Theorizing how the brain encodes consciousness based on negentropic entanglement.

The physicality of subjectivity is explained through a theoretical conceptualization of guidance waves informing meaning in negentropically entangled non-electrolytic brain regions. Subjectivity manifests its influence at the microscopic scale of matter originating from de Broglie 'hidden' thermodynamics as action of guidance waves. The preconscious experienceability of subjectivity is associated with a nested hierarchy of microprocesses, which are actualized as a continuum of patterns of discrete atomic microfeels (or "qualia"). The mechanism is suggested to be through negentropic entanglement of hierarchical thermodynamic transfer of information as thermo-qubits originating from nonpolarized regions of actin-binding proteinaceous structures of nonsynaptic spines. The resultant continuous stream of intrinsic information entails a negentropic action (or experiential flow of thermo-quantum internal energy that results in a negentropic force) which is encoded through the non-zero real component of the mean approximation of the negentropic force as a 'consciousness code'. Consciousness consisting of two major subprocesses: (1) preconscious experienceability and (2) conscious experience. Both are encapsulated by nonreductive physicalism and panexperiential materialism. The subprocess (1) governing "subjectivity" carries many microprocesses leading to the actualization of discrete atomic microfeels by the 'consciousness code'. These atomic microfeels constitute internal energy that results in the transfer intrinsic information in terms of thermo-qubits. These thermo-qubits are realized as thermal entropy and sensed by subprocess (2) governing "self-awareness" in conscious experience.

Erratum: Solitonic conduction of electrotonic signals in neuronal branchlets with polarized microstructure.

A correction to this article has been published and is linked from the HTML version of this paper. The error has been fixed in the paper.

Induced mitochondrial membrane potential for modeling solitonic conduction of electrotonic signals.

A cable model that includes polarization-induced capacitive current is derived for modeling the solitonic conduction of electrotonic potentials in neuronal branchlets with microstructure containing endoplasmic membranes. A solution of the nonlinear cable equation modified for fissured intracellular medium with a source term representing charge 'soakage' is used to show how intracellular capacitive effects of bound electrical charges within mitochondrial membranes can influence electrotonic signals expressed as solitary waves. The elastic collision resulting from a head-on collision of two solitary waves results in localized and non-dispersing electrical solitons created by the nonlinearity of the source term. It has been shown that solitons in neurons with mitochondrial membrane and quasi-electrostatic interactions of charges held by the microstructure (i.e., charge 'soakage') have a slower velocity of propagation compared with solitons in neurons with microstructure, but without endoplasmic membranes. When the equilibrium potential is a small deviation from rest, the nonohmic conductance acts as a leaky channel and the solitons are small compared when the equilibrium potential is large and the outer mitochondrial membrane acts as an amplifier, boosting the amplitude of the endogenously generated solitons. These findings demonstrate a functional role of quasi-electrostatic interactions of bound electrical charges held by microstructure for sustaining solitons with robust self-regulation in their amplitude through changes in the mitochondrial membrane equilibrium potential. The implication of our results indicate that a phenomenological description of ionic current can be successfully modeled with displacement current in Maxwell's equations as a conduction process involving quasi-electrostatic interactions without the inclusion of diffusive current. This is the first study in which solitonic conduction of electrotonic potentials are generated by polarization-induced capacitive current in microstructure and nonohmic mitochondrial membrane current.

Nonsynaptic plasticity model of long-term memory engrams.

Using steady-state electrical properties of non-ohmic dendrite based on cable theory, we derive electrotonic potentials that do not change over time and are localized in space. We hypothesize that clusters of such stationary, local and permanent pulses are the electrical signatures of enduring memories which are imprinted through nonsynaptic plasticity, encoded through epigenetic mechanisms, and decoded through electrotonic processing. We further hypothesize how retrieval of an engram is made possible by integration of these permanently imprinted standing pulses in a neural circuit through neurotransmission in the extracellular space as part of conscious recall that acts as a guiding template in the reconsolidation of long-term memories through novelty characterized by uncertainty that arises when new fragments of memories reinstate an engram by way of nonsynaptic plasticity that permits its destabilization. Collectively, these findings seem to reinforce this hypothesis that electrotonic processing in non-ohmic dendrites yield insights into permanent electrical signatures that could reflect upon enduring memories as fragments of long-term memory engrams.

Solitonic conduction of electrotonic signals in neuronal branchlets with polarized microstructure.

A model of solitonic conduction in neuronal branchlets with microstructure is presented. The application of cable theory to neurons with microstructure results in a nonlinear cable equation that is solved using a direct method to obtain analytical approximations of traveling wave solutions. It is shown that a linear superposition of two oppositely directed traveling waves demonstrate solitonic interaction: colliding waves can penetrate through each other, and continue fully intact as the exact pulses that entered the collision. These findings indicate that microstructure when polarized can sustain solitary waves that propagate at a constant velocity without attenuation or distortion in the absence of synaptic transmission. Solitonic conduction in a neuronal branchlet arising from polarizability of its microstructure is a novel signaling mode of electrotonic signals in thin processes (<0.5 µm diameter).

A fuzzy integral method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification across multiple subjects.

The huge number of voxels in fMRI over time poses a major challenge to for effective analysis. Fast, accurate, and reliable classifiers are required for estimating the decoding accuracy of brain activities. Although machine-learning classifiers seem promising, individual classifiers have their own limitations. To address this limitation, the present paper proposes a method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification for application across multiple subjects. Similarly, the fuzzy integral (FI) approach has been employed as an efficient tool for combining different classifiers. The FI approach led to the development of a classifiers ensemble technique that performs better than any of the single classifier by reducing the misclassification, the bias, and the variance. The proposed method successfully classified the different cognitive states for multiple subjects with high accuracy of classification. Comparison of the performance improvement, while applying ensemble neural networks method, vs. that of the individual neural network strongly points toward the usefulness of the proposed method.

A hybrid method for classifying cognitive states from fMRI data.

Functional magnetic resonance imaging (fMRI) makes it possible to detect brain activities in order to elucidate cognitive-states. The complex nature of fMRI data requires under-standing of the analyses applied to produce possible avenues for developing models of cognitive state classification and improving brain activity prediction. While many models of classification task of fMRI data analysis have been developed, in this paper, we present a novel hybrid technique through combining the best attributes of genetic algorithms (GAs) and ensemble decision tree technique that consistently outperforms all other methods which are being used for cognitive-state classification. Specifically, this paper illustrates the combined effort of decision-trees ensemble and GAs for feature selection through an extensive simulation study and discusses the classification performance with respect to fMRI data. We have shown that our proposed method exhibits significant reduction of the number of features with clear edge classification accuracy over ensemble of decision-trees.

Intracellular capacitive effects of polarized proteins in dendrites.

Passive dendrites become active as a result of electrostatic interactions by dielectric polarization in proteins in a segment of a dendrite. The resultant nonlinear cable equation for a cylindrical volume representation of a dendritic segment is derived from Maxwell's equations under assumptions: (i) the electric field is restricted longitudinally along the cable length; (ii) extracellular isopotentiality; (iii) quasi-electrostatic conditions; (iv) isotropic membrane and homogeneous medium with constant conductivity; and (v) protein polarization contributes to intracellular capacitive effects through a well defined nonlinear capacity-voltage characteristic; (vi) intracellular resistance and capacitance in parallel are connected to the membrane in series. Under the above hypotheses, traveling wave solutions of the cable equation are obtained as propagating fronts of electrical excitation associated with capacitive charge-equalization and dispersion of continuous polarization charge densities in an Ohmic cable. The intracellular capacitative effects of polarized proteins in dendrites contribute to the conduction process.

Associable representations as field of influence for dynamic cognitive processes.

In earlier models, synaptic plasticity forms the basis for cellular signaling underlying learning and memory. However, synaptic computation of learning and memory in the brain remains controversial. In this paper, we discuss ways in which synaptic plasticity remodels subcellular networks by deflecting trajectories in neuronal state-space as regulating patterns for the synthesis of dynamic continuity that form cognitive networks of associable representations through endogenous dendritic coding to consolidate memory.