Neural circuit dynamics for sensory detection

We consider the question of how sensory networks enable the detection of sensory stimuli in a combinatorial coding space. We are specifically interested in the olfactory system, wherein recent experimental studies have reported the existence of rich, enigmatic response patterns associated with stimulus onset and offset. This study aims to identify the functional relevance of such response patterns (i.e., what benefits does such neural activity provide in the context of detecting stimuli in a natural environment). We study this problem through the lens of normative, optimization-based modeling. Here, we define the notion of a low-dimensional latent representation of stimulus identity, which is generated through action of the sensory network. The objective of our optimization framework is to ensure high-fidelity tracking of a nominal representation in this latent space in an energy-efficient manner. It turns out that the optimal motifs emerging from this framework possess morphologic similarity with prototypical onset and offset responses observe

Mechanistic Models of Neural Computation in the Fruit Fly Brain

Understanding the operating principles of the brain functions is the key to building novel computing architectures for mimicking human intelligence. Neural activities at different scales lead to different levels of brain functions. For example, cellular functions, such as sensory transduction, occur in the molecular reactions, and cognitive functions, such as recognition, emerge in neural systems across multiple brain regions. To bridge the gap between neuroscience and artificial computation, we need systematic development of mechanistic models for neural computation across multiple scales. Existing models of neural computation are often independently developed for a specific scale and hence not compatible with others. In this thesis, we investigate the neural computations in the fruit fly brain and devise mechanistic models at different scales in a systematic manner so that models at one scale constitute functional building blocks for the next scale. Our study spans from the molecular and circuit computations in the olfactory system to the system-level computation of the central complex in the fruit fly. First, we study how the two key aspects of odorant, identity and concentration, are encoded by the odorant transduction process at the molecular scale. We mathematically quantify the odorant space and propose a biophysical model of the olfactory sensory neuron (OSN). To validate our modeling approaches, we examine the OSN model with a multitude of odorant waveforms and demonstrate that the model output reproduces the temporal responses of OSNs obtained from in vivo electrophysiology recordings. In addition, we evaluate the model at the OSN population level and quantify the combinatorial complexity of the transformation taking place between the odorant space and the OSNs. The resulting concentration-dependent combinatorial code determines the complexity of the input space driving olfactory processing in the downstream neuropil, the antennal lobe. Second, we investigate the neural information processing in the antennal lobe across the molecule scale and the circuit scale. The antennal lobe encodes the output of the OSN population from a concentration-dependent code into a concentration-independent combinatorial code. To study the transformation of the combinatorial code, we construct a computational model of the antennal lobe that consists of two sub circuits, a predictive coding circuit and an on-off circuit, realized by two distinct local neuron networks, respectively. By examining the entire circuit model with both monomolecular odorant and odorant mixtures, we demonstrate that the predictive coding circuit encodes the odorant identity into concentration invariant code and the on-off circuit encodes the onset and the offset of a unique odorant identity. Third, we investigate the odorant representation inherent in the Kenyon cell activities in the mushroom body. The Kenyon cells encodes the output of the antennal lobe into a high-dimensional, sparse neural code that is immediately used for learning and memory formation. We model the Kenyon cell circuitry as a real-time feedback normalization circuit converting odorant information into a time-dependent hash codes. The resultant real-time hash code represents odorants, pure or mixture alike, in a way conducive to classifications, and suggests an intrinsic partition of the odorant space with similar hash codes. Forth, we study at the system scale the neural coding of the central complex. The central complex is a set of neuropils in the center of the fly brain that integrates multiple sensory information and play an important role in locomotor control. We create an application that enables simultaneous graphical querying and construction of executable model of the central complex neural circuitry. By reconfiguring the circuitry and generating different executable models, we compare the model response of the wild type and mutant fly strains. Finally, we show that the multi-scale study of the fruit fly brain is made possible by the Fruit Fly Brain Observatory (FFBO), an open-source platform to support open, collaborative fruit fly neuroscience research. The software architecture of the FFBO and its key application are highlighted along with several examples

Short-term memory and olfactory signal processing

Modern neural recording methodologies, including multi-electrode and optical recordings, allow us to monitor the large population of neurons with high temporal resolution. Such recordings provide rich datasets that are expected to understand better how information about the external world is internally represented and how these representations are altered over time. Achieving this goal requires the development of novel pattern recognition methods and/or the application of existing statistical methods in novel ways to gain insights into basic neural computational principles. In this dissertation, I will take this data-driven approach to dissect the role of short-term memory in olfactory signal processing in two relatively simple models of the olfactory system: fruit fly (Drosophila melanogaster) and locust (Schistocerca americana). First, I will focus on understanding how odor representations within a single stimulus exposure are refined across different populations of neurons (faster dynamics; on the order seconds) in the early olfactory circuits. Using light-sheet imaging datasets from transgenic flies expressing calcium indicators in select populations of neurons, I will reveal how odor representations are decorrelated over time in different neural populations. Further, I will examine how this computation is altered by short-term memory in this neural circuitry. Next, I will examine how neural representations for odorants at an ensemble level are altered across different exposures (slower dynamics; on the order of tens of seconds to minutes). I will examine the role of this short-term adaptation in altering neural representations for odor identity and intensity. Lastly, I will present approaches to help achieve robustness against both extrinsic and intrinsic perturbations of odor-evoked neural responses. I will conclude with a Boolean neural network inspired by the insect olfactory system and compare its performance against other state-of-the-art methods on standard machine learning benchmark datasets. In sum, this work will provide deeper insights into how short-term plasticity alters sensory neural representations and their computational significance

Optimal Adaptation Principles In Neural Systems

Animal brains are remarkably efficient in handling complex computational tasks, which are intractable even for state-of-the-art computers. For instance, our ability to detect visual objects in the presence of substantial variability and clutter sur- passes any algorithm. This ability seems even more surprising given the noisiness and biophysical constraints of neural circuits. This thesis focuses on understanding the theoretical principles governing how neural systems, at various scales, are adapted to the structure of their environment in order to interact with it and perform informa- tion processing tasks efficiently. Here, we study this question in three very different and challenging scenarios: i) how a sensory neural circuit the olfactory pathway is organised to efficiently process odour stimuli in a very high-dimensional space with complex structure; ii) how individual neurons in the sensory periphery exploit the structure in a fast-changing environment to utilise their dynamic range efficiently; iii) how the auditory system of whole organisms is able to efficiently exploit temporal structure in a noisy, fast-changing environment to optimise perception of ambiguous sounds. We also study the theoretical issues in developing principled measures of model complexity and extending classical complexity notions to explicitly account for the scale/resolution at which we observe a system

Algorithm and Hardware Co-design for Learning On-a-chip

abstract: Machine learning technology has made a lot of incredible achievements in recent years. It has rivalled or exceeded human performance in many intellectual tasks including image recognition, face detection and the Go game. Many machine learning algorithms require huge amount of computation such as in multiplication of large matrices. As silicon technology has scaled to sub-14nm regime, simply scaling down the device cannot provide enough speed-up any more. New device technologies and system architectures are needed to improve the computing capacity. Designing specific hardware for machine learning is highly in demand. Efforts need to be made on a joint design and optimization of both hardware and algorithm. For machine learning acceleration, traditional SRAM and DRAM based system suffer from low capacity, high latency, and high standby power. Instead, emerging memories, such as Phase Change Random Access Memory (PRAM), Spin-Transfer Torque Magnetic Random Access Memory (STT-MRAM), and Resistive Random Access Memory (RRAM), are promising candidates providing low standby power, high data density, fast access and excellent scalability. This dissertation proposes a hierarchical memory modeling framework and models PRAM and STT-MRAM in four different levels of abstraction. With the proposed models, various simulations are conducted to investigate the performance, optimization, variability, reliability, and scalability. Emerging memory devices such as RRAM can work as a 2-D crosspoint array to speed up the multiplication and accumulation in machine learning algorithms. This dissertation proposes a new parallel programming scheme to achieve in-memory learning with RRAM crosspoint array. The programming circuitry is designed and simulated in TSMC 65nm technology showing 900X speedup for the dictionary learning task compared to the CPU performance. From the algorithm perspective, inspired by the high accuracy and low power of the brain, this dissertation proposes a bio-plausible feedforward inhibition spiking neural network with Spike-Rate-Dependent-Plasticity (SRDP) learning rule. It achieves more than 95% accuracy on the MNIST dataset, which is comparable to the sparse coding algorithm, but requires far fewer number of computations. The role of inhibition in this network is systematically studied and shown to improve the hardware efficiency in learning.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Neuronal underpinning of reproductive state dependent olfactory behavior in Drosophila

A general question in neuroscience is how the flow of sensory information is encoded towards a behavioral response. These behavioral responses can be interpreted as decisions the organism needs to make to get a most beneficial outcome. Factors which can influence these decisions can be external or internal. Considering sensory information, external stimuli can elicit "innate" responses to a sensory input, which lead to a certain behavior. Interestingly, these responses can be overwritten given a certain experience or context. The internal state of an organism can be such a context. Internal states, such as age, stress, hunger, or reproductive state can have effects on chemosensory decision making behavior. Such behavior usually manifests itself by attraction or aversion towards a certain odor or taste. Occasionally, transient neuromodulation can affect these behaviors, by focusing an animal's attention to relevant sensory stimuli in its environment. This might facilitate remembering relevant vs. irrelevant stimuli. Here, we are investigating the role of such a sensory neuromodulation and the formation of memory in the female fruit fly, Drosophila melanogaster. Previous work from our lab has shown that mating changes the sensitivity of olfactory and gustatory neurons with the help of specific neuromodulators that act directly on these chemosensory neurons. However, this very transient neuromodulation leads to a long-term behavioral change in females: for instance, while virgin flies usually prefer low concentrations of polyamines, mated flies will prefer higher concentrations after the mating experience and will continue this behavior for up to two weeks until falling back to a virgin-like state. Drosophila's genetic toolset allows us to test the hypothesis that this transient sensory enhancement facilitates the formation of a long-lasting memory. Using a quantitative olfactory choice assay, my collaborators and I silenced and activated neuronal activity in different parts of the fly's associative memory center (i.e. the mushroom body). We revealed a possible neuronal pathway and its modulatory switch between virgin and mated state. These findings suggest that dopaminergic neurons, which are innervating the mushroom body, control virgin vs. mated female behavior by processing sensory input differentially before and after mating, respectively. Furthermore, our data suggests that courtship and pheromones are highly important signals to trigger the reproductive state dependent change in olfactory preference behavior. In addition, my collaborators and I wanted to use state-of-the-art techniques to shed light on the detection of nutrients valuable for the gravid fly by using bioinformatic tools and to promote these methods to the biological fields. As two-photon laser scanning microscopy is an important tool for neuroscientific research in the fly and beyond, I built such a microscope. Harnessing this experience, I have, in collaboration, written a guide for life scientists wishing to build or purchase such a microscope. A joint effort between established behavioral assays and technological advances, such as bioinformatic tools, can support and extend our understanding of neuronal circuits underlying reproductive state dependent behaviors

Neuronal underpinning of reproductive state dependent olfactory behavior in Drosophila

A general question in neuroscience is how the flow of sensory information is encoded towards a behavioral response. These behavioral responses can be interpreted as decisions the organism needs to make to get a most beneficial outcome. Factors which can influence these decisions can be external or internal. Considering sensory information, external stimuli can elicit "innate" responses to a sensory input, which lead to a certain behavior. Interestingly, these responses can be overwritten given a certain experience or context. The internal state of an organism can be such a context. Internal states, such as age, stress, hunger, or reproductive state can have effects on chemosensory decision making behavior. Such behavior usually manifests itself by attraction or aversion towards a certain odor or taste. Occasionally, transient neuromodulation can affect these behaviors, by focusing an animal's attention to relevant sensory stimuli in its environment. This might facilitate remembering relevant vs. irrelevant stimuli. Here, we are investigating the role of such a sensory neuromodulation and the formation of memory in the female fruit fly, Drosophila melanogaster. Previous work from our lab has shown that mating changes the sensitivity of olfactory and gustatory neurons with the help of specific neuromodulators that act directly on these chemosensory neurons. However, this very transient neuromodulation leads to a long-term behavioral change in females: for instance, while virgin flies usually prefer low concentrations of polyamines, mated flies will prefer higher concentrations after the mating experience and will continue this behavior for up to two weeks until falling back to a virgin-like state. Drosophila's genetic toolset allows us to test the hypothesis that this transient sensory enhancement facilitates the formation of a long-lasting memory. Using a quantitative olfactory choice assay, my collaborators and I silenced and activated neuronal activity in different parts of the fly's associative memory center (i.e. the mushroom body). We revealed a possible neuronal pathway and its modulatory switch between virgin and mated state. These findings suggest that dopaminergic neurons, which are innervating the mushroom body, control virgin vs. mated female behavior by processing sensory input differentially before and after mating, respectively. Furthermore, our data suggests that courtship and pheromones are highly important signals to trigger the reproductive state dependent change in olfactory preference behavior. In addition, my collaborators and I wanted to use state-of-the-art techniques to shed light on the detection of nutrients valuable for the gravid fly by using bioinformatic tools and to promote these methods to the biological fields. As two-photon laser scanning microscopy is an important tool for neuroscientific research in the fly and beyond, I built such a microscope. Harnessing this experience, I have, in collaboration, written a guide for life scientists wishing to build or purchase such a microscope. A joint effort between established behavioral assays and technological advances, such as bioinformatic tools, can support and extend our understanding of neuronal circuits underlying reproductive state dependent behaviors

Sensory Processing and Associative Learning in Connectome-Based Neural Circuits

There has been a significant increase in the amount of connectomics data available at the level of single neurons and single synapses in the last few years. This increase enabled investigations into the structure and function of neural circuits in much greater detail than ever before. Thus, the next step in our quest to understand the brain's functional logic is the development of tools and methods to enable us to extract data from and model these new connectomics datasets, and their use to start to examine the brain computationally. Specifically, for Drosophila melanogaster, the fruit fly, a large amount of data on the connectome have become available in the last few years. In this dissertation, we start by introducing the tools we have built to extract information from the Drosophila connectome and to create spiking models of neuropils using this information to model sensory processing and associative learning circuits at single-synapse scale. We then use the toolkit we have introduced to explore sensory processing and associative learning in the brain. First, we introduce FlyBrainLab, an interactive computing environment designed to accelerate the discovery of functional logic of the Drosophila brain. Then, we propose a programmable ontology that expands the scope of the current Drosophila brain anatomy ontologies to encompass the functional logic of the fly brain, providing a language not only for modeling circuit motifs but also for programmatically exploring their functional logic; we introduce the FeedbackCircuits library for exploring the functional logic of the massive number of feedback loops (motifs) in the fruit fly brain, and NeuroNLP++, an application that supports free-form English queries for constructing functional brain circuits fully anchored on the available connectome/synaptome datasets. Thirdly, following up on the second, we explore the construction of antennal lobe circuits using models of glomeruli. We explore the composability of the connectivity of glomeruli with local neuron feedback loops, and quantitatively characterize the I/O of the AL as a function of feedback loop motifs in the one-glomerulus, two-glomerulus and 23-glomerulus scenarios. Lastly, in the final chapter, we consider the modeling of the mushroom body, a second order olfactory neuropil and a center of associative learning, to demonstrate how the architecture of the circuit interacts with the circuit mechanisms by which sensory inputs are represented and memories are updated. Thus, in this dissertation we introduce an approach for the analysis and modeling of neural circuits based on connectomics data, and apply this approach to neural circuits spanning multiple neuropils to extract and analyze the principles of computation in the brain. The methodology described here is designed to be applied to different sensory systems and organisms to infer the functional logic of connectome-based neural circuits