Towards a Unified Theory of Neocortex: Laminar Cortical Circuits for Vision and Cognition

A key goal of computational neuroscience is to link brain mechanisms to behavioral functions. The present article describes recent progress towards explaining how laminar neocortical circuits give rise to biological intelligence. These circuits embody two new and revolutionary computational paradigms: Complementary Computing and Laminar Computing. Circuit properties include a novel synthesis of feedforward and feedback processing, of digital and analog processing, and of pre-attentive and attentive processing. This synthesis clarifies the appeal of Bayesian approaches but has a far greater predictive range that naturally extends to self-organizing processes. Examples from vision and cognition are summarized. A LAMINART architecture unifies properties of visual development, learning, perceptual grouping, attention, and 3D vision. A key modeling theme is that the mechanisms which enable development and learning to occur in a stable way imply properties of adult behavior. It is noted how higher-order attentional constraints can influence multiple cortical regions, and how spatial and object attention work together to learn view-invariant object categories. In particular, a form-fitting spatial attentional shroud can allow an emerging view-invariant object category to remain active while multiple view categories are associated with it during sequences of saccadic eye movements. Finally, the chapter summarizes recent work on the LIST PARSE model of cognitive information processing by the laminar circuits of prefrontal cortex. LIST PARSE models the short-term storage of event sequences in working memory, their unitization through learning into sequence, or list, chunks, and their read-out in planned sequential performance that is under volitional control. LIST PARSE provides a laminar embodiment of Item and Order working memories, also called Competitive Queuing models, that have been supported by both psychophysical and neurobiological data. These examples show how variations of a common laminar cortical design can embody properties of visual and cognitive intelligence that seem, at least on the surface, to be mechanistically unrelated.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Acetylcholine neuromodulation in normal and abnormal learning and memory: vigilance control in waking, sleep, autism, amnesia, and Alzheimer's disease

This article provides a unified mechanistic neural explanation of how learning, recognition, and cognition break down during Alzheimer's disease, medial temporal amnesia, and autism. It also clarifies whey there are often sleep disturbances during these disorders. A key mechanism is how acetylcholine modules vigilance control in cortical layer

Consciousness CLEARS the Mind

A full understanding of consciouness requires that we identify the brain processes from which conscious experiences emerge. What are these processes, and what is their utility in supporting successful adaptive behaviors? Adaptive Resonance Theory (ART) predicted a functional link between processes of Consciousness, Learning, Expectation, Attention, Resonance, and Synchrony (CLEARS), includes the prediction that "all conscious states are resonant states." This connection clarifies how brain dynamics enable a behaving individual to autonomously adapt in real time to a rapidly changing world. The present article reviews theoretical considerations that predicted these functional links, how they work, and some of the rapidly growing body of behavioral and brain data that have provided support for these predictions. The article also summarizes ART models that predict functional roles for identified cells in laminar thalamocortical circuits, including the six layered neocortical circuits and their interactions with specific primary and higher-order specific thalamic nuclei and nonspecific nuclei. These prediction include explanations of how slow perceptual learning can occur more frequently in superficial cortical layers. ART traces these properties to the existence of intracortical feedback loops, and to reset mechanisms whereby thalamocortical mismatches use circuits such as the one from specific thalamic nuclei to nonspecific thalamic nuclei and then to layer 4 of neocortical areas via layers 1-to-5-to-6-to-4.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Olfaction As The Paradigm For Perceptual Philosophy

Plato wrote that smell is of a half-formed nature and that not much can be said about it, and Kant identified smell as the most ungrateful and most dispensable of the senses. Because contemporary philosophers share this distaste for smell perception, olfaction is often dismissed or ignored in philosophical accounts of perception. Instead, contemporary philosophy of perception is based almost exclusively on visual perception. The goal of this dissertation is to show that this focus on a single modality distorts our understanding of what perception is. I am not the first to realize the potential of opening up perceptual philosophy to the non-visual modalities. Bill Lycan asked how the philosophy of perception would be different if smell had been taken as a paradigm rather than vision. (Lycan 2000). In this dissertation, I will try to answer this question. My analysis will show that philosophy of perception would be very different, were it based on olfaction. Many of the most basic concepts of philosophy of perception are based on peculiarities of visual perception that do not generalize to other modalities

Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review

Predictive coding and stochastic resonance as fundamental principles of auditory phantom perception

Mechanistic insight is achieved only when experiments are employed to test formal or computational models. Furthermore, in analogy to lesion studies, phantom perception may serve as a vehicle to understand the fundamental processing principles underlying healthy auditory perception. With a special focus on tinnitus—as the prime example of auditory phantom perception—we review recent work at the intersection of artificial intelligence, psychology and neuroscience. In particular, we discuss why everyone with tinnitus suffers from (at least hidden) hearing loss, but not everyone with hearing loss suffers from tinnitus. We argue that intrinsic neural noise is generated and amplified along the auditory pathway as a compensatory mechanism to restore normal hearing based on adaptive stochastic resonance. The neural noise increase can then be misinterpreted as auditory input and perceived as tinnitus. This mechanism can be formalized in the Bayesian brain framework, where the percept (posterior) assimilates a prior prediction (brain’s expectations) and likelihood (bottom-up neural signal). A higher mean and lower variance (i.e. enhanced precision) of the likelihood shifts the posterior, evincing a misinterpretation of sensory evidence, which may be further confounded by plastic changes in the brain that underwrite prior predictions. Hence, two fundamental processing principles provide the most explanatory power for the emergence of auditory phantom perceptions: predictive coding as a top-down and adaptive stochastic resonance as a complementary bottom-up mechanism. We conclude that both principles also play a crucial role in healthy auditory perception. Finally, in the context of neuroscience-inspired artificial intelligence, both processing principles may serve to improve contemporary machine learning techniques

The Neural Correlates of Consciousness - An Update

This review examines recent advances in the study of brain correlates of consciousness. First, we briefly discuss some useful distinctions between consciousness and other brain functions. We then examine what has been learned by studying global changes in the level of consciousness, such as sleep, anesthesia, and seizures. Next we consider some of the most common paradigms used to study the neural correlates for specific conscious percepts and examine what recent findings say about the role of different brain regions in giving rise to consciousness for that percept. Then we discuss dynamic aspects of neural activity, such as sustained versus phasic activity, feedforward versus reentrant activity, and the role of neural synchronization. Finally, we briefly consider how a theoretical analysis of the fundamental properties of consciousness can usefully complement neurobiological studies

Emulation as an Integrating Principle for Cognition

Emulations, defined as ongoing internal representations of potential actions and the futures those actions are expected to produce, play a critical role in directing human bodily activities. Studies of gross motor behavior, perception, allocation of attention, response to errors, interoception, and homeostatic activities, and higher cognitive reasoning suggest that the proper execution of all these functions relies on emulations. Further evidence supports the notion that reinforcement learning in humans is aimed at updating emulations, and that action selection occurs via the advancement of preferred emulations toward realization of their action and environmental prediction. Emulations are hypothesized to exist as distributed active networks of neurons in cortical and sub-cortical structures. This manuscript ties together previously unrelated theories of the role of prediction in different aspects of human information processing to create an integrated framework for cognition

Perception of Human Movement Based on Modular Movement Primitives

People can identify and understand human movement from very degraded visual information without effort. A few dots representing the position of the joints are enough to induce a vivid and stable percept of the underlying movement. Due to this ability, the realistic animation of 3D characters requires great skill. Studying the constituents of movement that looks natural would not only help these artists, but also bring better understanding of the underlying information processing in the brain. Analogous to the hurdles in animation, the efforts of roboticists reflect the complexity of motion production: controlling the many degrees of freedom of a body requires time-consuming computations. Modularity is one strategy to address this problem: Complex movement can be decomposed into simple primitives. A few primitives can conversely be used to compose a large number of movements. Many types of movement primitives (MPs) have been proposed on different levels of information processing hierarchy in the brain. MPs have mostly been proposed for movement production. Yet, modularity based on primitives might similarly enable robust movement perception. For my thesis, I have conducted perceptual experiments based on the assumption of a shared representation of perception and action based on MPs. The three different types of MPs I have investigated are temporal MPs (TMP), dynamical MPs (DMP), and coupled Gaussian process dynamical models (cGPDM). The MP-models have been trained on natural movements to generate new movements. I then perceptually validated these artificial movements in different psychophysical experiments. In all experiments I used a two-alternative forced choice paradigm, in which human observers were presented a movement based on motion-capturing data, and one generated by an MP-model. They were then asked to chose the movement which they perceived as more natural. In the first experiment I investigated walking movements, and found that, in line with previous results, faithful representation of movement dynamics is more important than good reconstruction of pose. In the second experiment I investigated the role of prediction in perception using reaching movements. Here, I found that perceived naturalness of the predictions is similar to the perceived naturalness of movements itself obtained in the first experiment. I have found that MP models are able to produce movement that looks natural, with the TMP achieving the highest perceptual scores as well highest predictiveness of perceived naturalness among the three model classes, suggesting their suitability for a shared representation of perception and action