quantifying similarity in animal vocal sequences: which metric performs best? Methods Ecol Evol 2015. [DOI: 10.1111/2041-210x.12433] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | - Ellen C. Garland
- School of Biology University of St. Andrews St. Andrews Fife KY16 9TH UK
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29
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Bouchard KE, Ganguli S, Brainard MS. Role of the site of synaptic competition and the balance of learning forces for Hebbian encoding of probabilistic Markov sequences. Front Comput Neurosci 2015; 9:92. [PMID: 26257637 PMCID: PMC4508839 DOI: 10.3389/fncom.2015.00092] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2015] [Accepted: 06/30/2015] [Indexed: 12/11/2022] Open
Abstract
The majority of distinct sensory and motor events occur as temporally ordered sequences with rich probabilistic structure. Sequences can be characterized by the probability of transitioning from the current state to upcoming states (forward probability), as well as the probability of having transitioned to the current state from previous states (backward probability). Despite the prevalence of probabilistic sequencing of both sensory and motor events, the Hebbian mechanisms that mold synapses to reflect the statistics of experienced probabilistic sequences are not well understood. Here, we show through analytic calculations and numerical simulations that Hebbian plasticity (correlation, covariance, and STDP) with pre-synaptic competition can develop synaptic weights equal to the conditional forward transition probabilities present in the input sequence. In contrast, post-synaptic competition can develop synaptic weights proportional to the conditional backward probabilities of the same input sequence. We demonstrate that to stably reflect the conditional probability of a neuron's inputs and outputs, local Hebbian plasticity requires balance between competitive learning forces that promote synaptic differentiation and homogenizing learning forces that promote synaptic stabilization. The balance between these forces dictates a prior over the distribution of learned synaptic weights, strongly influencing both the rate at which structure emerges and the entropy of the final distribution of synaptic weights. Together, these results demonstrate a simple correspondence between the biophysical organization of neurons, the site of synaptic competition, and the temporal flow of information encoded in synaptic weights by Hebbian plasticity while highlighting the utility of balancing learning forces to accurately encode probability distributions, and prior expectations over such probability distributions.
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Affiliation(s)
- Kristofer E Bouchard
- Life Sciences and Computational Research Divisions, Lawrence Berkeley National Laboratory Berkeley, CA, USA
| | - Surya Ganguli
- Department of Applied Physics, Stanford University Stanford, CA, USA
| | - Michael S Brainard
- Department of Physiology, University of California, San Francisco and Center for Integrative Neuroscience, University of California, San Francisco San Francisco, CA, USA ; Howard Hughes Medical Institute Chevy Chase, MD, USA
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Menyhart O, Kolodny O, Goldstein MH, DeVoogd TJ, Edelman S. Juvenile zebra finches learn the underlying structural regularities of their fathers' song. Front Psychol 2015; 6:571. [PMID: 26005428 PMCID: PMC4424812 DOI: 10.3389/fpsyg.2015.00571] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2014] [Accepted: 04/20/2015] [Indexed: 11/13/2022] Open
Abstract
Natural behaviors, such as foraging, tool use, social interaction, birdsong, and language, exhibit branching sequential structure. Such structure should be learnable if it can be inferred from the statistics of early experience. We report that juvenile zebra finches learn such sequential structure in song. Song learning in finches has been extensively studied, and it is generally believed that young males acquire song by imitating tutors (Zann, 1996). Variability in the order of elements in an individual’s mature song occurs, but the degree to which variation in a zebra finch’s song follows statistical regularities has not been quantified, as it has typically been dismissed as production error (Sturdy et al., 1999). Allowing for the possibility that such variation in song is non-random and learnable, we applied a novel analytical approach, based on graph-structured finite-state grammars, to each individual’s full corpus of renditions of songs. This method does not assume syllable-level correspondence between individuals. We find that song variation can be described by probabilistic finite-state graph grammars that are individually distinct, and that the graphs of juveniles are more similar to those of their fathers than to those of other adult males. This grammatical learning is a new parallel between birdsong and language. Our method can be applied across species and contexts to analyze complex variable learned behaviors, as distinct as foraging, tool use, and language.
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Affiliation(s)
- Otília Menyhart
- Department of Psychology, Cornell University, Ithaca, NY USA ; MTA TTK Lendület Cancer Biomarker Research Group, Budapest Hungary
| | - Oren Kolodny
- Department of Zoology, Tel Aviv University, Tel Aviv Israel ; Department of Biology, Stanford University, Stanford, CA USA
| | | | | | - Shimon Edelman
- Department of Psychology, Cornell University, Ithaca, NY USA
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31
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Matheson LE, Sakata JT. Catecholaminergic contributions to vocal communication signals. Eur J Neurosci 2015; 41:1180-94. [DOI: 10.1111/ejn.12885] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2014] [Revised: 02/25/2015] [Accepted: 03/01/2015] [Indexed: 11/26/2022]
Affiliation(s)
- Laura E. Matheson
- Department of Biology; McGill University; Montreal QC H3A 1B1 Canada
| | - Jon T. Sakata
- Department of Biology; McGill University; Montreal QC H3A 1B1 Canada
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Gomez-Marin A, Paton JJ, Kampff AR, Costa RM, Mainen ZF. Big behavioral data: psychology, ethology and the foundations of neuroscience. Nat Neurosci 2014; 17:1455-62. [DOI: 10.1038/nn.3812] [Citation(s) in RCA: 183] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2014] [Accepted: 07/31/2014] [Indexed: 12/11/2022]
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Kershenbaum A, Bowles AE, Freeberg TM, Jin DZ, Lameira AR, Bohn K. Animal vocal sequences: not the Markov chains we thought they were. Proc Biol Sci 2014; 281:20141370. [PMID: 25143037 PMCID: PMC4150325 DOI: 10.1098/rspb.2014.1370] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2014] [Accepted: 07/24/2014] [Indexed: 11/12/2022] Open
Abstract
Many animals produce vocal sequences that appear complex. Most researchers assume that these sequences are well characterized as Markov chains (i.e. that the probability of a particular vocal element can be calculated from the history of only a finite number of preceding elements). However, this assumption has never been explicitly tested. Furthermore, it is unclear how language could evolve in a single step from a Markovian origin, as is frequently assumed, as no intermediate forms have been found between animal communication and human language. Here, we assess whether animal taxa produce vocal sequences that are better described by Markov chains, or by non-Markovian dynamics such as the 'renewal process' (RP), characterized by a strong tendency to repeat elements. We examined vocal sequences of seven taxa: Bengalese finches Lonchura striata domestica, Carolina chickadees Poecile carolinensis, free-tailed bats Tadarida brasiliensis, rock hyraxes Procavia capensis, pilot whales Globicephala macrorhynchus, killer whales Orcinus orca and orangutans Pongo spp. The vocal systems of most of these species are more consistent with a non-Markovian RP than with the Markovian models traditionally assumed. Our data suggest that non-Markovian vocal sequences may be more common than Markov sequences, which must be taken into account when evaluating alternative hypotheses for the evolution of signalling complexity, and perhaps human language origins.
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Affiliation(s)
- Arik Kershenbaum
- National Institute for Mathematical and Biological Synthesis, Knoxville, TN, USA
| | - Ann E Bowles
- Hubbs SeaWorld Research Institute, San Diego, CA 92109, USA
| | - Todd M Freeberg
- Department of Psychology, University of Tennessee, Knoxville, TN, USA
| | - Dezhe Z Jin
- Department of Physics and the Center for Neural Engineering, Penn State University, University Park, PA, USA
| | - Adriano R Lameira
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Sciencepark 904, 1098 XH, Amsterdam, The Netherlands Pongo Foundation, Papenhoeflaan 91, 3421 XN, Oudewater, The Netherlands
| | - Kirsten Bohn
- Department of Biological Sciences, Florida International University, Miami, FL, USA
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ten Cate C. On the phonetic and syntactic processing abilities of birds: from songs to speech and artificial grammars. Curr Opin Neurobiol 2014; 28:157-64. [PMID: 25078891 DOI: 10.1016/j.conb.2014.07.019] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2014] [Revised: 07/10/2014] [Accepted: 07/11/2014] [Indexed: 11/25/2022]
Abstract
Like speech and language, the songs of many songbirds consist of learned, rapidly produced, structured sequences of distinct vocal units, originating from an interplay between experience and learning biases. Songs are species specific, but also show considerable within species variation in elements or element sequencing. This variation implies that birds possess mechanisms to identify, categorize and combine sounds. I review the abilities for speech sound perception and categorization, as well as for grammatical rule learning by birds. Speech sound perception in birds is in many ways comparable to human speech perception. Birds can also detect and generalize patterns underlying artificially arranged strings of vocal elements. However, there is a need for more comparative studies to examine the limits of their rule learning abilities and how they relate to those of humans.
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Affiliation(s)
- Carel ten Cate
- Leiden Institute of Biology and Leiden Institute for Brain and Cognition, Leiden University, PO Box 9505, 2300 RA Leiden, The Netherlands.
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35
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Coen P, Clemens J, Weinstein AJ, Pacheco DA, Deng Y, Murthy M. Dynamic sensory cues shape song structure in Drosophila. Nature 2014. [PMID: 24598544 DOI: 10.1038/nature13131.] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The generation of acoustic communication signals is widespread across the animal kingdom, and males of many species, including Drosophilidae, produce patterned courtship songs to increase their chance of success with a female. For some animals, song structure can vary considerably from one rendition to the next; neural noise within pattern generating circuits is widely assumed to be the primary source of such variability, and statistical models that incorporate neural noise are successful at reproducing the full variation present in natural songs. In direct contrast, here we demonstrate that much of the pattern variability in Drosophila courtship song can be explained by taking into account the dynamic sensory experience of the male. In particular, using a quantitative behavioural assay combined with computational modelling, we find that males use fast modulations in visual and self-motion signals to pattern their songs, a relationship that we show is evolutionarily conserved. Using neural circuit manipulations, we also identify the pathways involved in song patterning choices and show that females are sensitive to song features. Our data not only demonstrate that Drosophila song production is not a fixed action pattern, but establish Drosophila as a valuable new model for studies of rapid decision-making under both social and naturalistic conditions.
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Affiliation(s)
- Philip Coen
- 1] Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, USA [2] Department of Molecular Biology, Princeton University, Princeton, New Jersey 08544, USA
| | - Jan Clemens
- 1] Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, USA [2] Department of Molecular Biology, Princeton University, Princeton, New Jersey 08544, USA
| | - Andrew J Weinstein
- 1] Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, USA [2] Department of Molecular Biology, Princeton University, Princeton, New Jersey 08544, USA
| | - Diego A Pacheco
- 1] Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, USA [2] Department of Molecular Biology, Princeton University, Princeton, New Jersey 08544, USA
| | - Yi Deng
- 1] Department of Physics, Princeton University, Princeton, New Jersey 08544, USA [2] Department of Biophysics, University of Washington School of Medicine, Seattle, Washington 98195, USA
| | - Mala Murthy
- 1] Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, USA [2] Department of Molecular Biology, Princeton University, Princeton, New Jersey 08544, USA
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36
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Dynamic sensory cues shape song structure in Drosophila. Nature 2014; 507:233-7. [PMID: 24598544 DOI: 10.1038/nature13131] [Citation(s) in RCA: 101] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2013] [Accepted: 02/05/2014] [Indexed: 11/09/2022]
Abstract
The generation of acoustic communication signals is widespread across the animal kingdom, and males of many species, including Drosophilidae, produce patterned courtship songs to increase their chance of success with a female. For some animals, song structure can vary considerably from one rendition to the next; neural noise within pattern generating circuits is widely assumed to be the primary source of such variability, and statistical models that incorporate neural noise are successful at reproducing the full variation present in natural songs. In direct contrast, here we demonstrate that much of the pattern variability in Drosophila courtship song can be explained by taking into account the dynamic sensory experience of the male. In particular, using a quantitative behavioural assay combined with computational modelling, we find that males use fast modulations in visual and self-motion signals to pattern their songs, a relationship that we show is evolutionarily conserved. Using neural circuit manipulations, we also identify the pathways involved in song patterning choices and show that females are sensitive to song features. Our data not only demonstrate that Drosophila song production is not a fixed action pattern, but establish Drosophila as a valuable new model for studies of rapid decision-making under both social and naturalistic conditions.
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37
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Miller A, Jin DZ. Potentiation decay of synapses and length distributions of synfire chains self-organized in recurrent neural networks. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2013; 88:062716. [PMID: 24483495 DOI: 10.1103/physreve.88.062716] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2013] [Indexed: 06/03/2023]
Abstract
Synfire chains are thought to underlie precisely timed sequences of spikes observed in various brain regions and across species. How they are formed is not understood. Here we analyze self-organization of synfire chains through the spike-timing dependent plasticity (STDP) of the synapses, axon remodeling, and potentiation decay of synaptic weights in networks of neurons driven by noisy external inputs and subject to dominant feedback inhibition. Potentiation decay is the gradual, activity-independent reduction of synaptic weights over time. We show that potentiation decay enables a dynamic and statistically stable network connectivity when neurons spike spontaneously. Periodic stimulation of a subset of neurons leads to formation of synfire chains through a random recruitment process, which terminates when the chain connects to itself and forms a loop. We demonstrate that chain length distributions depend on the potentiation decay. Fast potentiation decay leads to long chains with wide distributions, while slow potentiation decay leads to short chains with narrow distributions. We suggest that the potentiation decay, which corresponds to the decay of early long-term potentiation of synapses, is an important synaptic plasticity rule in regulating formation of neural circuity through STDP.
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Affiliation(s)
- Aaron Miller
- Department of Physics, Bridgewater College, Bridgewater, Virginia 22812, USA
| | - Dezhe Z Jin
- Department of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
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38
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Katahira K, Suzuki K, Kagawa H, Okanoya K. A simple explanation for the evolution of complex song syntax in Bengalese finches. Biol Lett 2013; 9:20130842. [PMID: 24284561 DOI: 10.1098/rsbl.2013.0842] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The songs of Bengalese finches (Lonchura striata var. domestica) have complex syntax and provide an opportunity to investigate how complex sequential behaviour emerges via the evolutionary process. In this study, we suggest that a simple mechanism, i.e. many-to-one mapping from internal states onto syllables, may underlie the emergence of apparent complex syllable sequences that have higher order history dependencies. We analysed the songs of Bengalese finches and of their wild ancestor, the white-rumped munia (L. striata), whose songs are more stereotypical and simpler compared with those of Bengalese finches. The many-to-one mapping mechanism sufficiently accounted for the differences in the complexity of song syllable sequences of these two strains.
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Affiliation(s)
- Kentaro Katahira
- Center for Evolutionary Cognitive Sciences, The University of Tokyo, , 3-8-1 Komaba, Meguro-ku, Meguro, Tokyo 153-8902, Japan
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40
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Collective phenomena and non-finite state computation in a human social system. PLoS One 2013; 8:e75818. [PMID: 24130745 PMCID: PMC3794014 DOI: 10.1371/journal.pone.0075818] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2013] [Accepted: 08/21/2013] [Indexed: 11/29/2022] Open
Abstract
We investigate the computational structure of a paradigmatic example of distributed social interaction: that of the open-source Wikipedia community. We examine the statistical properties of its cooperative behavior, and perform model selection to determine whether this aspect of the system can be described by a finite-state process, or whether reference to an effectively unbounded resource allows for a more parsimonious description. We find strong evidence, in a majority of the most-edited pages, in favor of a collective-state model, where the probability of a “revert” action declines as the square root of the number of non-revert actions seen since the last revert. We provide evidence that the emergence of this social counter is driven by collective interaction effects, rather than properties of individual users.
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41
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Markowitz JE, Ivie E, Kligler L, Gardner TJ. Long-range order in canary song. PLoS Comput Biol 2013; 9:e1003052. [PMID: 23658509 PMCID: PMC3642045 DOI: 10.1371/journal.pcbi.1003052] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2012] [Accepted: 03/22/2013] [Indexed: 11/18/2022] Open
Abstract
Bird songs range in form from the simple notes of a Chipping Sparrow to the rich performance of the nightingale. Non-adjacent correlations can be found in the syntax of some birdsongs, indicating that the choice of what to sing next is determined not only by the current syllable, but also by previous syllables sung. Here we examine the song of the domesticated canary, a complex singer whose song consists of syllables, grouped into phrases that are arranged in flexible sequences. Phrases are defined by a fundamental time-scale that is independent of the underlying syllable duration. We show that the ordering of phrases is governed by long-range rules: the choice of what phrase to sing next in a given context depends on the history of the song, and for some syllables, highly specific rules produce correlations in song over timescales of up to ten seconds. The neural basis of these long-range correlations may provide insight into how complex behaviors are assembled from more elementary, stereotyped modules. Bird songs range in form from the simple notes of a Chipping Sparrow to the complex repertoire of the nightingale. Recent studies suggest that bird songs may contain non-adjacent dependencies where the choice of what to sing next depends on the history of what has already been produced. However, the complexity of these rules has not been examined statistically for the most elaborate avian singers. Here we show that one complex singer—the domesticated canary—produces a song that is strongly influenced by long-range rules. The choice of how long to repeat a given note or which note to choose next depends on the history of the song, and these dependencies span intervals of time much longer than previously assumed for birdsong. Like most forms of human music, the songs of canaries contain patterns expressed over long timescales, governed by rules that apply to multiple levels of a temporal hierarchy. This vocal complexity provides a valuable model to examine how ordered behaviors are assembled from more elementary neural components in a relatively simple neural circuit.
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Affiliation(s)
- Jeffrey E. Markowitz
- Department of Cognitive and Neural Systems, Boston University, Boston, Massachusetts, United States of America
- Center of Excellence for Learning in Education, Science and Technology, Boston, Massachusetts, United States of America
| | - Elizabeth Ivie
- Department of Biology, Boston University, Boston, Massachusetts, United States of America
| | - Laura Kligler
- Department of Biology, Boston University, Boston, Massachusetts, United States of America
| | - Timothy J. Gardner
- Center of Excellence for Learning in Education, Science and Technology, Boston, Massachusetts, United States of America
- Department of Biology, Boston University, Boston, Massachusetts, United States of America
- * E-mail:
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42
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Abstract
Variation in sequencing of actions occurs in many natural behaviors, yet how such variation is maintained is poorly understood. We investigated maintenance of sequence variation in adult Bengalese finch song, a learned skill with rendition-to-rendition variation in the sequencing of discrete syllables (i.e., syllable "b" might transition to "c" with 70% probability and to "d" with 30% probability). We found that probabilities of transitions ordinarily remain stable but could be modified by delivering aversive noise bursts following one transition (e.g., "b→c") but not the alternative (e.g., "b→d"). Such differential reinforcement induced gradual, adaptive decreases in probabilities of targeted transitions and compensatory increases in alternative transitions. Thus, the normal stability of transition probabilities does not reflect hardwired premotor circuitry. While all variable transitions could be modified by differential reinforcement, some were less readily modified than others; these were cases that exhibited more alternation between possible transitions than predicted by chance (i.e., "b→d " would tend to follow "b→c " and vice versa). These history-dependent transitions were less modifiable than more stochastic transitions. Similarly, highly stereotyped transitions (which are completely predictable) were not modifiable. This suggests that stochastically generated variability is crucial for sequence modification. Finally, we found that, when reinforcement ceased, birds gradually restored transition probabilities to their baseline values. Hence, the nervous system retains a representation of baseline probabilities and has the impetus to restore them. Together, our results indicate that variable sequencing in a motor skill can reflect an end point of learning that is stably maintained via continual self-monitoring.
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43
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Glaze CM, Troyer TW. Development of temporal structure in zebra finch song. J Neurophysiol 2012; 109:1025-35. [PMID: 23175805 DOI: 10.1152/jn.00578.2012] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Zebra finch song has provided an excellent case study in the neural basis of sequence learning, with a high degree of temporal precision and tight links with precisely timed bursting in forebrain neurons. To examine the development of song timing, we measured the following four aspects of song temporal structure at four age ranges between 65 and 375 days posthatch: the mean durations of song syllables and the silent gaps between them, timing variability linked to song tempo, timing variability expressed independently across syllables and gaps, and transition probabilities between consecutive syllable pairs. We found substantial increases in song tempo between 65 and 85 days posthatch, due almost entirely to a shortening of gaps. We also found a decrease in tempo variability, also specific to gaps. Both the magnitude of the increase in tempo and the decrease in tempo variability were correlated on gap-by-gap basis with increases in the reliability of corresponding syllable transitions. Syllables had no systematic increase in tempo or decrease in tempo variability. In contrast to tempo parameters, both syllables and gaps showed an early sharp reduction in independent variability followed by continued reductions over the first year. The data suggest that links between syllable-based representations are strengthened during the later parts of the traditional period of song learning and that song rhythm continues to become more regular throughout the first year of life. Similar learning patterns have been identified in human sequence learning, suggesting a potentially rich area of comparative research.
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Affiliation(s)
- Christopher M Glaze
- Program in Neuroscience and Cognitive Science, Department of Psychology, University of Maryland, College Park, Maryland, USA.
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ten Cate C, Okanoya K. Revisiting the syntactic abilities of non-human animals: natural vocalizations and artificial grammar learning. Philos Trans R Soc Lond B Biol Sci 2012; 367:1984-94. [PMID: 22688634 DOI: 10.1098/rstb.2012.0055] [Citation(s) in RCA: 102] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The domain of syntax is seen as the core of the language faculty and as the most critical difference between animal vocalizations and language. We review evidence from spontaneously produced vocalizations as well as from perceptual experiments using artificial grammars to analyse animal syntactic abilities, i.e. abilities to produce and perceive patterns following abstract rules. Animal vocalizations consist of vocal units (elements) that are combined in a species-specific way to create higher order strings that in turn can be produced in different patterns. While these patterns differ between species, they have in common that they are no more complex than a probabilistic finite-state grammar. Experiments on the perception of artificial grammars confirm that animals can generalize and categorize vocal strings based on phonetic features. They also demonstrate that animals can learn about the co-occurrence of elements or learn simple 'rules' like attending to reduplications of units. However, these experiments do not provide strong evidence for an ability to detect abstract rules or rules beyond finite-state grammars. Nevertheless, considering the rather limited number of experiments and the difficulty to design experiments that unequivocally demonstrate more complex rule learning, the question of what animals are able to do remains open.
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Affiliation(s)
- Carel ten Cate
- Behavioural Biology, Institute of Biology and Leiden Institute for Brain and Cognition, Leiden University, PO Box 9505, 2300 RA Leiden, The Netherlands.
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45
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Sasahara K, Cody ML, Cohen D, Taylor CE. Structural design principles of complex bird songs: a network-based approach. PLoS One 2012; 7:e44436. [PMID: 23028539 PMCID: PMC3454418 DOI: 10.1371/journal.pone.0044436] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2012] [Accepted: 08/02/2012] [Indexed: 11/28/2022] Open
Abstract
Bird songs are acoustic communication signals primarily used in male-male aggression and in male-female attraction. These are often monotonous patterns composed of a few phrases, yet some birds have extremely complex songs with a large phrase repertoire, organized in non-random fashion with discernible patterns. Since structure is typically associated with function, the structures of complex bird songs provide important clues to the evolution of animal communication systems. Here we propose an efficient network-based approach to explore structural design principles of complex bird songs, in which the song networks–transition relationships among different phrases and the related structural measures–are employed. We demonstrate how this approach works with an example using California Thrasher songs, which are sequences of highly varied phrases delivered in succession over several minutes. These songs display two distinct features: a large phrase repertoire with a ‘small-world’ architecture, in which subsets of phrases are highly grouped and linked with a short average path length; and a balanced transition diversity amongst phrases, in which deterministic and non-deterministic transition patterns are moderately mixed. We explore the robustness of this approach with variations in sample size and the amount of noise. Our approach enables a more quantitative study of global and local structural properties of complex bird songs than has been possible to date.
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Affiliation(s)
- Kazutoshi Sasahara
- Graduate School of Information Science, Nagoya University, Nagoya, Aichi, Japan.
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46
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Katahira K, Suzuki K, Okanoya K, Okada M. Complex sequencing rules of birdsong can be explained by simple hidden Markov processes. PLoS One 2011; 6:e24516. [PMID: 21915345 PMCID: PMC3168521 DOI: 10.1371/journal.pone.0024516] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2011] [Accepted: 08/12/2011] [Indexed: 01/08/2023] Open
Abstract
Complex sequencing rules observed in birdsongs provide an opportunity to investigate the neural mechanism for generating complex sequential behaviors. To relate the findings from studying birdsongs to other sequential behaviors such as human speech and musical performance, it is crucial to characterize the statistical properties of the sequencing rules in birdsongs. However, the properties of the sequencing rules in birdsongs have not yet been fully addressed. In this study, we investigate the statistical properties of the complex birdsong of the Bengalese finch (Lonchura striata var. domestica). Based on manual-annotated syllable labeles, we first show that there are significant higher-order context dependencies in Bengalese finch songs, that is, which syllable appears next depends on more than one previous syllable. We then analyze acoustic features of the song and show that higher-order context dependencies can be explained using first-order hidden state transition dynamics with redundant hidden states. This model corresponds to hidden Markov models (HMMs), well known statistical models with a large range of application for time series modeling. The song annotation with these models with first-order hidden state dynamics agreed well with manual annotation, the score was comparable to that of a second-order HMM, and surpassed the zeroth-order model (the Gaussian mixture model; GMM), which does not use context information. Our results imply that the hierarchical representation with hidden state dynamics may underlie the neural implementation for generating complex behavioral sequences with higher-order dependencies.
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Affiliation(s)
- Kentaro Katahira
- ERATO, Okanoya Emotional Information Project, Japan Science Technology Agency, Wako, Saitama, Japan
- Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
- RIKEN Brain Science Institute, Wako, Saitama, Japan
| | - Kenta Suzuki
- ERATO, Okanoya Emotional Information Project, Japan Science Technology Agency, Wako, Saitama, Japan
- RIKEN Brain Science Institute, Wako, Saitama, Japan
- Graduate School of Science and Engineering, Saitama University, Saitama, Japan
| | - Kazuo Okanoya
- ERATO, Okanoya Emotional Information Project, Japan Science Technology Agency, Wako, Saitama, Japan
- RIKEN Brain Science Institute, Wako, Saitama, Japan
- Graduate School of Arts and Sciences, The University of Tokyo, Meguro, Tokyo, Japan
| | - Masato Okada
- ERATO, Okanoya Emotional Information Project, Japan Science Technology Agency, Wako, Saitama, Japan
- Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
- RIKEN Brain Science Institute, Wako, Saitama, Japan
- * E-mail:
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