Afference copy as a quantitative neurophysiological model for consciousness.

Consciousness is a topic of considerable human curiosity with a long history of philosophical analysis and debate. We consider there is nothing particularly complicated about consciousness when viewed as a necessary process of the vertebrate nervous system. Here, we propose a physiological "explanatory gap" is created during each present moment by the temporal requirements of neuronal activity. The gap extends from the time exteroceptive and proprioceptive stimuli activate the nervous system until they emerge into consciousness. During this "moment", it is impossible for an organism to have any conscious knowledge of the ongoing evolution of its environment. In our schematic model, a mechanism of "afference copy" is employed to bridge the explanatory gap with consciously experienced percepts. These percepts are fabricated from the conjunction of the cumulative memory of previous relevant experience and the given stimuli. They are structured to provide the best possible prediction of the expected content of subjective conscious experience likely to occur during the period of the gap. The model is based on the proposition that the neural circuitry necessary to support consciousness is a product of sub/preconscious reflexive learning and recall processes. Based on a review of various psychological and neurophysiological findings, we develop a framework which contextualizes the model and briefly discuss further implications.

Python as a federation tool for GENESIS 3.0.

The GENESIS simulation platform was one of the first broad-scale modeling systems in computational biology to encourage modelers to develop and share model features and components. Supported by a large developer community, it participated in innovative simulator technologies such as benchmarking, parallelization, and declarative model specification and was the first neural simulator to define bindings for the Python scripting language. An important feature of the latest version of GENESIS is that it decomposes into self-contained software components complying with the Computational Biology Initiative federated software architecture. This architecture allows separate scripting bindings to be defined for different necessary components of the simulator, e.g., the mathematical solvers and graphical user interface. Python is a scripting language that provides rich sets of freely available open source libraries. With clean dynamic object-oriented designs, they produce highly readable code and are widely employed in specialized areas of software component integration. We employ a simplified wrapper and interface generator to examine an application programming interface and make it available to a given scripting language. This allows independent software components to be 'glued' together and connected to external libraries and applications from user-defined Python or Perl scripts. We illustrate our approach with three examples of Python scripting. (1) Generate and run a simple single-compartment model neuron connected to a stand-alone mathematical solver. (2) Interface a mathematical solver with GENESIS 3.0 to explore a neuron morphology from either an interactive command-line or graphical user interface. (3) Apply scripting bindings to connect the GENESIS 3.0 simulator to external graphical libraries and an open source three dimensional content creation suite that supports visualization of models based on electron microscopy and their conversion to computational models. Employed in this way, the stand-alone software components of the GENESIS 3.0 simulator provide a framework for progressive federated software development in computational neuroscience.

A federated design for a neurobiological simulation engine: the CBI federated software architecture.

Simulator interoperability and extensibility has become a growing requirement in computational biology. To address this, we have developed a federated software architecture. It is federated by its union of independent disparate systems under a single cohesive view, provides interoperability through its capability to communicate, execute programs, or transfer data among different independent applications, and supports extensibility by enabling simulator expansion or enhancement without the need for major changes to system infrastructure. Historically, simulator interoperability has relied on development of declarative markup languages such as the neuron modeling language NeuroML, while simulator extension typically occurred through modification of existing functionality. The software architecture we describe here allows for both these approaches. However, it is designed to support alternative paradigms of interoperability and extensibility through the provision of logical relationships and defined application programming interfaces. They allow any appropriately configured component or software application to be incorporated into a simulator. The architecture defines independent functional modules that run stand-alone. They are arranged in logical layers that naturally correspond to the occurrence of high-level data (biological concepts) versus low-level data (numerical values) and distinguish data from control functions. The modular nature of the architecture and its independence from a given technology facilitates communication about similar concepts and functions for both users and developers. It provides several advantages for multiple independent contributions to software development. Importantly, these include: (1) Reduction in complexity of individual simulator components when compared to the complexity of a complete simulator, (2) Documentation of individual components in terms of their inputs and outputs, (3) Easy removal or replacement of unnecessary or obsoleted components, (4) Stand-alone testing of components, and (5) Clear delineation of the development scope of new components.

Dendritic excitability modulates dendritic information processing in a purkinje cell model.

Using an electrophysiological compartmental model of a Purkinje cell we quantified the contribution of individual active dendritic currents to processing of synaptic activity from granule cells. We used mutual information as a measure to quantify the information from the total excitatory input current (I(Glu)) encoded in each dendritic current. In this context, each active current was considered an information channel. Our analyses showed that most of the information was encoded by the calcium (I(CaP)) and calcium activated potassium (I(Kc)) currents. Mutual information between I(Glu) and I(CaP) and I(Kc) was sensitive to different levels of excitatory and inhibitory synaptic activity that, at the same time, resulted in the same firing rate at the soma. Since dendritic excitability could be a mechanism to regulate information processing in neurons we quantified the changes in mutual information between I(Glu) and all Purkinje cell currents as a function of the density of dendritic Ca (g(CaP)) and Kca (g(Kc)) conductances. We extended our analysis to determine the window of temporal integration of I(Glu) by I(CaP) and I(Kc) as a function of channel density and synaptic activity. The window of information integration has a stronger dependence on increasing values of g(Kc) than on g(CaP), but at high levels of synaptic stimulation information integration is reduced to a few milliseconds. Overall, our results show that different dendritic conductances differentially encode synaptic activity and that dendritic excitability and the level of synaptic activity regulate the flow of information in dendrites.

Mathematical analysis of locomotor behavior by mice in a radial maze.

We investigated the effects of beta-estradiol on the locomotor behavior of female mice in a radial maze. Data comprising the total distance traveled during each arm entry were obtained from video records of six consecutive daily recording sessions. Distributions of these data were bimodal for both ovariectomized control and beta-estradiol-treated ovariectomized subjects. Data were fit with the sum of two gamma probability distributions. Three parameters of the analytic fits were useful for quantifying the effect of beta-estradiol on locomotor behavior: (i) the sampling distance (median of the total distance traveled during each arm entry in the short-distance peak of a bimodal distribution), (ii) the committed distance (median of the total per-arm-entry distance traveled in the long-distance peak), and (iii) the partition distance (distance represented by the minimum between the two peaks). Analysis showed that for sampling-distance arm entries beta-estradiol typically had little if any significant effect on female locomotor behavior, whereas it significantly increased the total distance traveled during committed-distance arm entries on the first 2 days of exposure to the empty maze. beta-Estradiol also increased the ability of females to discriminate between empty maze arms and arms that contained intact or castrated male mice and partially prevented loss of this capacity after removal of the males.

Control of neuronal discharge timing by afferent fiber number and the temporal pattern of afferent impulses.

We employ computer simulations to explore the effect of different temporal patterns of afferent impulses on the evoked discharge of a model cerebellar Purkinje cell. We show that the frequency and temporal correlation of impulses across afferent fibers determines which of four regimes of discharge activity is evoked. In the uncorrelated, here Poissonian, case, (i) cell discharge is determined by the total stimulation rate and temporal patterns of discharge are the same for different combinations of afferent fiber number and mean impulse rate per fiber giving the same total stimulation. Alternatively, if temporal correlations are present in the stimulus, (ii) for stimulation frequencies of 4 to at least 64 Hz there is a narrow range of afferent fiber number for which every stimulus pulse (composed of a single impulse on each afferent fiber) evokes a single action potential. In this case cell discharge is frequency locked to the stimulus with a concomitant reduction in discharge variability. (iii) For lower fiber numbers and thus discharge frequencies lower than the locking frequency, the variability of cell discharge is typically independent of afferent impulse timing, whereas, (iv) at higher fiber numbers and thus higher discharge frequencies, the reverse is true. We conclude that in case (iii) the cell acts as an integrator and discharge is determined by the stimulation rate, whereas in case (iv) the cell acts as a coincidence detector and the timing of discharge is determined by the temporal pattern of afferent stimulation. We discuss our results in terms of their significance for neuronal activity at the network level and suggest that the reported effects of varying stimulus timing and afferent convergence can be expected to obtain also with other principal cell types within the central nervous system.

Estimating the temporal interval entropy of neuronal discharge.

To better understand the role of timing in the function of the nervous system, we have developed a methodology that allows the entropy of neuronal discharge activity to be estimated from a spike train record when it may be assumed that successive interspike intervals are temporally uncorrelated. The so-called interval entropy obtained by this methodology is based on an implicit enumeration of all possible spike trains that are statistically indistinguishable from a given spike train. The interval entropy is calculated from an analytic distribution whose parameters are obtained by maximum likelihood estimation from the interval probability distribution associated with a given spike train. We show that this approach reveals features of neuronal discharge not seen with two alternative methods of entropy estimation. The methodology allows for validation of the obtained data models by calculation of confidence intervals for the parameters of the analytic distribution and the testing of the significance of the fit between the observed and analytic interval distributions by means of Kolmogorov-Smirnov and Anderson-Darling statistics. The method is demonstrated by analysis of two different data sets: simulated spike trains evoked by either Poissonian or near-synchronous pulsed activation of a model cerebellar Purkinje neuron and spike trains obtained by extracellular recording from spontaneously discharging cultured rat hippocampal neurons.