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

Sensor Fusion in the Perception of Self-Motion

This dissertation has been written at the Max Planck Institute for Biological Cybernetics (Max-Planck-Institut für Biologische Kybernetik) in Tübingen in the department of Prof. Dr. Heinrich H. Bülthoff. The work has universitary support by Prof. Dr. Günther Palm (University of Ulm, Abteilung Neuroinformatik). Main evaluators are Prof. Dr. Günther Palm, Prof. Dr. Wolfgang Becker (University of Ulm, Sektion Neurophysiologie) and Prof. Dr. Heinrich Bülthoff.amp;lt;bramp;gt;amp;lt;bramp;gt; The goal of this thesis was to investigate the integration of different sensory modalities in the perception of self-motion, by using psychophysical methods. Experiments with healthy human participants were to be designed for and performed in the Motion Lab, which is equipped with a simulator platform and projection screen. Results from psychophysical experiments should be used to refine models of the multisensory integration process, with an mphasis on Bayesian (maximum likelihood) integration mechanisms.amp;lt;bramp;gt;amp;lt;bramp;gt; To put the psychophysical experiments into the larger framework of research on multisensory integration in the brain, results of neuroanatomical and neurophysiological experiments on multisensory integration are also reviewed

The neural dynamics of hierarchical Bayesian causal inference in multisensory perception

How do we make inferences about the source of sensory signals? Here, the authors use Bayesian causal modeling and measures of neural activity to show how the brain dynamically codes for and combines sensory signals to draw causal inferences

Location representations of objects in cluttered scenes in the human brain

When we perceive a visual scene, we usually see an arrangement of multiple cluttered and partly overlapping objects, like a park with trees and people in it. Spatial attention helps us to prioritize relevant portions of such scenes to efficiently interact with our environments. In previous experiments on object recognition, objects were often presented in isolation, and these studies found that the location of objects is encoded early in time (before ∼150 ms) and in early visual cortex or in the dorsal stream. However, in real life objects rarely appear in isolation but are instead embedded in cluttered scenes. Encoding the location of an object in clutter might require fundamentally different neural computations. Therefore this dissertation addressed the question of how location representations of objects on cluttered backgrounds are encoded in the human brain. To answer this question, we investigated where in cortical space and when in neural processing time location representations emerge when objects are presented on cluttered backgrounds and which role spatial attention plays for the encoding of object location. We addressed these questions in two studies, both including fMRI and EEG experiments. The results of the first study showed that location representations of objects on cluttered backgrounds emerge along the ventral visual stream, peaking in region LOC with a temporal delay that was linked to recurrent processing. The second study showed that spatial attention modulated those location representations in mid- and high-level regions along the ventral stream and late in time (after ∼150 ms), independently of whether backgrounds were cluttered or not. These findings show that location representations emerge during late stages of processing both in cortical space and in neural processing time when objects are presented on cluttered backgrounds and that they are enhanced by spatial attention. Our results provide a new perspective on visual information processing in the ventral visual stream and on the temporal dynamics of location processing. Finally, we discuss how shared neural substrates of location and category representations in the brain might improve object recognition for real-world vision

Principles of sensorimotor control and learning in complex motor tasks

The brain coordinates a continuous coupling between perception and action in the presence of uncertainty and incomplete knowledge about the world. This mapping is enabled by control policies and motor learning can be perceived as the update of such policies on the basis of improving performance given some task objectives. Despite substantial progress in computational sensorimotor control and empirical approaches to motor adaptation, to date it remains unclear how the brain learns motor control policies while updating its internal model of the world. In light of this challenge, we propose here a computational framework, which employs error-based learning and exploits the brain’s inherent link between forward models and feedback control to compute dynamically updated policies. The framework merges optimal feedback control (OFC) policy learning with a steady system identification of task dynamics so as to explain behavior in complex object manipulation tasks. Its formalization encompasses our empirical findings that action is learned and generalised both with regard to a body-based and an object-based frame of reference. Importantly, our approach predicts successfully how the brain makes continuous decisions for the generation of complex trajectories in an experimental paradigm of unfamiliar task conditions. A complementary method proposes an expansion of the motor learning perspective at the level of policy optimisation to the level of policy exploration. It employs computational analysis to reverse engineer and subsequently assess the control process in a whole body manipulation paradigm. Another contribution of this thesis is to associate motor psychophysics and computational motor control to their underlying neural foundation; a link which calls for further advancement in motor neuroscience and can inform our theoretical insight to sensorimotor processes in a context of physiological constraints. To this end, we design, build and test an fMRI-compatible haptic object manipulation system to relate closed-loop motor control studies to neurophysiology. The system is clinically adjusted and employed to host a naturalistic object manipulation paradigm on healthy human subjects and Friedreich’s ataxia patients. We present methodology that elicits neuroimaging correlates of sensorimotor control and learning and extracts longitudinal neurobehavioral markers of disease progression (i.e. neurodegeneration). Our findings enhance the understanding of sensorimotor control and learning mechanisms that underlie complex motor tasks. They furthermore provide a unified methodological platform to bridge the divide between behavior, computation and neural implementation with promising clinical and technological implications (e.g. diagnostics, robotics, BMI).Open Acces

Functional and structural MRI image analysis for brain glial tumors treatment

Cotutela con il Dipartimento di Biotecnologie e Scienze della Vita, Universiità degli Studi dell'Insubria.openThis Ph.D Thesis is the outcome of a close collaboration between the Center for Research in Image Analysis and Medical Informatics (CRAIIM) of the Insubria University and the Operative Unit of Neurosurgery, Neuroradiology and Health Physics of the University Hospital ”Circolo Fondazione Macchi”, Varese. The project aim is to investigate new methodologies by means of whose, develop an integrated framework able to enhance the use of Magnetic Resonance Images, in order to support clinical experts in the treatment of patients with brain Glial tumor. Both the most common uses of MRI technology for non-invasive brain inspection were analyzed. From the Functional point of view, the goal has been to provide tools for an objective reliable and non-presumptive assessment of the brain’s areas locations, to preserve them as much as possible at surgery. From the Structural point of view, methodologies for fully automatic brain segmentation and recognition of the tumoral areas, for evaluating the tumor volume, the spatial distribution and to be able to infer correlation with other clinical data or trace growth trend, have been studied. Each of the proposed methods has been thoroughly assessed both qualitatively and quantitatively. All the Medical Imaging and Pattern Recognition algorithmic solutions studied for this Ph.D. Thesis have been integrated in GliCInE: Glioma Computerized Inspection Environment, which is a MATLAB prototype of an integrated analysis environment that offers, in addition to all the functionality specifically described in this Thesis, a set of tools needed to manage Functional and Structural Magnetic Resonance Volumes and ancillary data related to the acquisition and the patient.openInformaticaPedoia, ValentinaPedoia, Valentin

Functional and structural MRI image analysis for brain glial tumors treatment

This Ph.D Thesis is the outcome of a close collaboration between the Center for Research in Image Analysis and Medical Informatics (CRAIIM) of the Insubria University and the Operative Unit of Neurosurgery, Neuroradiology and Health Physics of the University Hospital ”Circolo Fondazione Macchi”, Varese. The project aim is to investigate new methodologies by means of whose, develop an integrated framework able to enhance the use of Magnetic Resonance Images, in order to support clinical experts in the treatment of patients with brain Glial tumor. Both the most common uses of MRI technology for non-invasive brain inspection were analyzed. From the Functional point of view, the goal has been to provide tools for an objective reliable and non-presumptive assessment of the brain’s areas locations, to preserve them as much as possible at surgery. From the Structural point of view, methodologies for fully automatic brain segmentation and recognition of the tumoral areas, for evaluating the tumor volume, the spatial distribution and to be able to infer correlation with other clinical data or trace growth trend, have been studied. Each of the proposed methods has been thoroughly assessed both qualitatively and quantitatively. All the Medical Imaging and Pattern Recognition algorithmic solutions studied for this Ph.D. Thesis have been integrated in GliCInE: Glioma Computerized Inspection Environment, which is a MATLAB prototype of an integrated analysis environment that offers, in addition to all the functionality specifically described in this Thesis, a set of tools needed to manage Functional and Structural Magnetic Resonance Volumes and ancillary data related to the acquisition and the patient

Proscription supports robust perceptual integration by suppression in human visual cortex.

Perception relies on integrating information within and between the senses, but how does the brain decide which pieces of information should be integrated and which kept separate? Here we demonstrate how proscription can be used to solve this problem: certain neurons respond best to unrealistic combinations of features to provide 'what not' information that drives suppression of unlikely perceptual interpretations. First, we present a model that captures both improved perception when signals are consistent (and thus should be integrated) and robust estimation when signals are conflicting. Second, we test for signatures of proscription in the human brain. We show that concentrations of inhibitory neurotransmitter GABA in a brain region intricately involved in integrating cues (V3B/KO) correlate with robust integration. Finally, we show that perturbing excitation/inhibition impairs integration. These results highlight the role of proscription in robust perception and demonstrate the functional purpose of 'what not' sensors in supporting sensory estimation