On a Simple General Principle of Brain Organization.

A possible framework to characterize nervous system dynamics and its organization in conscious and unconscious states is introduced, derived from a high level perspective on the coordinated activity of brain cell ensembles. Some questions are best addressable in a global framework and here we build on past observations about the structure of configurations of brain networks in conscious and unconscious states and about neurophysiological results. Aiming to bind some results together into some sort of coherence with a central theme, the scenario that emerges underscores the crucial importance of the creation and dissipation of energy gradients in brain cellular ensembles resulting in maximization of the configurations in the functional connectivity among those networks that favor conscious awareness and healthy conditions. These considerations are then applied to indicate approaches that can be used to improve neuropathological syndromes.

An integration-to-bound model of decision-making that accounts for the spectral properties of neural data.

Integration-to-bound models are among the most widely used models of perceptual decision-making due to their simplicity and power in accounting for behavioral and neurophysiological data. They involve temporal integration over an input signal ("evidence") plus Gaussian white noise. However, brain data shows that noise in the brain is long-term correlated, with a spectral density of the form 1/fα (with typically 1 < α < 2), also known as pink noise or '1/f' noise. Surprisingly, the adequacy of the spectral properties of drift-diffusion models to electrophysiological data has received little attention in the literature. Here we propose a model of accumulation of evidence for decision-making that takes into consideration the spectral properties of brain signals. We develop a generalization of the leaky stochastic accumulator model using a Langevin equation whose non-linear noise term allows for varying levels of autocorrelation in the time course of the decision variable. We derive this equation directly from magnetoencephalographic data recorded while subjects performed a spontaneous movement-initiation task. We then propose a nonlinear model of accumulation of evidence that accounts for the '1/f' spectral properties of brain signals, and the observed variability in the power spectral properties of brain signals. Furthermore, our model outperforms the standard drift-diffusion model at approximating the empirical waiting time distribution.

The Efficient Coding of Speech: Cross-Linguistic Differences.

Neural coding in the auditory system has been shown to obey the principle of efficient neural coding. The statistical properties of speech appear to be particularly well matched to the auditory neural code. However, only English has so far been analyzed from an efficient coding perspective. It thus remains unknown whether such an approach is able to capture differences between the sound patterns of different languages. Here, we use independent component analysis to derive information theoretically optimal, non-redundant codes (filter populations) for seven typologically distinct languages (Dutch, English, Japanese, Marathi, Polish, Spanish and Turkish) and relate the statistical properties of these filter populations to documented differences in the speech rhythms (Analysis 1) and consonant inventories (Analysis 2) of these languages. We show that consonant class membership plays a particularly important role in shaping the statistical structure of speech in different languages, suggesting that acoustic transience, a property that discriminates consonant classes from one another, is highly relevant for efficient coding.

The statistical signature of morphosyntax: a study of Hungarian and Italian infant-directed speech.

Does statistical learning (Saffran, Aslin, & Newport, 1996) offer a universal segmentation strategy for young language learners? Previous studies on large corpora of English and structurally similar languages have shown that statistical segmentation can be an effective strategy. However, many of the world's languages have richer morphological systems, with sometimes several affixes attached to a stem (e.g. Hungarian: iskoláinkban: iskolá-i-nk-ban school.pl.poss1pl.inessive 'in our schools'). In these languages, word boundaries and morpheme boundaries do not coincide. Does the internal structure of words affect segmentation? What word forms does segmentation yield in morphologically rich languages: complex word forms or separate stems and affixes? The present paper answers these questions by exploring different segmentation algorithms in infant-directed speech corpora from two typologically and structurally different languages, Hungarian and Italian. The results suggest that the morphological and syntactic type of a language has an impact on statistical segmentation, with different strategies working best in different languages. Specifically, the direction of segmentation seems to be sensitive to the affixation order of a language. Thus, backward probabilities are more effective in Hungarian, a heavily suffixing language, whereas forward probabilities are more informative in Italian, which has fewer suffixes and a large number of phrase-initial function words. The consequences of these findings for potential segmentation and word learning strategies are discussed.

The Standard Genetic Code and its relation to mutational pressure: robustness and equilibrium criteria

Under the assumption of even point mutation pressure on the DNA strand, rates for transitions from one amino acid into another were assessed. Nearly 25por ciento of all mutations were silent. About 48por ciento of the mutations from a given amino acid stream either into the same amino acid or into an amino acid of the same class. These results suggest a great stability of the Standard Genetic Code respect to mutation load. Concepts from chemical equilibrium theory are applicable into this case provided that mutation rate constants are given. It was obtained that unequal synonymic codon usage may lead to changes in the equilibrium concentrations. Data from real biological species showed that several amino acids are close to the respective equilibrium concentration. However in all the cases the concentration of leucine nearly doubled its equilibrium concentration, whereas for the stop command (Term) it was about 10 times lower. The overall distance from equilibrium for a set of species suggests that eukaryotes are closer to equilibrium than prokaryotes, and the HIV virus was closest to equilibrium among 15 species. We obtained that contemporary species are closer to the equilibrium than the Last Universal Common Ancestor (LUCA) was. Similarly, non-preserved regions in proteins are closer to equilibrium than the preserved ones. We suggest that this approach can be useful for exploring some aspects of biological evolution in the framework of Standard Genetic Code properties(AU)