Whole brain Probabilistic Generative Model toward Realizing Cognitive Architecture for Developmental Robots

Building a humanlike integrative artificial cognitive system, that is, an artificial general intelligence, is one of the goals in artificial intelligence and developmental robotics. Furthermore, a computational model that enables an artificial cognitive system to achieve cognitive development will be an excellent reference for brain and cognitive science. This paper describes the development of a cognitive architecture using probabilistic generative models (PGMs) to fully mirror the human cognitive system. The integrative model is called a whole-brain PGM (WB-PGM). It is both brain-inspired and PGMbased. In this paper, the process of building the WB-PGM and learning from the human brain to build cognitive architectures is described.Comment: 55 pages, 8 figures, submitted to Neural Network

Brain network mechanisms in learning behavior

The study of learning has been a central focus of psychology and neuroscience since their inception. Cognitive neuroscience’s traditional approach to understanding learn-ing has been to decompose it into discrete cognitive processes with separable and localized underlying neural systems. While this focus on modular cognitive functions for individual brain areas has led to considerable progress, there is increasing evidence that much of learn-ing behavior relies on overlapping cognitive and neural systems, which may be harder to disentangle than previously envisioned. This is not surprising, as the processes underlying learning must involve widespread integration of information from sensory, affective, and motor sources. The standard tools of cognitive neuroscience limit our ability to describe processes that rely on widespread coordination of brain activity. To understand learning, it will be necessary to characterize dynamic co-activation at the circuit level. In this dissertation, I present three studies that seek to describe the roles of distrib-uted brain networks in learning. I begin by giving an overview of our current understand-ing of multiple forms of learning, describing the neural and computational mechanisms thought to underlie incremental feedback-based learning and flexible episodic memory. I will focus in particular on the difficulties in separating these processes at the cognitive level and in localizing them to individual regions at the neural level. I will then describe recent findings that have begun to characterize the brain’s large-scale network structure, emphasiz-ing the potential roles that distributed networks could play in understanding learning and cognition more generally. I will end the introduction by reviewing current attempts to char-acterize the dynamics of large-scale brain networks, which will be essential for providing a mechanistic link to learning behavior. Chapter 2 is a study demonstrating that intrinsic connectivity between the hippo-campus and the ventromedial prefrontal cortex, as well as between these regions and dis-tributed brain networks, is related to individual differences in the transfer of learning on a sensory preconditioning task. The hippocampus and ventromedial prefrontal cortex have both been shown to be involved in this type of learning, and this study represents an early attempt to link connectivity between individual regions and broader networks to learning processes. Chapter 3 is a study that takes advantage of recent developments in mathematical modeling of temporal networks to demonstrate a relationship between large-scale network dynamics and reinforcement learning within individuals. This study shows that the flexibil-ity of network connectivity in the striatum is related to learning performance over time, as well as to individual differences in parameters estimated from computational models of re-inforcement learning. Notably, connectivity between the striatum and visual as well as or-bitofrontal regions increased over the course of the task, which is consistent with an inte-grative role for the region in learning value-based associations. Network flexibility in a dis-tinct set of regions is associated with episodic memory for object images presented during the learning task. Chapter 4 examines the role of dopamine, a neurotransmitter strongly linked to val-ue updating in reinforcement learning, in the dynamic network changes occurring during learning. Patients with Parkinson’s disease, who experience a loss of dopaminergic neu-rons in the substantia nigra, performed a reversal-learning task while undergoing functional magnetic resonance imaging. Patients were scanned on and off of a dopamine precursor medication (levodopa) in a within-subject design in order to examine the impact of dopa-mine on brain network dynamics during learning. The reversal provided an experimental manipulation of dynamic connectivity, and patients on medication showed greater modula-tion of striatal-cortical connectivity. Similar results were found in a number of regions re-ceiving midbrain projections including the prefrontal cortex and medial temporal lobe. This study indicates that dopamine inputs from the midbrain modulate large-scale network dy-namics during learning, providing a direct link between reinforcement learning theories of value updating and network neuroscience accounts of dynamic connectivity. Together, these results indicate that large-scale networks play a critical role in multi-ple forms of learning behavior. Each highlights the potential importance of understanding dynamic routing and integration of information across large-scale circuits for our concep-tion of learning and other cognitive processes. Understanding the when, where, and how of this information flow in the brain may provide an alternative or compliment to traditional theories of distinct learning systems. These studies also illustrate challenges in integrating this perspective with established theories in cognitive neuroscience. Chapter 5 will situate the studies in a broader discussion of how brain activity relates to cognition in general, while pointing out current roadblocks and potential ways forward for a cognitive network neuroscience of learning

Visual and Contextual Modeling for the Detection of Repeated Mild Traumatic Brain Injury.

Currently, there is a lack of computational methods for the evaluation of mild traumatic brain injury (mTBI) from magnetic resonance imaging (MRI). Further, the development of automated analyses has been hindered by the subtle nature of mTBI abnormalities, which appear as low contrast MR regions. This paper proposes an approach that is able to detect mTBI lesions by combining both the high-level context and low-level visual information. The contextual model estimates the progression of the disease using subject information, such as the time since injury and the knowledge about the location of mTBI. The visual model utilizes texture features in MRI along with a probabilistic support vector machine to maximize the discrimination in unimodal MR images. These two models are fused to obtain a final estimate of the locations of the mTBI lesion. The models are tested using a novel rodent model of repeated mTBI dataset. The experimental results demonstrate that the fusion of both contextual and visual textural features outperforms other state-of-the-art approaches. Clinically, our approach has the potential to benefit both clinicians by speeding diagnosis and patients by improving clinical care

Reading aloud boosts connectivity through the putamen

Functional neuroimaging and lesion studies have frequently reported thalamic and putamen activation during reading and speech production. However, it is currently unknown how activity in these structures interacts with that in other reading and speech production areas. This study investigates how reading aloud modulates the neuronal interactions between visual recognition and articulatory areas, when both the putamen and thalamus are explicitly included. Using dynamic causal modeling in skilled readers who were reading regularly spelled English words, we compared 27 possible pathways that might connect the ventral anterior occipito-temporal sulcus (aOT) to articulatory areas in the precentral cortex (PrC). We focused on whether the neuronal interactions within these pathways were increased by reading relative to picture naming and other visual and articulatory control conditions. The results provide strong evidence that reading boosts the aOT–PrC pathway via the putamen but not the thalamus. However, the putamen pathway was not exclusive because there was also evidence for another reading pathway that did not involve either the putamen or the thalamus. We conclude that the putamen plays a special role in reading but this is likely to vary with individual reading preferences and strategies

Bayesian pathway analysis over brain network mediators for survival data

Technological advancements in noninvasive imaging facilitate the construction of whole brain interconnected networks, known as brain connectivity. Existing approaches to analyze brain connectivity frequently disaggregate the entire network into a vector of unique edges or summary measures, leading to a substantial loss of information. Motivated by the need to explore the effect mechanism among genetic exposure, brain connectivity and time to disease onset, we propose an integrative Bayesian framework to model the effect pathway between each of these components while quantifying the mediating role of brain networks. To accommodate the biological architectures of brain connectivity constructed along white matter fiber tracts, we develop a structural modeling framework that includes a symmetric matrix-variate accelerated failure time model and a symmetric matrix response regression to characterize the effect paths. We further impose within-graph sparsity and between-graph shrinkage to identify informative network configurations and eliminate the interference of noisy components. Extensive simulations confirm the superiority of our method compared with existing alternatives. By applying the proposed method to the landmark Alzheimer's Disease Neuroimaging Initiative study, we obtain neurobiologically plausible insights that may inform future intervention strategies

What does semantic tiling of the cortex tell us about semantics?

Recent use of voxel-wise modeling in cognitive neuroscience suggests that semantic maps tile the cortex. Although this impressive research establishes distributed cortical areas active during the conceptual processing that underlies semantics, it tells us little about the nature of this processing. While mapping concepts between Marr's computational and implementation levels to support neural encoding and decoding, this approach ignores Marr's algorithmic level, central for understanding the mechanisms that implement cognition, in general, and conceptual processing, in particular. Following decades of research in cognitive science and neuroscience, what do we know so far about the representation and processing mechanisms that implement conceptual abilities? Most basically, much is known about the mechanisms associated with: (1) features and frame representations, (2) grounded, abstract, and linguistic representations, (3) knowledge-based inference, (4) concept composition, and (5) conceptual flexibility. Rather than explaining these fundamental representation and processing mechanisms, semantic tiles simply provide a trace of their activity over a relatively short time period within a specific learning context. Establishing the mechanisms that implement conceptual processing in the brain will require more than mapping it to cortical (and sub-cortical) activity, with process models from cognitive science likely to play central roles in specifying the intervening mechanisms. More generally, neuroscience will not achieve its basic goals until it establishes algorithmic-level mechanisms that contribute essential explanations to how the brain works, going beyond simply establishing the brain areas that respond to various task conditions

Semiparametric Bayes conditional graphical models for imaging genetics applications

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135205/1/sta4119_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135205/2/sta4119.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135205/3/sta4119-sup-0002-Supplementary2.pd