Science of the conscious mind

Human beings have direct access to their own mental states, but can only indirectly observe cosmic radiation and enzyme kinetics. Why then can we measure the temperature of far away galaxies and the activation constant of kinases to the third digit, yet we only gauge our happiness on a scale from 1 to 7? Here we propose a radical research paradigm shift to embrace the subjective conscious mind into the realm of objective empirical science. Key steps are the axiomatic acceptance of first-person experiences as scientific observables; the definition of a quantitative, reliable metric system based on natural language; and the careful distinction of subjective mental states (e.g., interpretation and intent) from physically measurable sensory and motor behaviors (input and output). Using this approach, we propose a series of reproducible experiments that may help define a still largely unexplored branch of science. We speculate that the development of this new discipline will be initially parallel to, and eventually converging with, neurobiology and physics.

Morphological homeostasis in cortical dendrites

Neurons have significant potential for the homeostatic regulation of a broad range of functional features, from gene expression to synaptic excitability. In this article, we show that dendritic morphology may also be under intrinsic homeostatic control. We present the results from a statistical analysis of a large collection of digitally reconstructed neurons, demonstrating that fluctuations in dendritic size in one given portion of a neuron are systematically counterbalanced by the remaining dendrites in the same cell. As a result, the total dendritic measure (e.g., number of branches, length, and surface area) of each neuron in a given morphological class is, on average, significantly less random than would be expected if trees (and their parts) were regulated independently during development. This observation is general across scales that range from gross basal/apical subdivisions to individual branches and bifurcations, and its statistical significance is robust among various brain regions, cell types, and experimental conditions. Given the pivotal dendritic role in signal integration, synaptic plasticity, and network connectivity, these findings add a dimension to the functional characterization of neuronal homeostasis.

A simple neural network model of the hippocampus suggesting its pathfinding role in episodic memory retrieval

The goal of this work is to extend the theoretical understanding of the relationship between hippocampal spatial and memory functions to the level of neurophysiological mechanisms underlying spatial navigation and episodic memory retrieval. The proposed unifying theory describes both phenomena within a unique framework, as based on one and the same pathfinding function of the hippocampus. We propose a mechanism of reconstruction of the context of experience involving a search for a nearly shortest path in the space of remembered contexts. To analyze this concept in detail, we define a simple connectionist model consistent with available rodent and human neurophysiological data. Numerical study of the model begins with the spatial domain as a simple analogy for more complex phenomena. It is demonstrated how a nearly shortest path is quickly found in a familiar environment. We prove numerically that associative learning during sharp waves can account for the necessary properties of hippocampal place cells. Computational study of the model is extended to other cognitive paradigms, with the main focus on episodic memory retrieval. We show that the ability to find a correct path may be vital for successful retrieval. The model robustly exhibits the pathfinding capacity within a wide range of several factors, including its memory load (up to 30,000 abstract contexts), the number of episodes that become associated with potential target contexts, and the level of dynamical noise. We offer several testable critical predictions in both spatial and memory domains to validate the theory. Our results suggest that (1) the pathfinding function of the hippocampus, in addition to its associative and memory indexing functions, may be vital for retrieval of certain episodic memories, and (2) the hippocampal spatial navigation function could be a precursor of its memory function.

Statistical determinants of dendritic morphology in hippocampal pyramidal neurons: A hidden Markov model

Dendritic structure is traditionally characterized by distributions and interrelations of morphometric parameters, such as Sholl-like plots of the number of branches versus dendritic path distance. However, how much of a given morphology is effectively captured by any statistical description is generally unknown. In this work, we assemble a small number of standard geometrical parameters measured from experimental data in a simple stochastic algorithm to describe the dendrograms of hippocampal pyramidal cells. The model, consistent with the hidden Markov framework, is feedforward, local, and causal. It relies on two "hidden" local variables: the expected number of terminal tips in a given subtree, and the current path distance from the soma. The algorithm generates dendrograms that statistically reproduce all morphological essentials of dendrites observed in real neurons, including the distributions of branching and termination points, branch lengths, membrane area, topological asymmetry, and (assuming passive membrane parameters within physiological range) electrotonic characteristics. Thus, this algorithm and the small number of its morphometric parameters constitute a remarkably complete description of the dendrograms of hippocampal pyramidal cells. Specifically, it is found that CA3 and CA1 basal dendrites and CA3 apical dendrites can each be described as homogeneous morphological classes. In contrast, the accurate generation of CA1 apical dendrites necessitates the separate sampling of two types of branches, main and oblique, suggesting their derivations from different developmental mechanisms (terminal and interstitial growth, respectively). We further offer a plausible biophysical interpretation of the model hidden variables, relating them to microtubules and other intracellular resources.

Fundamental Principles and Mechanisms of the Conscious Self

We start by assuming that the self is implemented in the brain as a functional unit, with a definite set of properties. We deduce the fundamental properties of the self from an analysis of neurological disorders and from introspection. We formulate a functionalist concept of the self based on these properties reduced to constraints. We use the formalism of schemas in our functionalist analysis, i.e. a symbolic level description of brain dynamics. We then reformulate the functionalist model at a connectionist level and address the emergent "context shifting" problem. We suggest how the model might be mapped onto the functional neuroanatomy of the brain, and how it could be used to give an account of a range of neurological disorders, including hippocampal amnesia, various forms of schizophrenia, multiple personality, autism, PTSD, hemineglect, and reversible anosognosia. Finally, we briefly discuss future perspectives and possible applications of computer implementations of the model.

The Conscious Self: Ontology, Epistemology and the Mirror Quest

Here we address the notion of the self as the subject of experience, in contrast with its other popular meanings of a state of self-awareness, a body image, or a narrative center. In this perspective, the main player is not consciousness per se (the famous "hard problem"), but its subject. We start by showing that the self as the subject of immediate self-awareness cannot be conceived as an illusion. Then we formulate axioms of the self-concept based on the framework that Chalmers initially developed for consciousness. Using a thought experiments (the "mirror quest"), we show that this framework may require corrections in order to accommodate the subject. We analyze several possibilities that lead to a novel doctrine of "subjective nonreductive functionalism". As an epistemological model, this position has further important implications for brain sciences.

Statistical morphological analysis of hippocampal principal neurons indicates cell-specific repulsion of dendrites from their own cell

Traditionally, the sources of guidance cues for dendritic outgrowth are mainly associated with external bodies (A) rather than with the same neuron from which dendrites originate (B). To quantify the relationship between factors A and B as determinants of the adult dendritic shape, the morphology of 83 intracellularly characterized, stained, completely reconstructed, and digitized principal neurons of the rat hippocampus was statistically analyzed using Bayesian optimization. It was found that the dominant directional preference (tropism) manifested in dendritic turns is to grow away from the soma rather than toward the incoming fibers or in any other fixed direction; therefore, B is predominant. Results are robust and consistent for all examined morphological classes (dentate gyrus granule cells, basal and apical trees of CA3 and CA1 pyramidal cells). In addition, computer remodeling of neurons based on the measured parameters produced virtual structures consistent with real morphologies, as confirmed by measurement of several global emergent parameters. Thus, the simple description of dendritic shape based on dendrites' tendency to grow straight, away from their own soma, and with additional random deflections, proves remarkably accurate and complete. Although based on adult neurons, these results suggest that dendritic guidance during development may be associated primarily with the host cell. This possibility challenges the traditional concept of dendritic guidance: in that hippocampal cells are densely packed and have highly overlapping dendritic fields, the somatodendritic repulsion must be cell specific. Plausible mechanisms involving extracellular effects of spikes are discussed, together with feasible experimental tests and predicted results.