Browsing by Subject "Neuroscience"

Classical models of cerebellar learning posit that climbing fibers operate according to a supervised learning rule to instruct changes in motor output by signaling the occurrence of movement errors. This model is grounded largely in studies of behaviors that utilize hardwired neural pathways to link sensory input to motor output. Yet, cerebellar output is also associated with non-motor behaviors, and recently with modulating reward association pathways in the VTA. Here, I test whether the supervised learning model applies to more flexible learning regimes and how the cerebellum processes reward related signals. I have used both classical and operant condition paradigms in combination with calcium imaging. In the operant conditioning paradigm I find that climbing fibers are preferentially driven by and more time-locked to correctly executed movements and other task parameters that predict reward outcome in a manner consistent with an unsigned reinforcement learning rule. In the classical conditioning paradigm I find distinct climbing fiber responses in three lateral cerebellar regions that can each signal reward prediction, but not reward prediction errors per se. These instructional signals are well suited to guide cerebellar learning based on reward expectation and enable a cerebellar contribution to reward driven behaviors.

“Investigating Contemplative Christian Spirituality as Christian Formation through a Process Hermeneutic: an analysis of History, Evolution, and Neuroscience in Christian Meditation” argues that a contemplative approach to contemporary Christianity may serve to deepen the formation and discipleship of Christians in a manner that endeavors to shape the worldview and the epistemological lens through which followers of Jesus experience life in this world. This thesis offers a social and theological critique that addresses a failure in Christian formation by considering obstacles to intimacy with God created by common ecclesial pedagogical approaches that neglect the experiential and the intellectual dimensions of the faith journey due to outmoded cosmological models and a lack of dialogue with neuroscientific research on the human brain. Considering theologians in the early, medieval, and modern church who have cultivated approaches to experiential understandings of faith through meditation, this thesis argues that contemplative practice in dialogue with a theology of process may provide a necessary vocabulary for the future vitality of Christian discipleship. Rooting a theological methodology in the ‘evolutionary’ perspective proposed by Pierre Teilhard de Chardin, in concert with findings at the intersection of religion and neuroscience, this thesis finds that convincing biological and theological warrants exist for incorporating meditation into paradigms for Christian formation.

“Investigating Contemplative Christian Spirituality as Christian Formation through a Process Hermeneutic: an analysis of History, Evolution, and Neuroscience in Christian Meditation” argues that a contemplative approach to contemporary Christianity may serve to deepen the formation and discipleship of Christians in a manner that endeavors to shape the worldview and the epistemological lens through which followers of Jesus experience life in this world. This thesis offers a social and theological critique that addresses a failure in Christian formation by considering obstacles to intimacy with God created by common ecclesial pedagogical approaches that neglect the experiential and the intellectual dimensions of the faith journey due to outmoded cosmological models and a lack of dialogue with neuroscientific research on the human brain. Considering theologians in the early, medieval, and modern church who have cultivated approaches to experiential understandings of faith through meditation, this thesis argues that contemplative practice in dialogue with a theology of process may provide a necessary vocabulary for the future vitality of Christian discipleship. Rooting a theological methodology in the ‘evolutionary’ perspective proposed by Pierre Teilhard de Chardin, in concert with findings at the intersection of religion and neuroscience, this thesis finds that convincing biological and theological warrants exist for incorporating meditation into paradigms for Christian formation.

In this thesis, I take up two important issues for understanding neurobiological systems: normative functions and information. After introducing the topic and my methodology in chapters 1 and 2, chapter 3 contains an extended critique of the most prominent theory of biological functions, the selected-effects theory of functions. My arguments center on the influential recent selected-effects theory arguing that it has trouble accounting for certain cases and does not seem to capture the sense of malfunction employed in the neurosciences. Chapter 4 defends an alternative theory of normative biological functions that I label the statistical fitness theory. Roughly, this theory holds that tokens of a trait type have the normative function to do something y if it is typical for tokens of that type of trait to y and their doing y contributes to the inclusive fitness of the organism that possesses the trait. In turn, this theory defines malfunctioning trait tokens as those whose effects that typically make positive contributions to fitness fall below the "normal" range in the population. Chapter 5 argues that several other recently popular theories of normative functions have significant flaws.

Chapter 6 takes up the issue of a certain kind of information, namely natural, propositional information. I provide a general framework that explains when signals carry this kind of information about their signifieds based upon stable, perfect correlations holding between the two. Hence, I label this the "stable correlation theory". I also argue that there are good reasons to think that neurons in our brains carry natural, propositional information and that their ability to do so is also grounded in stable correlations.

The brain encodes diverse cognitive functions through the coordinated activity of interacting neural circuits. Neural ensembles are groups of coactive neurons in these circuits that respond to similar stimuli. Neural ensembles are found throughout the brain and have been associated with many cognitive processes including memory, motor control, and perception. However, a key goal of systems neuroscience is to establish a functional link between neural activity and behavior and these previous studies established only a correlation between ensembles and behavior. Demonstrating a functional link between ensembles and behavior requires precise manipulation of ensemble activity. Manipulating ensemble activity allows neuroscientists to determine the patterns of neural activity that are necessary and sufficient to drive behavior. Additionally, recording and analyzing the activity of hundreds to thousands of neurons simultaneously allows neuroscientists to elucidate the patterns of neural activity underlying behavior. In this dissertation, we developed novel computational tools to help scientists selectively activate ensembles and analyze large-scale neural activity with single-cell resolution.One method to precisely activate cortical ensembles while limiting off-target effects is to stimulate pattern completion neurons. Pattern completion neurons are subsets of neurons in an ensemble that, when activated, can trigger the activation of the rest of the ensemble. However, scientists currently lack methods to reliably identify pattern completion neurons. The first project in this dissertation used computational modeling to identify characteristics of pattern completion neurons in cortical ensembles. We developed a realistic spiking model of layer 2/3 of the mouse visual cortex. We then identified ensembles in the network and quantified the pattern completion capability of different neuron pairs in an ensemble. We analyzed the relationship between structural and dynamic parameters and pattern completion capability. We found that multiple graph theory parameters, and degree in particular, could predict the pattern completion capability of a neuron pair. Additionally, we found that neurons that fired earlier in an ensemble recall event were more likely to have pattern completion properties than neurons that fired later. Lastly, we showed that we can measure this temporal latency in vivo with modern calcium indicators. The later projects in this dissertation used deep learning to improve calcium imaging analysis. First, we developed a semi-supervised pipeline for neuron segmentation to reduce the burden of manual labeling. We compensated for the low number of ground truth labels in two ways. First, we augmented the training data with pseudolabels generated with ensemble learning. Next, we used domain-specific knowledge to predict optimal hyperparameters from the limited ground truth labels. Our pipeline achieved state-of-the-art accuracy when trained on only 25% the number of manual labels as supervised methods. Lastly, we developed a spatiotemporal deep learning pipeline to predict the underlying electrical activity from calcium imaging videos. Calcium imaging provides only an indirect measurement of spiking neural activity, and various spike inference pipelines have attempted to accurately recover spiking timing and rate. Our pipeline improved the detection of single-spike events and improved spike rate prediction throughout the video. This improved performance will help scientists reconstruct neural circuits and study single-cell responses to stimuli. Overall, the tools developed in this dissertation will help systems neuroscience researchers establish a causal link between neural activity and behavior and will help determine the precise patterns of neural activity underlying these behaviors.

This dissertation is an attempt to explain art in the 21st century by an artist/researcher. It is a theoretical writing on art informed by current discourses that influence art such as science and technology. There are two goals of this project. The first one is to understand art’s cultural role in the age of the anthropocene. What is the anthropocene? How does art’s role in society change in this particular geological epoch (following Crutzen’s definition), compared to for instance Holocene? This brings us to the second goal of my project. To better understand art’s role in society, can we benefit from a theory of art, that could present an insight on the dynamics within an artistic experience? What are the current tools and tendencies that can help form such a theory of art? Which fields can contribute to such an understanding?

As an instance of artistic research practice which involves both academic research and art practice, I will be using art projects as case studies to reach these goals. Case studies will consist of my own projects as well as projects by other artists who had been working on similar topics such as Edward Burtynsky, Chris Jordan, Louis Bec, Trevor Paglen, Patricia Piccinini and Lynn Hershmann to name a few. From Timothy Morton to Mackenzie Wark to Donna Haraway cultural theorists of our time, highlight the fact that there is a need for a cultural theory that can attend to what we might call the anthropocene. What is the contribution of art for such a theory? Or can art be instrumental in building a cultural theory at all? My dissertation offers a multi-disciplinary argument for the need to address such questions . Starting from art’s roots in biology and extending to what we might call our biological imagination, the dissertation focuses on art’s connection to biology to initiate a formula for art in the age of the anthropocene.

The structure of this dissertation comprises an introduction and four chapters, which contain three original musical compositions and one article. The first composition is a piece for wind symphony, the second one works with a string quartet, and the third explores the bass clarinet and electronics combination. The article comprises research on musical composition creativity and its pedagogical possibilities.

Chapter 1, “To Pernambuco with Love for Wind Symphony,” is a three-movement composition written as a tribute to the people of Pernambuco, one of the most musical and creative Brazilian states. During its 18 minutes, the piece explores some regional genres, such as the traditional Frevo and Maracatú, as well as the contemporary Maguebeat style. The first movement addresses the historical development of Frevo “Fanfarras” and “Orquestras,” which are musical ensemble sculpted in the roots of Pernambucan culture. The second movement still deals with Frevo. It starts with a percussion interlude, very common during frevo parades, followed by another particular manifestation of this genre: the clash of the bands. Different bands start to parade at different places in Pernambuco, in particular in its capital, Recife, and a surrounding city, Olinda. Eventually, some of those bands cross each other and start a beautiful sound battle to entice the people who were following the other band. Finally, the third movement expresses my personal admiration for Maracatú and Manguebeat. The former, a genre strongly connected with its Afro-Brazilian roots with a very characteristic complex percussive pattern. The latter, a genre born in the 1990s which expresses very well how people in Brazil cope with cultural globalization: they adjust any international cultural commodity to Brazilian unique flavors and roots.

The chapter 2, “String Quartet No. 1,” is a 19-minute piece that explores the traditional instrumentation of this ensemble with non-traditional musical material. Thus, the sonic result of the use of the digital delay effect inspires the first movement of this piece. Although there was no used resource other than the traditional instruments, the piece intends to emulate this and other effects acoustically. It works as a kind of stylized canon, with different dynamic layers. The second movement explores the sonorities derived from the amplitude modulation and frequency modulation synthesis. The complex harmonic result of such manipulations leads the group to represent it in complex chords with quarter-tone intervals. The last movement explores a Brazilian marginalized urban musical genre called Funk Carioca. It is inspired by the rhythmic and overall sonic quality of the genre, which encompasses some characteristic sounds provided by analogic drum machines that are represented by the string quartet instruments.

Chapter 3 presents the piece “Maco Light,” a piece for bass clarinet and prerecorded electronics that lasts exactly 7 minutes and 41 seconds. This piece is named after a North Carolinian legend originated in 1867. The railroad conductor Joe Baldwin died in a tragic train accident. Few weeks after this event people started to see apparitions of mysterious lights close to where the accident happened, the Maco station. This phenomenon, real or not, was reported dozens of times until the 1970s when Maco station was closed. Thus, this piece explores this story and uses the electronics to manipulate train sounds that engage in constant dialogue with the bass clarinet and with the story behind the music.

In chapter 4, the article “Educating for Composition Creativity” exposes how musical improvisation skills can be beneficial to the development of creative compositional strategies. It argues that improvisation should be part of the composition curriculum for college students and a particular subject in composition textbooks. Through an intense literature review in the fields of Neuroscience, Psychology, and Composition Pedagogy, this chapter makes evident that improvisation can allow different kinds of insights to happen during the compositional task. In particular, we used a framework that establishes three different modes of cognitive processing for creativity: deliberate, spontaneous, and flow mode. The neuroscientific evidence is thus interpreted over this framework which allowed the proposition of different strategies in coping creativity, depending on how well-defined or not the objective of a compositional work is.

The brain supports numerous complex processes ranging from signal processing and motor control to learning and memory. These processes rely on signal transduction between interconnected networks of neurons that form neural circuits. Understanding how neural circuits function requires non-invasive, genetically specific technologies to both record and manipulate neural activity. Recording neural activity establishes a correlative relationship between the activity and cognitive function, while manipulating neural activity establishes a causal relationship between the activity and behavioral or physiological processes. Genetically encoded protein tools facilitate neuroscience research in both experimental paradigms. Genetically encoded sensors enable optical recording of neural activity across a wide spatiotemporal range. These indicators detect diverse forms of neural activity, including calcium ion flux, membrane voltage potential, and neurotransmitter concentration. Conversely, optogenetic actuators enable targeted, optical excitation or inhibition of neurons upon activation with a specific wavelength of light.

Advancement of genetically encoded tools will allow researchers to access new experimental regimes of neuroscience. Enhancing the fluorescence response and temporal fidelity of genetically encoded sensors improves signal detection fidelity, enabling neuroscientists to access more neurons at once and more precisely analyze neural circuits. Expanding the spectral diversity of genetically encoded tools makes it possible to record from multiple neural populations simultaneously or to optogenetically excite one population with a specific wavelength of light while recording the activity of another in a distinct optical channel. Such multi-channel experiments enable neuroscientists to investigate the influence of the activity of an ensemble of neurons on the activity of another ensemble downstream in a neural circuit or feedback between neural circuits. However, expanding the palette of protein sensors and actuators for such multi-channel experiments has been challenging. Most state-of-the-art genetically encoded sensors fuse cyan-light-sensitive green fluorescent protein to a sensing domain, so the dual channel experiments described above require a complementary sensor or actuator that is sensitive to a spectrally distinct wavelength of light. However, the performance of red fluorescent genetically encoded tools typically lags relative to their green counterparts, and using cyan-light-activated sensors in conjunction with green-light-activated actuators introduces high optical crosstalk. Additionally, the dynamic properties and context-dependent performance of genetically encoded sensors make high-throughput screens of this class of tools labor intensive and time consuming. This constraint on the throughput of screens has limited development efforts to a miniscule fraction of the possible variants of each sensor.

In this dissertation, I expanded the spectral diversity of the tools described above and developed a novel strategy for high throughput evolution of genetically encoded sensors. First, I developed a red fluorescent genetically encoded voltage sensor by engineering the fluorescence resonance energy transfer (FRET) efficiency between a voltage sensitive domain and a red fluorescent protein. This red fluorescent sensor enabled high fidelity recordings of neural activity with sub-millisecond temporal resolution, dual-channel recordings in parallel with green fluorescent sensors, and simultaneous optogenetic excitation and voltage imaging with minimal optical crosstalk. Second, I developed an optogenetic actuator with a blue-shifted activation spectrum by employing this same FRET mechanism. I demonstrated that the activation spectrum of optogenetic tools could be tuned by engineering FRET efficiency between the actuator domain and a compatible fluorescent protein. This straightforward strategy represents a technical step forward for engineering the spectra of optogenetic actuators, which has been difficult to achieve without compromising functionality. Third, I developed a screening method that enabled pooled, high-throughput screens of diverse libraries containing genetically encoded sensor mutants. This method employed both experimental and computational advancements. I used in situ optical mRNA sequencing to determine the sequence of each screened protein variant and machine learning to predict the function of unscreened variants. I expanded the coverage of the possible sequence space by over an order of magnitude compared to traditional directed evolution.