The ubiquitous P-loop fold nucleoside triphosphatases (NTPases) are typically activated by an arginine or lysine ‘finger’. Some of the apparently ancestral NTPases are, instead, activated by potassium ions. To clarify the activation mechanism, we combined comparative structure analysis with molecular dynamics (MD) simulations of Mg-ATP and Mg-GTP complexes in water and in the presence of potassium, sodium, or ammonium ions. In all analyzed structures of diverse P-loop NTPases, the conserved P-loop motif keeps the triphosphate chain of bound NTPs (or their analogs) in an extended, catalytically prone conformation, similar to that imposed on NTPs in water by potassium or ammonium ions. MD simulations of potassium-dependent GTPase MnmE showed that linking of alpha- and gamma phosphates by the activating potassium ion led to the rotation of the gamma-phosphate group yielding an almost eclipsed, catalytically productive conformation of the triphosphate chain, which could represent the basic mechanism of hydrolysis by P-loop NTPases.
Efficient regulation of internal homeostasis and defending it against perturbations requires adaptive behavioral strategies. However, the computational principles mediating the interaction between homeostatic and associative learning processes remain undefined. Here we use a definition of primary rewards, as outcomes fulfilling physiological needs, to build a normative theory showing how learning motivated behaviors may be modulated by internal states. Within this framework, we mathematically prove that seeking rewards is equivalent to the fundamental objective of physiological stability, defining the notion of physiological rationality of behavior. We further suggest a formal basis for temporal discounting of rewards by showing that discounting motivates animals to follow the shortest path in the space of physiological variables toward the desired setpoint. We also explain how animals learn to act predictively to preclude prospective homeostatic challenges, and several other behavioral patterns. Finally, we suggest a computational role for interaction between hypothalamus and the brain reward system.
Do people routinely pre-activate the meaning and even the phonological form of upcoming words? The most acclaimed evidence for phonological prediction comes from a 2005 Nature Neuroscience publication by DeLong, Urbach and Kutas, who observed a graded modulation of electrical brain potentials (N400) to nouns and preceding articles by the probability that people use a word to continue the sentence fragment (‘cloze’). In our direct replication study spanning 9 laboratories (N=334), pre-registered replication-analyses and exploratory Bayes factor analyses successfully replicated the noun-results but, crucially, not the article-results. Pre-registered single-trial analyses also yielded a statistically significant effect for the nouns but not the articles. Exploratory Bayesian single-trial analyses showed that the article-effect may be non-zero but is likely far smaller than originally reported and too small to observe without very large sample sizes. Our results do not support the view that readers routinely pre-activate the phonological form of predictable words.
Cortical networks exhibit 'global oscillations', where neural spikes are entrained to an underlying oscillatory rhythm, but where individual neurons fire irregularly. While the network dynamics underlying global oscillations are well characterised, their function is debated. Here, we show that such global oscillations are a direct consequence of optimal efficient coding in spiking networks with synaptic delays. To avoid firing unnecessary spikes, neurons must share information about the network state. Ideally, membrane potentials should be correlated and reflect a 'prediction error' while spikes themselves are uncorrelated and occur rarely. We show that the most efficient representation is achieved when: (i) spikes are entrained to a global Gamma rhythm (implying a consistent representation of the error); but (ii) few neurons fire on each cycle (implying high efficiency), while (iii) excitation and inhibition are tightly balanced. This suggests that cortical networks exhibiting such dynamics are tuned to achieve a maximally efficient population code.
Many environmental stimuli present a quasi-rhythmic structure at different timescales that the brain needs to decompose and integrate. Cortical oscillations have been proposed as instruments of sensory de-multiplexing, i.e., the parallel processing of different frequency streams in sensory signals. Yet their causal role in such a process has never been demonstrated. Here, we used a neural microcircuit model to address whether coupled theta–gamma oscillations, as observed in human auditory cortex, could underpin the multiscale sensory analysis of speech. We show that, in continuous speech, theta oscillations can flexibly track the syllabic rhythm and temporally organize the phoneme-level response of gamma neurons into a code that enables syllable identification. The tracking of slow speech fluctuations by theta oscillations, and its coupling to gamma-spiking activity both appeared as critical features for accurate speech encoding. These results demonstrate that cortical oscillations can be a key instrument of speech de-multiplexing, parsing, and encoding.