Mechanisms of Sensory Adaptation in the Primate Visual System

Under ecological conditions, the luminance impinging on the retina varies within a dynamic range of 220 dB. Stimulus contrast can also vary drastically within a scene, and eye movements leave little time for sampling luminance. In addition, the amount of information reaching our visual system far exceeds the brain’s information processing capacity. Given the limited dynamic range of its neurons and its limited capacity in processing visual information in real-time, the brain deploys both structural and functional solutions that work in tandem to adapt to the surroundings. In this work, employing visual psychophysics and computational neuroscience, we study the mechanisms by which the brain adapts to the sensory signals that it encounters in the natural environment. We found that the processes underlying motion perception in ecological vision are mediated by an adaptive center-surround mechanism that trade-offs spatiotemporal resolution for signal enhancement when the signal is weak. We proposed a new dynamic neural network that can account for adaptive properties of motion integration and segregation under various luminance and contrast conditions. Finally, in order to clarify the implications of attentional mechanisms deployed to select inputs according to the brain’s processing resources, we tested the predictions of a neural model and showed that competitive interactions between parvocellular and magnocellular systems can explain the differential effects of attention on spatial and temporal acuity. This dissertation\u27s results contribute to the important work of reverse-engineering the human brain, with potential clinical and engineering applications

Sensor fusion in distributed cortical circuits

The substantial motion of the nature is to balance, to survive, and to reach perfection. The evolution in biological systems is a key signature of this quintessence. Survival cannot be achieved without understanding the surrounding world. How can a fruit fly live without searching for food, and thereby with no form of perception that guides the behavior? The nervous system of fruit fly with hundred thousand of neurons can perform very complicated tasks that are beyond the power of an advanced supercomputer. Recently developed computing machines are made by billions of transistors and they are remarkably fast in precise calculations. But these machines are unable to perform a single task that an insect is able to do by means of thousands of neurons. The complexity of information processing and data compression in a single biological neuron and neural circuits are not comparable with that of developed today in transistors and integrated circuits. On the other hand, the style of information processing in neural systems is also very different from that of employed by microprocessors which is mostly centralized. Almost all cognitive functions are generated by a combined effort of multiple brain areas. In mammals, Cortical regions are organized hierarchically, and they are reciprocally interconnected, exchanging the information from multiple senses. This hierarchy in circuit level, also preserves the sensory world within different levels of complexity and within the scope of multiple modalities. The main behavioral advantage of that is to understand the real-world through multiple sensory systems, and thereby to provide a robust and coherent form of perception. When the quality of a sensory signal drops, the brain can alternatively employ other information pathways to handle cognitive tasks, or even to calibrate the error-prone sensory node. Mammalian brain also takes a good advantage of multimodal processing in learning and development; where one sensory system helps another sensory modality to develop. Multisensory integration is considered as one of the main factors that generates consciousness in human. Although, we still do not know where exactly the information is consolidated into a single percept, and what is the underpinning neural mechanism of this process? One straightforward hypothesis suggests that the uni-sensory signals are pooled in a ploy-sensory convergence zone, which creates a unified form of perception. But it is hard to believe that there is just one single dedicated region that realizes this functionality. Using a set of realistic neuro-computational principles, I have explored theoretically how multisensory integration can be performed within a distributed hierarchical circuit. I argued that the interaction of cortical populations can be interpreted as a specific form of relation satisfaction in which the information preserved in one neural ensemble must agree with incoming signals from connected populations according to a relation function. This relation function can be seen as a coherency function which is implicitly learnt through synaptic strength. Apart from the fact that the real world is composed of multisensory attributes, the sensory signals are subject to uncertainty. This requires a cortical mechanism to incorporate the statistical parameters of the sensory world in neural circuits and to deal with the issue of inaccuracy in perception. I argued in this thesis how the intrinsic stochasticity of neural activity enables a systematic mechanism to encode probabilistic quantities within neural circuits, e.g. reliability, prior probability. The systematic benefit of neural stochasticity is well paraphrased by the problem of Duns Scotus paradox: imagine a donkey with a deterministic brain that is exposed to two identical food rewards. This may make the animal suffer and die starving because of indecision. In this thesis, I have introduced an optimal encoding framework that can describe the probability function of a Gaussian-like random variable in a pool of Poisson neurons. Thereafter a distributed neural model is proposed that can optimally combine conditional probabilities over sensory signals, in order to compute Bayesian Multisensory Causal Inference. This process is known as a complex multisensory function in the cortex. Recently it is found that this process is performed within a distributed hierarchy in sensory cortex. Our work is amongst the first successful attempts that put a mechanistic spotlight on understanding the underlying neural mechanism of Multisensory Causal Perception in the brain, and in general the theory of decentralized multisensory integration in sensory cortex. Engineering information processing concepts in the brain and developing new computing technologies have been recently growing. Neuromorphic Engineering is a new branch that undertakes this mission. In a dedicated part of this thesis, I have proposed a Neuromorphic algorithm for event-based stereoscopic fusion. This algorithm is anchored in the idea of cooperative computing that dictates the defined epipolar and temporal constraints of the stereoscopic setup, to the neural dynamics. The performance of this algorithm is tested using a pair of silicon retinas

Sensor fusion in distributed cortical circuits

The substantial motion of the nature is to balance, to survive, and to reach perfection. The evolution in biological systems is a key signature of this quintessence. Survival cannot be achieved without understanding the surrounding world. How can a fruit fly live without searching for food, and thereby with no form of perception that guides the behavior? The nervous system of fruit fly with hundred thousand of neurons can perform very complicated tasks that are beyond the power of an advanced supercomputer. Recently developed computing machines are made by billions of transistors and they are remarkably fast in precise calculations. But these machines are unable to perform a single task that an insect is able to do by means of thousands of neurons. The complexity of information processing and data compression in a single biological neuron and neural circuits are not comparable with that of developed today in transistors and integrated circuits. On the other hand, the style of information processing in neural systems is also very different from that of employed by microprocessors which is mostly centralized. Almost all cognitive functions are generated by a combined effort of multiple brain areas. In mammals, Cortical regions are organized hierarchically, and they are reciprocally interconnected, exchanging the information from multiple senses. This hierarchy in circuit level, also preserves the sensory world within different levels of complexity and within the scope of multiple modalities. The main behavioral advantage of that is to understand the real-world through multiple sensory systems, and thereby to provide a robust and coherent form of perception. When the quality of a sensory signal drops, the brain can alternatively employ other information pathways to handle cognitive tasks, or even to calibrate the error-prone sensory node. Mammalian brain also takes a good advantage of multimodal processing in learning and development; where one sensory system helps another sensory modality to develop. Multisensory integration is considered as one of the main factors that generates consciousness in human. Although, we still do not know where exactly the information is consolidated into a single percept, and what is the underpinning neural mechanism of this process? One straightforward hypothesis suggests that the uni-sensory signals are pooled in a ploy-sensory convergence zone, which creates a unified form of perception. But it is hard to believe that there is just one single dedicated region that realizes this functionality. Using a set of realistic neuro-computational principles, I have explored theoretically how multisensory integration can be performed within a distributed hierarchical circuit. I argued that the interaction of cortical populations can be interpreted as a specific form of relation satisfaction in which the information preserved in one neural ensemble must agree with incoming signals from connected populations according to a relation function. This relation function can be seen as a coherency function which is implicitly learnt through synaptic strength. Apart from the fact that the real world is composed of multisensory attributes, the sensory signals are subject to uncertainty. This requires a cortical mechanism to incorporate the statistical parameters of the sensory world in neural circuits and to deal with the issue of inaccuracy in perception. I argued in this thesis how the intrinsic stochasticity of neural activity enables a systematic mechanism to encode probabilistic quantities within neural circuits, e.g. reliability, prior probability. The systematic benefit of neural stochasticity is well paraphrased by the problem of Duns Scotus paradox: imagine a donkey with a deterministic brain that is exposed to two identical food rewards. This may make the animal suffer and die starving because of indecision. In this thesis, I have introduced an optimal encoding framework that can describe the probability function of a Gaussian-like random variable in a pool of Poisson neurons. Thereafter a distributed neural model is proposed that can optimally combine conditional probabilities over sensory signals, in order to compute Bayesian Multisensory Causal Inference. This process is known as a complex multisensory function in the cortex. Recently it is found that this process is performed within a distributed hierarchy in sensory cortex. Our work is amongst the first successful attempts that put a mechanistic spotlight on understanding the underlying neural mechanism of Multisensory Causal Perception in the brain, and in general the theory of decentralized multisensory integration in sensory cortex. Engineering information processing concepts in the brain and developing new computing technologies have been recently growing. Neuromorphic Engineering is a new branch that undertakes this mission. In a dedicated part of this thesis, I have proposed a Neuromorphic algorithm for event-based stereoscopic fusion. This algorithm is anchored in the idea of cooperative computing that dictates the defined epipolar and temporal constraints of the stereoscopic setup, to the neural dynamics. The performance of this algorithm is tested using a pair of silicon retinas

Value-based decision making via sequential sampling with hierarchical competition and attentional modulation

In principle, formal dynamical models of decision making hold the potential to represent fundamental computations underpinning value-based (i.e., preferential) decisions in addition to perceptual decisions. Sequential-sampling models such as the race model and the drift-diffusion model that are grounded in simplicity, analytical tractability, and optimality remain popular, but some of their more recent counterparts have instead been designed with an aim for more feasibility as architectures to be implemented by actual neural systems. Connectionist models are proposed herein at an intermediate level of analysis that bridges mental phenomena and underlying neurophysiological mechanisms. Several such models drawing elements from the established race, drift-diffusion, feedforward-inhibition, divisive-normalization, and competing-accumulator models were tested with respect to fitting empirical data from human participants making choices between foods on the basis of hedonic value rather than a traditional perceptual attribute. Even when considering performance at emulating behavior alone, more neurally plausible models were set apart from more normative race or drift-diffusion models both quantitatively and qualitatively despite remaining parsimonious. To best capture the paradigm, a novel six-parameter computational model was formulated with features including hierarchical levels of competition via mutual inhibition as well as a static approximation of attentional modulation, which promotes “winner-take-all” processing. Moreover, a meta-analysis encompassing several related experiments validated the robustness of model-predicted trends in humans’ value-based choices and concomitant reaction times. These findings have yet further implications for analysis of neurophysiological data in accordance with computational modeling, which is also discussed in this new light

Value-Based Decision Making and Learning as Algorithms Computed by the Nervous System

How do we do what we do? Casting light on this essential question, the blossoming perspective of computational cognitive neuroscience gives rise to the present exposition of the nervous system and its phenomena of value-based decision making and learning. As justified herein by not only theory but also simulation against empirical data, human decision making and learning are framed mathematically in the explicit terms of two fundamental classes of algorithms--namely, sequential sampling and reinforcement learning. These counterparts are complementary in their coverage of the dynamics of unified neural, mental, and behavioral processes at different temporal scales. Novel variants of models based on such algorithms are introduced here to account for findings from experiments including measurements of both behavior and the brain in human participants. In principle, formal dynamical models of decision making hold the potential to represent fundamental computations underpinning value-based (i.e., preferential) decisions in addition to perceptual decisions. Sequential-sampling models such as the race model and the drift-diffusion model that are grounded in simplicity, analytical tractability, and optimality remain popular, but some of their more recent counterparts have instead been designed with an aim for more feasibility as architectures to be implemented by actual neural systems. In Chapter 2, connectionist models are proposed at an intermediate level of analysis that bridges mental phenomena and underlying neurophysiological mechanisms. Several such models drawing elements from the established race, drift-diffusion, feedforward-inhibition, divisive-normalization, and competing-accumulator models were tested with respect to fitting empirical data from human participants making choices between foods on the basis of hedonic value rather than a traditional perceptual attribute. Even when considering performance at emulating behavior alone, more neurally plausible models were set apart from more normative race or drift-diffusion models both quantitatively and qualitatively despite remaining parsimonious. To best capture the paradigm, a novel six-parameter computational model was formulated with features including hierarchical levels of competition via mutual inhibition as well as a static approximation of attentional modulation, which promotes "winner-take-all" processing. Moreover, a meta-analysis encompassing several related experiments validated the robustness of model-predicted trends in humans' value-based choices and concomitant reaction times. These findings have yet further implications for analysis of neurophysiological data in accordance with computational modeling, which is also discussed in this new light. Decision making in any brain is imperfect and costly in terms of time and energy. Operating under such constraints, an organism could be in a position to improve performance if an opportunity arose to exploit informative patterns in the environment being searched. Such an improvement of performance could entail both faster and more accurate (i.e., reward-maximizing) decisions. Chapter 3 investigated the extent to which human participants could learn to take advantage of immediate patterns in the spatial arrangement of serially presented foods such that a region of space would consistently be associated with greater subjective value. Eye movements leading up to choices demonstrated rapidly induced biases in the selective allocation of visual fixation and attention that were accompanied by both faster and more accurate choices of desired goods as implicit learning occurred. However, for the control condition with its spatially balanced reward environment, these subjects exhibited preexisting lateralized biases for eye and hand movements (i.e., leftward and rightward, respectively) that could act in opposition not only to each other but also to the orienting biases elicited by the experimental manipulation, producing an asymmetry between the left and right hemifields with respect to performance. Potentially owing at least in part to learned cultural conventions (e.g., reading from left to right), the findings herein particularly revealed an intrinsic leftward bias underlying initial saccades in the midst of more immediate feedback-directed processes for which spatial biases can be learned flexibly to optimize oculomotor and manual control in value-based decision making. The present study thus replicates general findings of learned attentional biases in a novel context with inherently rewarding stimuli and goes on to further elucidate the interactions between endogenous and exogenous biases. Prediction-error signals consistent with formal models of "reinforcement learning" (RL) have repeatedly been found within dopaminergic nuclei of the midbrain and dopaminoceptive areas of the striatum. However, the precise form of the RL algorithms implemented in the human brain is not yet well determined. For Chapter 4, we created a novel paradigm optimized to dissociate the subtypes of reward-prediction errors that function as the key computational signatures of two distinct classes of RL models--namely, "actor/critic" models and action-value-learning models (e.g., the Q-learning model). The state-value-prediction error (SVPE), which is independent of actions, is a hallmark of the actor/critic architecture, whereas the action-value-prediction error (AVPE) is the distinguishing feature of action-value-learning algorithms. To test for the presence of these prediction-error signals in the brain, we scanned human participants with a high-resolution functional magnetic-resonance imaging (fMRI) protocol optimized to enable measurement of neural activity in the dopaminergic midbrain as well as the striatal areas to which it projects. In keeping with the actor/critic model, the SVPE signal was detected in the substantia nigra. The SVPE was also clearly present in both the ventral striatum and the dorsal striatum. However, alongside these purely state-value-based computations we also found evidence for AVPE signals throughout the striatum. These high-resolution fMRI findings suggest that model-free aspects of reward learning in humans can be explained algorithmically with RL in terms of an actor/critic mechanism operating in parallel with a system for more direct action-value learning.</p

Characterization of aminergic neurons controlling behavioral persistence and motivation in Drosophila melanogaster

Deprivation is at odds with survival. To obliterate their condition of hunger animals engage in costly foraging behavior. This conundrum demands unceasing integration of external sensory processing and internal metabolic monitors. Unsurprisingly, such critical behaviors are translated to strong impulses. If unchecked, however, impulsivity can trap animals in unfavorable behavioral states and prevent them from exploiting other valuable opportunities. Categorically, motivational mechanisms have been proposed as the conduit to comply with or decline a response to a strong impulse. Thus, motivation emerges as a critical determinant for observed animal behavioral variability at a given time. Although neuronal circuit diagrams may be deceptively static, neuromodulation can implement behavioral variability in the nervous systems. Bioamines, such as dopamine and norepinephrine, mediate modulatory impact on intrinsic motivational circuits that govern feeding and reward. Across model organisms, however, how animals integrate and update decision-making based on the current motivational and internal states are still poorly understood at the molecular and circuitry levels. Due to its extensive toolbox and amenable miniature nervous systems, Drosophila melanogaster is poised to enrich the current perspective for these concepts. For Drosophila melanogaster, certain odors are salient cues for long distance foraging events. To explore how starved flies make goal-directed decisions, I developed a novel spherical treadmill paradigm. Through the utilization of high-resolution behavioral analyses and tight control of, otherwise highly turbulent, odor delivery, I found that food-deprived flies tracked vinegar persistently even in the repeated absence of a food reward. Combining this behavioral paradigm with immediate neuronal manipulations revealed that this innate persistence recruited circuits that are traditionally linked with learning and memory in an internal state-dependent manner. TH+ cluster dopaminergic neurons, operators of punishment learning, and Dop1R2 signaling enabled this olfactory-driven persistence. Downstream of these dopaminergic neurons, a single mushroom body output neuron, MVP2 was crucial for persistence. MVP2 was necessary and sufficient to integrate hunger state as the underlying motivational drive for food-seeking persistence. Furthermore, I investigated how this strong impulse is counteracted when a fly reaches its goal, nutritious food. A change from odor tracking to food consumption demands the coordination of different sensory systems and motor control subunits. Norepinephrine is implemented in such global switches; such as fight or flight transitions. Using optogenetic manipulation, I demonstrated that the food-seeking drive was suppressed by, an insect norepinephrine analog, octopaminergic input, via VPM4 neurons. Being connected to MVP2 synaptically, which we showed using high-resolution tracing techniques, and a surrogate for feeding at the neuronal level, VPM4 neurons acted as the inhibitory brake on persistent odor tracking to allow feeding related behavior. As a culmination of novel paradigm development, thermo/optogenetic neuronal manipulations and connectomics, this work presents a neuronal microcircuit that recapitulates the alterations of animal behavior faithfully from odor tracking to olfactory suppression during feeding. Specific subsets of dopaminergic and octopaminergic neurons are found to be mediators of motivationally driven events. My findings provide fresh mechanistic insights on how multimodal integration can occur in the brain, how such systems are prone to the internal states, and offers several plausible explanations on how persistence emerges. Finally, this work might serve as a template to better understand the roles and the functional diversity of mammalian aminergic neurons

Characterization of aminergic neurons controlling behavioral persistence and motivation in Drosophila melanogaster

Deprivation is at odds with survival. To obliterate their condition of hunger animals engage in costly foraging behavior. This conundrum demands unceasing integration of external sensory processing and internal metabolic monitors. Unsurprisingly, such critical behaviors are translated to strong impulses. If unchecked, however, impulsivity can trap animals in unfavorable behavioral states and prevent them from exploiting other valuable opportunities. Categorically, motivational mechanisms have been proposed as the conduit to comply with or decline a response to a strong impulse. Thus, motivation emerges as a critical determinant for observed animal behavioral variability at a given time. Although neuronal circuit diagrams may be deceptively static, neuromodulation can implement behavioral variability in the nervous systems. Bioamines, such as dopamine and norepinephrine, mediate modulatory impact on intrinsic motivational circuits that govern feeding and reward. Across model organisms, however, how animals integrate and update decision-making based on the current motivational and internal states are still poorly understood at the molecular and circuitry levels. Due to its extensive toolbox and amenable miniature nervous systems, Drosophila melanogaster is poised to enrich the current perspective for these concepts. For Drosophila melanogaster, certain odors are salient cues for long distance foraging events. To explore how starved flies make goal-directed decisions, I developed a novel spherical treadmill paradigm. Through the utilization of high-resolution behavioral analyses and tight control of, otherwise highly turbulent, odor delivery, I found that food-deprived flies tracked vinegar persistently even in the repeated absence of a food reward. Combining this behavioral paradigm with immediate neuronal manipulations revealed that this innate persistence recruited circuits that are traditionally linked with learning and memory in an internal state-dependent manner. TH+ cluster dopaminergic neurons, operators of punishment learning, and Dop1R2 signaling enabled this olfactory-driven persistence. Downstream of these dopaminergic neurons, a single mushroom body output neuron, MVP2 was crucial for persistence. MVP2 was necessary and sufficient to integrate hunger state as the underlying motivational drive for food-seeking persistence. Furthermore, I investigated how this strong impulse is counteracted when a fly reaches its goal, nutritious food. A change from odor tracking to food consumption demands the coordination of different sensory systems and motor control subunits. Norepinephrine is implemented in such global switches; such as fight or flight transitions. Using optogenetic manipulation, I demonstrated that the food-seeking drive was suppressed by, an insect norepinephrine analog, octopaminergic input, via VPM4 neurons. Being connected to MVP2 synaptically, which we showed using high-resolution tracing techniques, and a surrogate for feeding at the neuronal level, VPM4 neurons acted as the inhibitory brake on persistent odor tracking to allow feeding related behavior. As a culmination of novel paradigm development, thermo/optogenetic neuronal manipulations and connectomics, this work presents a neuronal microcircuit that recapitulates the alterations of animal behavior faithfully from odor tracking to olfactory suppression during feeding. Specific subsets of dopaminergic and octopaminergic neurons are found to be mediators of motivationally driven events. My findings provide fresh mechanistic insights on how multimodal integration can occur in the brain, how such systems are prone to the internal states, and offers several plausible explanations on how persistence emerges. Finally, this work might serve as a template to better understand the roles and the functional diversity of mammalian aminergic neurons

Local and Iterative Visual Processing Deficits in Schizophrenia

University of Minnesota Ph.D. dissertation. May 2017. Major: Psychology. Advisors: Cheryl Olman, Shmuel Lissek. 1 computer file (PDF); vii, 103 pages.Evidence of dysfunctional visual processing in schizophrenia patients has been noted in all stages of the visual processing pathway. The iterative nature of vision- with hierarchical feedforward signals, modulating feedback signals, and horizontal intracortical connections- makes it difficult to pinpoint exact loci that are driving these deficits. This dissertation uses several contextual modulation paradigms in an effort to isolate and explain the nature of disruptions in iterative visual processing in schizophrenia. Chapter 1 provides an overview of visual processing dysfunctions in schizophrenia, and examines a variety of mechanisms that may play a role. These include neurotransmitters, magnocellular versus parvocellular processing streams, abnormal local connections, and abnormal long-range feedback connections. These are presented in the context of several theoretical perspectives of visual neuroscience. Chapter 2 provides functional magnetic resonance imaging data from a fractured ambiguous object task that probes the role of high-level qualities in primary visual cortex activation and interregional connectivity, and how these may be disrupted in psychosis. Chapter 3 introduces a computational model that attempts to fit parameters to psychophysical data to isolate disrupted mechanisms in schizophrenia. This model focuses on the role of gain control and segmentation of center and surround stimuli in a tilt illusion paradigm. Chapter 4 presents previous work, examining the modulatory effect of the NMDA receptor agonist d-cycoserine on conditioned fear generalization. This work was done in healthy controls as a step in expanding our knowledge of the function of d-cycloserine in increasing specificity and efficacy in the fear learning process