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Construction of spatial features in echolocating bottlenose dolphins (Tursiops truncatus)
Get PDFDolphins use an advanced biosonar system to accurately navigate and forage in their environment. They have the unique capability to rapidly detect and classify underwater targets in a complex acoustic environment and do so more precisely than man-made underwater sonars. Dolphins use the fine-scale temporal and spectral features of the echo-waveforms to determine different attributes of the target and rely on echo-delay, the time between their emitted sound pulse “click” and the return of the reflected echo to determine the range to a target. In this dissertation, phantom echoes are used to determine the effect that changes in mean echo-delay, i.e. range, have on echo-delay discrimination abilities and which fine-scale acoustic features dolphins use to create a coherent mental representation of a target. In Chapter 1, the dolphin’s ability to detect changes in echo-delay as a function of range was determined. Chapter 2 then tested the dolphin’s ability to discriminate between the ranges of two targets at different mean ranges. In this experiment targets were presented successively, meaning, the dolphin had to hold the range of one target in memory while they compared it to the second target. Chapters 1 and 2 conclude that as mean range increases greater than 10 m the dolphin’s ability to discriminate between echo-delays degrades rapidly.
Chapter 3 explores the possibility of the dolphin using spectral cues to determine the difference in range between two targets that are presented simultaneously. First, the dolphin’s peripheral auditory system was modeled to determine available spectral cues. The dolphin’s ability to discriminate the difference in range between two targets was then tested and compared under successive and simultaneous conditions. Although spectral cues were available to the dolphin, the results from the simultaneous condition suggest limited improvement when compared to the successive condition. Lastly, Chapter 4 focuses on the auditory perceived cues the dolphin may use to convert fine-scale spectral and temporal information into a coherent mental representation. By manipulating echo-phase information the results suggest that for fine-scale echo-delays the dolphins may use a pitch cue to discriminate between complex targets
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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Neuronal representations underlying probabilistic sequence discrimination
Get PDFHow language and music are processed by the biological connection in our heads is one of the most significant challenges to neuroscience. A critical aspect of this challenge is that the signals are necessarily sequential and the brain must process events at multiple hierarchical levels simultaneously. Aspects of this are not unique to humans, however, but shared with many animals who rely on vocal communication. In this work, we focus on characterizing the capacity of European starlings to discriminate between sequences and understanding how individual neurons and populations of neurons support sequence discrimination. We train starlings on novel behavioral protocols wherein subjects must discriminate between probabilistically generated sequences composed of vocal elements using only the sequential relationships between elements. When faced with uncertainty, subjects discriminate sequences in a way which is consistent with weighing the evidence afforded by the sequence. By recording the spiking activity of neurons in anesthetized subjects, we found that neurons in the caudomedial nidopallium (NCM) are sensitive to sequences and that their capacity to encode sequences is higher than their capacity to encode elements. Neuronal responses to sequences are shaped by a combination of reward-association and behavioral demands. Recordings of population activity in awake behaving subjects revealed that neurons in caudolateral mesopallium (CLM) exhibit mixed selectivity for elements and position, supporting robust population representations of element identity. Element encoding fidelity in CLM is state-dependent—accurate decoding of element identity from either fast-spiking or regular-spiking populations improves the reliability and encoding fidelity of the complementary population. These results emphasize the importance of behavioral goals in understanding how sequences are processed by neuronal populations
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Predictive Coding in the Auditory System
No full textFrom the noisy information bombarding our senses, our brains must construct percepts that are veridical – reflecting the true state of the world – and informative – conveying what we did not already know. Influential theories suggest that both challenges are met through mechanisms that use expectations about the likely state of the world to shape perception. Predictive coding, a theoretical framework in which the brain compares a generative model to incoming sensory signals, seeks to explain this inferential process of perception. Implementation of predictive coding at a mechanistic level in individual neurons is however not well understood. This dissertation builds on the field of computational modeling of sensory systems to further our understanding of perceptual inference in individual neurons in the auditory system of songbirds (European starling). Chapter 1 summarizes some possible ways in which predictive coding could be realized at the algorithmic level. The main contribution of this chapter is not to present models that can uniquely account for sensory data, but to reconcile different cognitive behaviors under the umbrella of predictive coding which were previously considered incompatible. Chapter 2 combines computational modeling with electrophysiological activity to study internal representations of sensory information under predictive coding framework in single auditory neurons in songbirds. This study examines unique components of the response variance guided by predictive coding. It is shown that during song listening, individual neuron responses in primary and secondary auditory regions are modeled by expectations of future song and uncertainty of song. Generative machine learning approaches enable us to make hypotheses about internal generative models for complex natural stimuli. Chapter 3 explores the flexibility of the internal model and whether prediction error is correlated with behavior. This study draws primarily upon the complex structure of birdsong, developing methods to analyze the acoustic and temporal structure in vocal signals and then behaviorally and physiologically probing the underpinnings of the predictive structure of birdsong. The findings in this chapter indicate that prediction error encoding is modulated by task-relevant generative models and behavior. Taken together, my dissertation work offers a unified explanation of inferential frameworks of perception in contexts of systems processing behaviorally relevant signals, showcasing its adaptation to these systems
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Through the looking glass: population dynamics through membrane potentials
Get PDFRelating the collective activities of neural populations to external sensory stimuli or to motor output is essential to understanding how nervous systems support behavior. Equally important is examining these processes in the context of ethological signals, which are typically high-dimensional with a wide range of co-varying features at multiple timescales. The synaptic and network mechanisms for encoding these complex signals are largely unknown, due in part to limitations inherent in experimental design practices leveraging dimensionality reduction rather than embracing the full suite of stimulus complexity. Additional limitations arise because most studies to date examine spiking statistics in randomly-selected populations without consideration for the functional relevance of sub-network groupings. We do not know how spiking responses of individual neurons are pooled, functionally, by downstream neurons – a mechanism that could significantly alter population coding. Stimulus and behavior likely modulate the functional selection of sub-populations, which then likely exhibit different spiking statistics than the population at large. Using a combination of intracellular and extracellular electrophysiology techniques in the caudal mesopallium (CM) and the caudal nidopallium (NCM) of the common European Starling, I examine synaptic and spiking activity driven by previously-recorded conspecific vocalizations, maintaining the full ethologically-relevant complexity contained in each signal. Uniquely, I feature the synaptic response as an independent variable in the examination of population spiking activity. The main implication of the collective results presented in this study is that, rather than a model of hierarchical processing in which stimulus-specific information is restricted to parallel circuits within each region, sensory integration and processing are supported by a system in which information about even the most complex stimuli is likely massively redundant and shared among the population at large. This scaffolds a sensory processing model in which flexible network re-organization - on short timescales and in a stimulus-specific way - support the complexities of spiking output observed in sensory cortex in response to learning, adaptation, attention, and context to meet the demands of an ever-changing environment
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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The Topology of Neural Population Activity in the Songbird Auditory System
Get PDFPerception depends upon the coordinated activity of populations of neurons. Howneural populations represent structure in the outside world through their activity is an
important open question. One approach to this problem is the concept of the receptive field,
which quantifies how external stimulation modulates the activity of individual neurons. While
receptive fields are a powerful concept for the experimenter, the brain itself does not have
access to its own receptive fields. This dissertation applies methods from the field of
algebraic topology to characterize the structure of neural population activity in the secondary
auditory region NCM of the European starling. Chapter 2 demonstrates that the simplicial
complex associated to population activity in NCM carries behaviorally-relevant information
xiiabout learned categories. Along the way, a new similarity measure for population activity is
developed, called the Simplicial Laplacian Spectral Entropy. It is shown that this measure
quantifies the similarity of simplicial complexes associated with neural activity in a way that
depends upon their global topological structure. Chapter 3 explores the connection between
neural topology and classical receptive fields, by showing that the intrinsic geometry of the
population activity matches the geometry of the receptive fields. This shows that the temporal
coativity structure of the population contains a direct representation of the stimulus structure
without requiring explicit computation of receptive fields. This validates a previously
described theoretical mechanism in a sensory system for the first time in-vivo, and provides a
new understanding for population activity in sensory regions. This chapter also introduces a
technique for reconstructing acoustic spectrograms of complex, natural vocal signals from
neural activity, which will be a powerful technique for exploring population level
representations in future studies. The final chapter of this dissertation describes a new
mathematical description of the phenomenon of polychronization in spiking neural networks.
This serves as a bridge between the experimental results of this dissertation and a theoretical
understanding of the emergence of spatiotemporal structure in the activity of neural
populations
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The Unreasonable Effectiveness of Machine Learning in Neuroscience: Understanding High-dimensional Neural Representations with Realistic Synthetic Stimuli
Get PDFParametrizing complex natural stimuli is a difficult and long-standing challenge. We used a generative deep convergent network to represent and parametrize a large corpus of song from European starlings, a songbird species, into a compressed low-dimensional space. We applied psychophysical methods to probe categorical perception of natural starling song syllables, which reveal a shared categorical perceptual space. Some categorical boundaries are sensitive to the category assignment of training syllables, indicating that the consensus is context dependent and that underlying dimensions of the space are not independent. We record simultaneous firing from populations of 10's of neurons in a secondary auditory cortical region of anesthetized starlings. By estimating how fast population level neural representation change with respect to the stimuli, we produce a measure along a path in stimuli space that is shared between birds and descriptive of the psychophysically determined parameters in other birds. Consistent with this, we predict the behavioral psychometric function along one dimension by fitting the behavior for other dimensions to the population level neural activity. Thus, knowing how the animal responds in one sub-region of the parametrized space informs responses in other sub-regions. Our results implicate the importance of experience in shaping shared perceptual boundaries among complex communication signals and suggest the categorical representation of natural signals in secondary sensory cortices is distributed much more densely than predicted by traditional hierarchical object recognition models. This thesis also explores other applications of machine learning to solve neuroscience problems, in particular, the curse of dimensionality and exploring predictive coding and surprise. A model explicitly designed to predict future states allows the compression of high-dimensional time-varying signals into a lower-dimensional representation encoding exclusively predictive and predictable information and has many practical applications
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