Not just a bad metaphor, but a little piece of a big bad metaphor.

Besides failing for the reasons Brette gives, codes fail to help us understand brain function because codes imply algorithms that compute outputs without reference to the signals' meanings. Algorithms cannot be found in the brain, only manipulations that operate on meaningful signals and that cannot be described as computations, that is, sequences of predefined operations.

γ-Aminobutyric Acid Type A Receptor Potentiation Inhibits Learning in a Computational Network Model.

Background: Propofol produces memory impairment at concentrations well below those abolishing consciousness. Episodic memory, mediated by the hippocampus, is most sensitive. Two potentially overlapping scenarios may explain how γ-aminobutyric acid receptor type A (GABAA) potentiation by propofol disrupts episodic memory-the first mediated by shifting the balance from excitation to inhibition while the second involves disruption of rhythmic oscillations.

Neuronal spike train entropy estimation by history clustering.

Neurons send signals to each other by means of sequences of action potentials (spikes). Ignoring variations in spike amplitude and shape that are probably not meaningful to a receiving cell, the information content, or entropy of the signal depends on only the timing of action potentials, and because there is no external clock, only the interspike intervals, and not the absolute spike times, are significant. Estimating spike train entropy is a difficult task, particularly with small data sets, and many methods of entropy estimation have been proposed.

Network model of top-down influences on local gain and contextual interactions in visual cortex.

The visual system uses continuity as a cue for grouping oriented line segments that define object boundaries in complex visual scenes. Many studies support the idea that long-range intrinsic horizontal connections in early visual cortex contribute to this grouping. Top-down influences in primary visual cortex (V1) play an important role in the processes of contour integration and perceptual saliency, with contour-related responses being task dependent.

γ-Aminobutyric acid receptor type A receptor potentiation reduces firing of neuronal assemblies in a computational cortical model.

Background: The understanding of how general anesthetics act on individual cells and on global brain function has increased significantly during the last decade. What remains poorly understood is how anesthetics act at intermediate scales. Several major theories emphasize the importance of neuronal groups, sets of strongly connected neurons that fire in a time-locked fashion, in all aspects of brain function, particularly as a necessary substrate of consciousness.

Quantitative tools for examining the vocalizations of juvenile songbirds.

The singing of juvenile songbirds is highly variable and not well stereotyped, a feature that makes it difficult to analyze with existing computational techniques. We present here a method suitable for analyzing such vocalizations, windowed spectral pattern recognition (WSPR). Rather than performing pairwise sample comparisons, WSPR measures the typicality of a sample against a large sample set.

Quantitative descriptions of generalized arousal, an elementary function of the vertebrate brain.

We review a concept of the most primitive, fundamental function of the vertebrate CNS, generalized arousal (GA). Three independent lines of evidence indicate the existence of GA: statistical, genetic, and mechanistic. Here we ask, is this concept amenable to quantitative analysis? Answering in the affirmative, four quantitative approaches have proven useful: (i) factor analysis, (ii) information theory, (iii) deterministic chaos, and (iv) application of a Gaussian equation.

A continuous entropy rate estimator for spike trains using a K-means-based context tree.

Entropy rate quantifies the change of information of a stochastic process (Cover & Thomas, 2006). For decades, the temporal dynamics of spike trains generated by neurons has been studied as a stochastic process (Barbieri, Quirk, Frank, Wilson, & Brown, 2001; Brown, Frank, Tang, Quirk, & Wilson, 1998; Kass & Ventura, 2001; Metzner, Koch, Wessel, & Gabbiani, 1998; Zhang, Ginzburg, McNaughton, & Sejnowski, 1998). We propose here to estimate the entropy rate of a spike train from an inhomogeneous hidden Markov model of the spike intervals.

Modelling criteria: Not just for robots.

Webb's scheme for classifying behavioral models is applicable to a wide range of theories and simulations, nonrobotic as well as robotic. It is suggested that a meta-analysis of existing models, characterized according to the proposed scheme, could identify regions of the seven-dimensional modelling space that are particularly likely to lead to new insights in understanding behavior.

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.

Theories and measures of consciousness: an extended framework.

A recent theoretical emphasis on complex interactions within neural systems underlying consciousness has been accompanied by proposals for the quantitative characterization of these interactions. In this article, we distinguish key aspects of consciousness that are amenable to quantitative measurement from those that are not. We carry out a formal analysis of the strengths and limitations of three quantitative measures of dynamical complexity in the neural systems underlying consciousness: neural complexity, information integration, and causal density.

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.

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.

The composite neuron: a realistic one-compartment Purkinje cell model suitable for large-scale neuronal network simulations.

We present a simple method for the realistic description of neurons that is well suited to the development of large-scale neuronal network models where the interactions within and between neural circuits are the object of study rather than the details of dendritic signal propagation in individual cells. Referred to as the composite approach, it combines in a one-compartment model elements of both the leaky integrator cell and the conductance-based formalism of Hodgkin and Huxley (1952). Composite models treat the cell membrane as an equivalent circuit that contains ligand-gated synaptic, voltage-gated, and voltage- and concentration-dependent conductances.

Value-dependent selection in the brain: simulation in a synthetic neural model.

Many forms of learning depend on the ability of an organism to sense and react to the adaptive value of its behavior. Such value, if reflected in the activity of specific neural structures (neural value systems), can selectively increase the probability of adaptive behaviors by modulating synaptic changes in the circuits relevant to those behaviors. Neuromodulatory systems in the brain are well suited to carry out this process since they respond to evolutionarily important cues (innate value), broadcast their responses to widely distributed areas of the brain through diffuse projections, and release substances that can modulate changes in synaptic strength.

Behaviorally based modeling and computational approaches to neuroscience.

The almost incredible advances that have recently occurred in the power of computers available to scientists in all disciplines have encouraged an explosion of neural network and behavioral models. Some of these have been constrained more by the imagination of the programmer than by rude biological facts. Their relevance for the experimental neuroscientist thus varies from case to case.

Synthetic neural modeling applied to a real-world artifact.

We describe the general design, operating principles, and performance of a neurally organized, multiply adaptive device (NOMAD) under control of a nervous system simulated in a computer. The complete system, Darwin IV, is the latest in a series of models based on the theory of neuronal group selection, which postulates that adaptive behavior is the result of selection in somatic time among synaptic populations. The simulated brain of Darwin IV includes visual and motor areas that are connected with NOMAD by telemetry.

The NO hypothesis: possible effects of a short-lived, rapidly diffusible signal in the development and function of the nervous system.

Several observations suggest that the Ca2(+)-dependent postsynaptic release of nitric oxide (NO) may be important in the formation and function of the vertebrate nervous system. We explore here the hypothesis that the release of NO and its subsequent diffusion may be critically related to three aspects of nervous system function: (i) synaptic plasticity and long-term potentiation in certain regions of the adult nervous system, (ii) the control of cerebral blood flow in such regions, and (iii) the establishment and activity-dependent refinement of axonal projections during the later stages of development. In this paper, we detail and analyze the basic assumptions underlying this NO hypothesis and describe a computer simulation of a minimal version of the hypothesis.

Reentrant signaling among simulated neuronal groups leads to coherency in their oscillatory activity.

Recent experiments have revealed tightly synchronized oscillatory discharges in local assemblies of cortical neurons as well as phase coherency of oscillations at distant cortical sites. These findings are consistent with the theory of neuronal group selection, a population theory of brain function that is based on the properties of local groups of neurons. A set of computer simulations shows that cooperative interactions within and among neuronal groups can generate the observed phenomena.

Three-dimensional structure of favin: saccharide binding-cyclic permutation in leguminous lectins.

The three-dimensional structure of favin, the glucose- and mannose-binding lectin of Vicia faba (vetch, broad bean), has been determined at a resolution of 2.8 angstroms by molecular replacement. The crystals contain specifically bound glucose and provide the first high-resolution view of specific saccharide binding in a leguminous lectin.

Three-dimensional structure of beta 2-microglobulin.

The three-dimensional structure of beta 2-microglobulin, the light chain of the major histocompatibility complex class I antigens, has been determined by x-ray crystallography. An electron density map of the bovine protein was calculated at a nominal resolution of 2.9 A by using the methods of multiple isomorphous replacement and electron density modification refinement.

Selective networks and recognition automata.

The results we have presented demonstrate that a network based on a selective principle can function in the absence of forced learning or an a priori program to give recognition, classification, generalization, and association. While Darwin II is not a model of any actual nervous system, it does set out to solve one of the same problems that evolution had to solve--the need to form categories in a bottom-up manner from information in the environment, without incorporating the assumptions of any particular observer. The key features of the model that make this possible are (1) Darwin II incorporates selective networks whose initial specificities enable them to respond without instruction to unfamiliar stimuli; (2) degeneracy provides multiple possibilities of response to any one stimulus, at the same time providing functional redundancy against component failure; (3) the output of Darwin II is a pattern of response, making use of the simultaneous responses of multiple degenerate groups to avoid the need for very high specificity and the combinatorial disaster that would imply; (4) reentry within individual networks vitiates the limitations described by Minsky and Papert for a class of perceptual automata lacking such connections; and (5) reentry between intercommunicating networks with different functions gives rise to new functions, such as association, that either one alone could not display.

Selective networks capable of representative transformations, limited generalizations, and associative memory.

Two parallel sets of selective networks composed of intercommunicating neuron-like elements have been connected to produce a new kind of automaton capable of limited recognition of two-dimensional patterns. Salient features of this automaton are (i) preestablished unchanging connectivity, (ii) preassigned connection strengths that are selectively altered according to experience, (iii) local feature detection in one network with simultaneous global feature correlation in the other, and (iv) reentrant interactions between the two networks to generate a new function, associative memory. No forced learning, explicit semantic rules, or a priori instructions are used.