General Rules for Optimal Codon Choice

Different synonymous codons are favored by natural selection for translation efficiency and accuracy in different organisms. The rules governing the identities of favored codons in different organisms remain obscure. In fact, it is not known whether such rules exist or whether favored codons are chosen randomly in evolution in a process akin to a series of frozen accidents. Here, we study this question by identifying for the first time the favored codons in 675 bacteria, 52 archea, and 10 fungi. We use a number of tests to show that the identified codons are indeed likely to be favored and find that across all studied organisms the identity of favored codons tracks the GC content of the genomes. Once the effect of the genomic GC content on selectively favored codon choice is taken into account, additional universal amino acid specific rules governing the identity of favored codons become apparent. Our results provide for the first time a clear set of rules governing the evolution of selectively favored codon usage. Based on these results, we describe a putative scenario for how evolutionary shifts in the identity of selectively favored codons can occur without even temporary weakening of natural selection for codon bias.     

离散的互惠生态系统的最优捕获策略

本文利用离散的两种群互惠模型给出了在捕获能力不同状况下的不同最优捕获策略,指出了理论上和实践上可获得的最大经济效益。     

The Empirical Bayes Classification Rules for Mixtures of Discrete and Continuous Variables

Let us consider a general population π. Each object belonging to the population π is characterized by a pair of correlated random vectors (X, Y ). Both X and Y may be mixtures of discrete and continuous random variables. It will be assumed that our population π consists of k groups π₁,….,πk, which depend on the value of the random vector Y. A certain object, which is an element of one of the k groups π₁, …, πk, has to be classified into the correct group. The knowledge of the value of the random vector Y would permit its correct classification, but the observation of this vector is difficult or dangerous and we must assign the individual on the basis of the observation of the random vector X . The classification procedure is based on randomized decision function δ* which minimizes the risk function i.e. Bayes decision function. We give also two empirical Bayes classification rules i.e. decision functions based on the sample from population π and having property that their risks converge to Bayes risk when the sample size increases.     

A Machine Learning Method for the Prediction of Receptor Activation in the Simulation of Synapses

Chemical synaptic transmission involves the release of a neurotransmitter that diffuses in the extracellular space and interacts with specific receptors located on the postsynaptic membrane. Computer simulation approaches provide fundamental tools for exploring various aspects of the synaptic transmission under different conditions. In particular, Monte Carlo methods can track the stochastic movements of neurotransmitter molecules and their interactions with other discrete molecules, the receptors. However, these methods are computationally expensive, even when used with simplified models, preventing their use in large-scale and multi-scale simulations of complex neuronal systems that may involve large numbers of synaptic connections. We have developed a machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the percentage of open synaptic receptors as a function of time since the release of the neurotransmitter, with considerably lower computational cost compared with the conventional Monte Carlo alternative. The method is designed to learn patterns and general principles from a corpus of previously generated Monte Carlo simulations of synapses covering a wide range of structural and functional characteristics. These patterns are later used as a predictive model of the behavior of synapses under different conditions without the need for additional computationally expensive Monte Carlo simulations. This is performed in five stages: data sampling, fold creation, machine learning, validation and curve fitting. The resulting procedure is accurate, automatic, and it is general enough to predict synapse behavior under experimental conditions that are different to the ones it has been trained on. Since our method efficiently reproduces the results that can be obtained with Monte Carlo simulations at a considerably lower computational cost, it is suitable for the simulation of high numbers of synapses and it is therefore an excellent tool for multi-scale simulations.     

Presynaptic GABAB Receptors Regulate Hippocampal Synapses during Associative Learning in Behaving Mice

GABA_B receptors are the G-protein-coupled receptors for GABA, the main inhibitory neurotransmitter in the central nervous system. Pharmacological activation of GABA_B receptors regulates neurotransmission and neuronal excitability at pre- and postsynaptic sites. Electrophysiological activation of GABA_B receptors in brain slices generally requires strong stimulus intensities. This raises the question as to whether behavioral stimuli are strong enough to activate GABA_B receptors. Here we show that GABA_B1a^-/- mice, which constitutively lack presynaptic GABA_B receptors at glutamatergic synapses, are impaired in their ability to acquire an operant learning task. In vivo recordings during the operant conditioning reveal a deficit in learning-dependent increases in synaptic strength at CA3-CA1 synapses. Moreover, GABA_B1a^-/- mice fail to synchronize neuronal activity in the CA1 area during the acquisition process. Our results support that activation of presynaptic hippocampal GABA_B receptors is important for acquisition of a learning task and for learning-associated synaptic changes and network dynamics.     

Insect Neuromuscular Synapses

Miniature end-plate potentials were used in studying severalaspects of the neuromuscular systems in the cockroach femur.The similar sizes and time courses of miniatures associatedwith fast and slow type excitatory axons suggest that they employthe same transmitter. There is other evidence also indicatingthat the essential difference between these two excitatory systemsis in the number of packets of transmitter released per nerveimpulse rather than different transmitter substances. From theshapes of miniatures it was suspected that typical muscle fibersmight have a branching structure. This was confirmed by histologicalexamination, intracellular stimulation, and intracellular dyeinjection. That inhibitory transmission is quantal is indicatedby hyperpolarizing miniatures which occur at random time intervals.Inhibitory transmission can be made to fail and recover in astepwise manner by manipulating the Ca/Mg ratio. In studiesof toxins which affect transmitter release at vertebrate motorend-plates, botulinal toxin was found to be without effect ateither excitatory or inhibitory junctions in cockroach muscle.However, black widow spider venom acted as it does in vertebrates,promoting massive release of transmitters and then permanentinactivation of the junctions.     

Computational Consequences of Temporally Asymmetric Learning Rules: I. Differential Hebbian Learning

Temporally asymetric learning rules governing plastic changes in synaptic efficacy have recently been identified in physiological studies. In these rules, the exact timing of pre- and postsynaptic spikes is critical to the induced change of synaptic efficacy. The temporal learning rules treated in this article are approximately antisymmetric; the synaptic efficacy is enhanced if the postsynaptic spike follows the presynaptic spike by a few milliseconds, but the efficacy is depressed if the postsynaptic spike precedes the presynaptic spike. The learning dynamics of this rule are studied using a stochastic model neuron receiving a set of serially delayed inputs. The average change of synaptic efficacy due to the temporally antisymmetric learning rule is shown to yield differential Hebbian learning. These results are demonstrated with both mathematical analyses and computer simulations, and connections with theories of classical conditioning are discussed.     

Synapses and Memory Storage

The synapse is the functional unit of the brain. During the last several decades we have acquired a great deal of information on its structure, molecular components, and physiological function. It is clear that synapses are morphologically and molecularly diverse and that this diversity is recruited to different functions. One of the most intriguing findings is that the size of the synaptic response in not invariant, but can be altered by a variety of homo- and heterosynaptic factors such as past patterns of use or modulatory neurotransmitters. Perhaps the most difficult challenge in neuroscience is to design experiments that reveal how these basic building blocks of the brain are put together and how they are regulated to mediate the information flow through neural circuits that is necessary to produce complex behaviors and store memories. In this review we will focus on studies that attempt to uncover the role of synaptic plasticity in the regulation of whole-animal behavior by learning and memory.The idea that learning results from changes in the strength of the synapse was first suggested by Santiago Ramon y Cajal (1894) based on insights from his anatomical studies. That modulation of synaptic connectivity is a critical mechanism of learning was incorporated into more refined models by Hebb in the 1940s and 1950s. The experimental investigation of these intriguing conjectures required the development of behavioral systems in which one could examine changes in the neuronal components of a specific behavior during or after the modification of that behavior with learning (Kandel and Spencer 1968).     

Optimal Sequencing Rules for Some Large-Scale Flexible Manufacturing Problems Under the Manhattan and Chebychev Metrics

The purpose of this paper is to develop optimal tool partitioning policies and strip sequencing strategies for a class of flexible manufacturing problems. The problems under consideration involve a large number of operations to be performed by a series of tools on a two-dimensional object. For example, these operations could consist of drilling holes in a metallic sheet. Tools are arranged in a carousel or along a toolbar according to a predetermined sequence. Operations are performed by repeatedly moving the sheet to bring the hole locations under the tool. During each pass, as all operations involving a series of consecutive tools are executed, two main problems are to be solved: (1) how to move the sheet during each pass, (2) how to partition the tools into blocks of consecutive tools. A strip strategy is used to move the sheet. Given this policy, optimal strip widths and tool partitioning policies are determined jointly. Analytical solutions are derived under two metrics corresponding to different operating modes. A numerical example is provided.     

The Effect of STDP Temporal Kernel Structure on the Learning Dynamics of Single Excitatory and Inhibitory Synapses

Spike-Timing Dependent Plasticity (STDP) is characterized by a wide range of temporal kernels. However, much of the theoretical work has focused on a specific kernel – the “temporally asymmetric Hebbian” learning rules. Previous studies linked excitatory STDP to positive feedback that can account for the emergence of response selectivity. Inhibitory plasticity was associated with negative feedback that can balance the excitatory and inhibitory inputs. Here we study the possible computational role of the temporal structure of the STDP. We represent the STDP as a superposition of two processes: potentiation and depression. This allows us to model a wide range of experimentally observed STDP kernels, from Hebbian to anti-Hebbian, by varying a single parameter. We investigate STDP dynamics of a single excitatory or inhibitory synapse in purely feed-forward architecture. We derive a mean-field-Fokker-Planck dynamics for the synaptic weight and analyze the effect of STDP structure on the fixed points of the mean field dynamics. We find a phase transition along the Hebbian to anti-Hebbian parameter from a phase that is characterized by a unimodal distribution of the synaptic weight, in which the STDP dynamics is governed by negative feedback, to a phase with positive feedback characterized by a bimodal distribution. The critical point of this transition depends on general properties of the STDP dynamics and not on the fine details. Namely, the dynamics is affected by the pre-post correlations only via a single number that quantifies its overlap with the STDP kernel. We find that by manipulating the STDP temporal kernel, negative feedback can be induced in excitatory synapses and positive feedback in inhibitory. Moreover, there is an exact symmetry between inhibitory and excitatory plasticity, i.e., for every STDP rule of inhibitory synapse there exists an STDP rule for excitatory synapse, such that their dynamics is identical.     

Optimal Foraging Theory and the Psychology of Learning

The development of optimization theory has made important contributionsto the study of animal behavior. But the optimization approachneeds to be integrated with other methods of ethology and psychology.For example, the ability to learn is an important componentof efficient foraging behavior in many species, and the psychologyof animal learning could contribute substantially to testingand extending the predictions of optimal foraging theory.     

Matching Rules for Collective Behaviors on Complex Networks: Optimal Configurations for Vibration Frequencies of Networked Harmonic Oscillators

The structure-dynamics-function has become one of central problems in modern sciences, and it is a great challenge to unveil the organization rules for different dynamical processes on networks. In this work, we study the vibration spectra of the classical mass spring model with different masses on complex networks, and pay our attention to how the mass spatial configuration influences the second-smallest vibrational frequency () and the largest one (). For random networks, we find that becomes maximal and becomes minimal if the node degrees are point-to-point-positively correlated with the masses. In these cases, we call it point-to-point matching. Moreover, becomes minimal under the condition that the heaviest mass is placed on the lowest-degree vertex, and is maximal as long as the lightest mass is placed on the highest-degree vertex, and in both cases all other masses can be arbitrarily settled. Correspondingly, we call it single-point matching. These findings indicate that the matchings between the node dynamics (parameter) and the node position rule the global systems dynamics, and sometimes only one node is enough to control the collective behaviors of the whole system. Therefore, the matching rules might be the common organization rules for collective behaviors on networks.     

Modelling Vesicular Release at Hippocampal Synapses

We study local calcium dynamics leading to a vesicle fusion in a stochastic, and spatially explicit, biophysical model of the CA3-CA1 presynaptic bouton. The kinetic model for vesicle release has two calcium sensors, a sensor for fast synchronous release that lasts a few tens of milliseconds and a separate sensor for slow asynchronous release that lasts a few hundred milliseconds. A wide range of data can be accounted for consistently only when a refractory period lasting a few milliseconds between releases is included. The inclusion of a second sensor for asynchronous release with a slow unbinding site, and thereby a long memory, affects short-term plasticity by facilitating release. Our simulations also reveal a third time scale of vesicle release that is correlated with the stimulus and is distinct from the fast and the slow releases. In these detailed Monte Carlo simulations all three time scales of vesicle release are insensitive to the spatial details of the synaptic ultrastructure. Furthermore, our simulations allow us to identify features of synaptic transmission that are universal and those that are modulated by structure.     

Structure of Excitatory Synapses and GABAA Receptor Localization at Inhibitory Synapses Are Regulated by Neuroplastin-65

Formation, maintenance, and activity of excitatory and inhibitory synapses are essential for neuronal network function. Cell adhesion molecules (CAMs) are crucially involved in these processes. The CAM neuroplastin-65 (Np65) highly expressed during periods of synapse formation and stabilization is present at the pre- and postsynaptic membranes. Np65 can translocate into synapses in response to electrical stimulation and it interacts with subtypes of GABA_A receptors in inhibitory synapses. Here, we report that in the murine hippocampus and in hippocampal primary culture, neurons of the CA1 region and the dentate gyrus (DG) express high Np65 levels, whereas expression in CA3 neurons is lower. In neuroplastin-deficient (Np^−/−) mice the number of excitatory synapses in CA1 and DG, but not CA3 regions is reduced. Notably this picture is mirrored in mature Np^−/− hippocampal cultures or in mature CA1 and DG wild-type (Np^+/+) neurons treated with a function-blocking recombinant Np65-Fc extracellular fragment. Although the number of GABAergic synapses was unchanged in Np^−/− neurons or in mature Np65-Fc-treated Np^+/+ neurons, the ratio of excitatory to inhibitory synapses was significantly lower in Np^−/− cultures. Furthermore, GABA_A receptor composition was altered at inhibitory synapses in Np^−/− neurons as the α1 to α2 GABA_A receptor subunit ratio was increased. Changes of excitatory and inhibitory synaptic function in Np^−/− neurons were confirmed evaluating the presynaptic release function and using patch clamp recording. These data demonstrate that Np65 is an important regulator of the number and function of synapses in the hippocampus.