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Changsong Zhou - One of the best experts on this subject based on the ideXlab platform.

  • spike Pattern Structure influences synaptic efficacy variability under stdp and synaptic homeostasis ii spike shuffling methods on lif networks
    Frontiers in Computational Neuroscience, 2016
    Co-Authors: Changsong Zhou
    Abstract:

    Synapses may undergo variable changes during plasticity because of the variability of spike Patterns such as temporal stochasticity and spatial randomness. Here, we call the variability of synaptic weight changes during plasticity to be efficacy variability. In this paper, we investigate how four aspects of spike Pattern statistics (i.e., synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations) influence the efficacy variability under pair-wise additive spike-timing dependent plasticity (STDP) and synaptic homeostasis (the mean strength of plastic synapses into a neuron is bounded), by implementing spike shuffling methods onto spike Patterns self-organized by a network of excitatory and inhibitory leaky integrate-and-fire (LIF) neurons. With the increase of the decay time scale of the inhibitory synaptic currents, the LIF network undergoes a transition from asynchronous state to weak synchronous state and then to synchronous bursting state. We first shuffle these spike Patterns using a variety of methods, each designed to evidently change a specific Pattern statistics; and then investigate the change of efficacy variability of the synapses under STDP and synaptic homeostasis, when the neurons in the network fire according to the spike Patterns before and after being treated by a shuffling method. In this way, we can understand how the change of Pattern statistics may cause the change of efficacy variability. Our results are consistent with those of our previous study which implements spike-generating models on converging motifs. We also find that burstiness/regularity is important to determine the efficacy variability under asynchronous states, while heterogeneity of cross-correlations is the main factor to cause efficacy variability when the network moves into synchronous bursting states (the states observed in epilepsy).

  • spike Pattern Structure influences synaptic efficacy variability under stdp and synaptic homeostasis i spike generating models on converging motifs
    Frontiers in Computational Neuroscience, 2016
    Co-Authors: Changsong Zhou
    Abstract:

    In neural systems, synaptic plasticity is usually driven by spike trains. Due to the inherent noises of neurons and synapses as well as the randomness of connection details, spike trains typically exhibit variability such as spatial randomness and temporal stochasticity, resulting in variability of synaptic changes under plasticity, which we call efficacy variability. How the variability of spike trains influences the efficacy variability of synapses remains unclear. In this paper, we try to understand this influence under pair-wise additive spike-timing dependent plasticity (STDP) when the mean strength of plastic synapses into a neuron is bounded (synaptic homeostasis). Specifically, we systematically study, analytically and numerically, how four aspects of statistical features, i.e., synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations, as well as their interactions influence the efficacy variability in converging motifs (simple networks in which one neuron receives from many other neurons). Neurons (including the post-synaptic neuron) in a converging motif generate spikes according to statistical models with tunable parameters. In this way, we can explicitly control the statistics of the spike Patterns, and investigate their influence onto the efficacy variability, without worrying about the feedback from synaptic changes onto the dynamics of the post-synaptic neuron. We separate efficacy variability into two parts: the drift part (DriftV) induced by the heterogeneity of change rates of different synapses, and the diffusion part (DiffV) induced by weight diffusion caused by stochasticity of spike trains. Our main findings are: (1) synchronous firing and burstiness tend to increase DiffV, (2) heterogeneity of rates induces DriftV when potentiation and depression in STDP are not balanced, and (3) heterogeneity of cross-correlations induces DriftV together with heterogeneity of rates. We anticipate our work important for understanding functional processes of neuronal networks (such as memory) and neural development.

Stephen B Fountain - One of the best experts on this subject based on the ideXlab platform.

  • central muscarinic cholinergic involvement in serial Pattern learning atropine impairs acquisition and retention in a serial multiple choice smc task in rats
    Neurobiology of Learning and Memory, 2015
    Co-Authors: Amber M Chenoweth, Stephen B Fountain
    Abstract:

    Abstract Atropine sulfate is a muscarinic cholinergic antagonist which impairs acquisition and retention performance on a variety of cognitive tasks. The present study examined the effects of atropine on acquisition and retention of a highly-Structured serial Pattern in a serial multiple choice (SMC) task. Rats were given daily intraperitoneal injections of either saline or atropine sulfate (50 mg/kg) and trained in an octagonal operant chamber equipped with a lever on each wall. They learned to press the levers in a particular order (the serial Pattern) for brain-stimulation reward in a discrete-trial procedure with correction. The two groups learned a Pattern composed of eight 3-element chunks ending with a violation element: 123–234–345–456–567–678–781–818 where the digits represent the clock-wise positions of levers in the chamber, dashes indicate 3-s pauses, and other intertrial intervals were 1 s. Central muscarinic cholinergic blockade by atropine caused profound impairments during acquisition, specifically in the encoding of chunk-boundary elements (the first element of chunks) and the violation element of the Pattern, but had a significant but negligible effect on the encoding of within-chunk elements relative to saline-injected rats. These effects persisted when atropine was removed, and similar impairments were also observed in retention performance. The results indicate that intact central muscarinic cholinergic systems are necessary for learning and producing appropriate responses at places in sequences where Pattern Structure changes. The results also provide further evidence that multiple cognitive systems are recruited to learn and perform within-chunk, chunk-boundary, and violation elements of a serial Pattern.

  • central cholinergic involvement in sequential behavior impairments of performance by atropine in a serial multiple choice task for rats
    Neurobiology of Learning and Memory, 2013
    Co-Authors: Stephen B Fountain, James D Rowan, Michael O Wollan
    Abstract:

    Two experiments examined whether muscarinic cholinergic systems play a role in rats' ability to perform well-learned highly-Structured serial response Patterns, particularly focusing on rats' performance on Pattern elements learned by encoding rules versus by acquisition of stimulus-response (S-R) associations. Rats performed serial Patterns of responses in a serial multiple choice task in an 8-lever circular array for hypothalamic brain-stimulation reward. Two experiments examined the effects of atropine, a centrally-acting muscarinic cholinergic receptor antagonist, on rats' ability to perform Pattern elements where responses were controlled by rules versus elements, such as rule-inconsistent "violation elements" and elements following "phrasing cues," where responses were controlled by associative cues. In Experiment 1, 3-element chunks of both Patterns were signaled by pauses that served as phrasing cues before chunk-boundary elements, but one Pattern also included a violation element that was inconsistent with Pattern Structure. Once rats reached a high criterion of performance, the drug challenge was intraperitoneal injection of a single dose of 50 mg/kg atropine sulfate. Atropine impaired performance on elements learned by S-R learning, namely, chunk-boundary elements and the violation element, but had no effect on performance of rule-based within-chunk elements. In Experiment 2, Patterns were phrased and unphrased perfect Patterns (i.e., without violation elements). To control for peripheral effects of atropine, rats were treated with a series of doses of either centrally-acting atropine or peripherally-acting atropine methyl nitrate (AMN), which does not cross the blood-brain barrier. Once rats reached a high criterion, the drug challenges were on alternate days in the order 50, 25, and 100 mg/kg of either atropine sulfate or AMN. Atropine, but not AMN, impaired performance in the phrased perfect Pattern for Pattern elements where S-R associations were important for performance, namely, chunk-boundary elements. However, in the structurally more ambiguous unphrased perfect Pattern where rats had fewer cues and presumably relied more on S-R associations throughout, atropine impaired performance on all Pattern elements. Thus, intact muscarinic cholinergic systems were shown to be necessary for discriminative control previously established by S-R learning, but were not necessary for rule-based serial Pattern performance.

Jihua Song - One of the best experts on this subject based on the ideXlab platform.

  • research on knowledge representation and automatic recognition of dynamic words for chinese automatic syntactic analysis
    IEEE Access, 2020
    Co-Authors: Dongdong Guo, Weiming Peng, Jihua Song
    Abstract:

    There are many temporarily constructed dynamic words in Chinese sentences. Dynamic words are sentence building units that are not included in the general lexicon and are not suitable for further syntactic analysis. Automatic recognition and analysis of dynamic words in sentences play an important role in improving the efficiency and accuracy of Chinese automatic syntactic analysis. The existing researches on dynamic words mainly focus on the qualitative description of concepts and categories. There is no overall algorithm design and experimental exploration on automatic recognition of dynamic words. In the practice of automatic syntactic analysis, dynamic words are generally segmented, and the components are analyzed according to syntax, while the automatic recognition and analysis of dynamic words as a whole are ignored. In this study, the dynamic word is separated from syntactic analysis as the content of lexical analysis and recognized and analyzed as a whole. This paper uses the method of knowledge engineering to research and analyze dynamic words for Chinese automatic syntactic analysis based on sentence Pattern Structure, initially designs a knowledge representation method of dynamic words, secondly constructs the dynamic word structural mode knowledge base by annotating the dynamic words in the corpus of a certain scale of international Chinese textbooks, and finally explores the automatic recognition methods of dynamic words based on regular expressions, semantic category combinations and machine learning classification algorithms. The experimental results show that the three algorithms can cover the recognition of all types of dynamic words, and achieve relatively ideal accuracy and recall rate.

  • formal schema of diagrammatic chinese syntactic analysis
    Workshop on Chinese Lexical Semantics, 2015
    Co-Authors: Weiming Peng, Jihua Song, Zhifang Sui, Dongdong Guo
    Abstract:

    This paper reviews the research on diagrammatic Chinese syntactic analysis and its Treebank construction which use Sentence Component Analysis (SCA) as the main ideas, puts forward a new formal schema of diagrammatic Chinese syntactic analysis based on sentence Pattern Structure, including diagrammatic style and its XML Structure. Syntactic schemes are drawn on the sentence Pattern system according to the sequence of “basic sentence Pattern, extend sentence Pattern, complex sentence Pattern and special sentence Pattern”, while lexical schemes are drawn on the Diagrammatic Unit which covers idioms/proper nouns, syntactic words and morphology.

Dongdong Guo - One of the best experts on this subject based on the ideXlab platform.

  • research on knowledge representation and automatic recognition of dynamic words for chinese automatic syntactic analysis
    IEEE Access, 2020
    Co-Authors: Dongdong Guo, Weiming Peng, Jihua Song
    Abstract:

    There are many temporarily constructed dynamic words in Chinese sentences. Dynamic words are sentence building units that are not included in the general lexicon and are not suitable for further syntactic analysis. Automatic recognition and analysis of dynamic words in sentences play an important role in improving the efficiency and accuracy of Chinese automatic syntactic analysis. The existing researches on dynamic words mainly focus on the qualitative description of concepts and categories. There is no overall algorithm design and experimental exploration on automatic recognition of dynamic words. In the practice of automatic syntactic analysis, dynamic words are generally segmented, and the components are analyzed according to syntax, while the automatic recognition and analysis of dynamic words as a whole are ignored. In this study, the dynamic word is separated from syntactic analysis as the content of lexical analysis and recognized and analyzed as a whole. This paper uses the method of knowledge engineering to research and analyze dynamic words for Chinese automatic syntactic analysis based on sentence Pattern Structure, initially designs a knowledge representation method of dynamic words, secondly constructs the dynamic word structural mode knowledge base by annotating the dynamic words in the corpus of a certain scale of international Chinese textbooks, and finally explores the automatic recognition methods of dynamic words based on regular expressions, semantic category combinations and machine learning classification algorithms. The experimental results show that the three algorithms can cover the recognition of all types of dynamic words, and achieve relatively ideal accuracy and recall rate.

  • formal schema of diagrammatic chinese syntactic analysis
    Workshop on Chinese Lexical Semantics, 2015
    Co-Authors: Weiming Peng, Jihua Song, Zhifang Sui, Dongdong Guo
    Abstract:

    This paper reviews the research on diagrammatic Chinese syntactic analysis and its Treebank construction which use Sentence Component Analysis (SCA) as the main ideas, puts forward a new formal schema of diagrammatic Chinese syntactic analysis based on sentence Pattern Structure, including diagrammatic style and its XML Structure. Syntactic schemes are drawn on the sentence Pattern system according to the sequence of “basic sentence Pattern, extend sentence Pattern, complex sentence Pattern and special sentence Pattern”, while lexical schemes are drawn on the Diagrammatic Unit which covers idioms/proper nouns, syntactic words and morphology.

King P - One of the best experts on this subject based on the ideXlab platform.

  • Stress inhomogeneity effect on fluid-induced fracture behaviour into weakly consolidated granular systems
    'American Physical Society (APS)', 2020
    Co-Authors: Gago P, Konstantinou C, Biscontin G, King P
    Abstract:

    We study the effect of stress inhomogeneity on the behavior of fluid-driven fracture development in weakly consolidated granular systems. Using numerical models we investigate the change in fracture growth rate and fracture Pattern Structure in unconsolidated granular packs (also referred to as soft-sands) as a function of the change in the confining stresses applied to the system. Soft-sands do not usually behave like brittle, linear elastic materials, and as a consequence, poroelastic models are often not applicable to describe their behavior. By making a distinction between “cohesive” and “compressive” grain-grain contact forces depending on their magnitude, we propose an expression that describes the fluid opening pressure as a function of the mean value and the standard deviation of the “compressive stress” distribution. We also show that the standard deviation of this distribution can be related with the extent to which fracture “branches” reach into the material

  • Stress inhomogeneity effect on fluid-induced fracture behavior into weakly consolidated granular systems
    2020
    Co-Authors: Pa Gago, Konstantinou C, Biscontin G, King P
    Abstract:

    © 2020 American Physical Society. We study the effect of stress inhomogeneity on the behavior of fluid-driven fracture development in weakly consolidated granular systems. Using numerical models we investigate the change in fracture growth rate and fracture Pattern Structure in unconsolidated granular packs (also referred to as soft-sands) as a function of the change in the confining stresses applied to the system. Soft-sands do not usually behave like brittle, linear elastic materials, and as a consequence, poroelastic models are often not applicable to describe their behavior. By making a distinction between "cohesive"and "compressive"grain-grain contact forces depending on their magnitude, we propose an expression that describes the fluid opening pressure as a function of the mean value and the standard deviation of the "compressive stress"distribution. We also show that the standard deviation of this distribution can be related with the extent to which fracture "branches"reach into the material