Pub Date : 2019-02-25DOI: 10.32470/CCN.2018.1096-0
M. Kern, Johanna Ruescher, A. Schulze-Bonhage, T. Ball
Analogous to the mirror neuron system repeatedly described in monkeys as a possible substrate for imitation learning and/or action understanding, a neuronal execution/observation matching system (OEMS) is assumed in humans, but little is known to what extent this system is activated in non-experimental, real-life conditions. In the present case study, we investigated brain activity of this system during natural, non-experimental motor behavior as it occurred during playing of the board game "Malefiz". We compared spectral modulations of the high-gamma band related to ipsilateral reaching movement execution and observation of the same kind of movement using electrocorticography (ECoG) in one participant. Spatially coincident activity during both conditions execution and observation was recorded at electrode contacts over the premotor/primary motor cortex. The topography and amplitude of the high-gamma modulations related to both, movement observation and execution were clearly spatially correlated over several fronto-parietal brain areas. Thus, our findings indicate that a network of cortical areas contributes to the human OEMS, beyond primary/premotor cortex including Brocas area and the temporo-parieto-occipital junction area, in real-life conditions.
{"title":"Cortical Mirror-System Activation During Real-Life Game Playing: An Intracranial Electroencephalography (EEG) Study","authors":"M. Kern, Johanna Ruescher, A. Schulze-Bonhage, T. Ball","doi":"10.32470/CCN.2018.1096-0","DOIUrl":"https://doi.org/10.32470/CCN.2018.1096-0","url":null,"abstract":"Analogous to the mirror neuron system repeatedly described in monkeys as a possible substrate for imitation learning and/or action understanding, a neuronal execution/observation matching system (OEMS) is assumed in humans, but little is known to what extent this system is activated in non-experimental, real-life conditions. In the present case study, we investigated brain activity of this system during natural, non-experimental motor behavior as it occurred during playing of the board game \"Malefiz\". We compared spectral modulations of the high-gamma band related to ipsilateral reaching movement execution and observation of the same kind of movement using electrocorticography (ECoG) in one participant. Spatially coincident activity during both conditions execution and observation was recorded at electrode contacts over the premotor/primary motor cortex. The topography and amplitude of the high-gamma modulations related to both, movement observation and execution were clearly spatially correlated over several fronto-parietal brain areas. Thus, our findings indicate that a network of cortical areas contributes to the human OEMS, beyond primary/premotor cortex including Brocas area and the temporo-parieto-occipital junction area, in real-life conditions.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130473329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-24DOI: 10.1007/978-3-319-93776-2_8
D. Boyer, G. Ramos-Fernández
{"title":"Contribution of Social Network Analysis and Collective Phenomena to Understanding Social Complexity and Cognition","authors":"D. Boyer, G. Ramos-Fernández","doi":"10.1007/978-3-319-93776-2_8","DOIUrl":"https://doi.org/10.1007/978-3-319-93776-2_8","url":null,"abstract":"","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114867034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-20DOI: 10.32470/ccn.2018.1050-0
D. G. Nagy, B. Török, Gergő Orbán
Storing knowledge of an agent's environment in the form of a probabilistic generative model has been established as a crucial ingredient in a multitude of cognitive tasks. Perception has been formalised as probabilistic inference over the state of latent variables, whereas in decision making the model of the environment is used to predict likely consequences of actions. Such generative models have earlier been proposed to underlie semantic memory but it remained unclear if this model also underlies the efficient storage of experiences in episodic memory. We formalise the compression of episodes in the normative framework of information theory and argue that semantic memory provides the distortion function for compression of experiences. Recent advances and insights from machine learning allow us to approximate semantic compression in naturalistic domains and contrast the resulting deviations in compressed episodes with memory errors observed in the experimental literature on human memory.
{"title":"Semantic compression of episodic memories","authors":"D. G. Nagy, B. Török, Gergő Orbán","doi":"10.32470/ccn.2018.1050-0","DOIUrl":"https://doi.org/10.32470/ccn.2018.1050-0","url":null,"abstract":"Storing knowledge of an agent's environment in the form of a probabilistic generative model has been established as a crucial ingredient in a multitude of cognitive tasks. Perception has been formalised as probabilistic inference over the state of latent variables, whereas in decision making the model of the environment is used to predict likely consequences of actions. Such generative models have earlier been proposed to underlie semantic memory but it remained unclear if this model also underlies the efficient storage of experiences in episodic memory. We formalise the compression of episodes in the normative framework of information theory and argue that semantic memory provides the distortion function for compression of experiences. Recent advances and insights from machine learning allow us to approximate semantic compression in naturalistic domains and contrast the resulting deviations in compressed episodes with memory errors observed in the experimental literature on human memory.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130318590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-05-31DOI: 10.32470/CCN.2018.1169-0
Christopher W. Lynn, Ari E. Kahn, D. Bassett
Humans are adept at uncovering complex associations in the world around them, yet the underlying mechanisms remain poorly understood. Intuitively, learning the higher-order structure of statistical relationships should involve sophisticated mental processes, expending valuable computational resources. Here we propose a competing perspective: that higher-order associations actually arise from natural errors in learning. Combining ideas from information theory and reinforcement learning, we derive a novel maximum entropy model of people's internal expectations about the transition structures underlying sequences of ordered events. Importantly, our model analytically accounts for previously unexplained network effects on human expectations and quantitatively describes human reaction times in probabilistic sequential motor tasks. Additionally, our model asserts that human expectations should depend critically on the different topological scales in a transition network, a prediction that we subsequently test and validate in a novel experiment. Generally, our results highlight the important role of mental errors in shaping abstract representations, and directly inspire new physically-motivated models of human behavior.
{"title":"Structure from noise: Mental errors yield abstract representations of events","authors":"Christopher W. Lynn, Ari E. Kahn, D. Bassett","doi":"10.32470/CCN.2018.1169-0","DOIUrl":"https://doi.org/10.32470/CCN.2018.1169-0","url":null,"abstract":"Humans are adept at uncovering complex associations in the world around them, yet the underlying mechanisms remain poorly understood. Intuitively, learning the higher-order structure of statistical relationships should involve sophisticated mental processes, expending valuable computational resources. Here we propose a competing perspective: that higher-order associations actually arise from natural errors in learning. Combining ideas from information theory and reinforcement learning, we derive a novel maximum entropy model of people's internal expectations about the transition structures underlying sequences of ordered events. Importantly, our model analytically accounts for previously unexplained network effects on human expectations and quantitatively describes human reaction times in probabilistic sequential motor tasks. Additionally, our model asserts that human expectations should depend critically on the different topological scales in a transition network, a prediction that we subsequently test and validate in a novel experiment. Generally, our results highlight the important role of mental errors in shaping abstract representations, and directly inspire new physically-motivated models of human behavior.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128548391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To understand consciousness within the framework of natural science we must acknowledge the role of energy in the brain. Many contemporary neuroscientists regard the brain as an information processor. However, evidence from brain imaging experiments demonstrates that the brain is actually a voracious consumer of energy, and that functionality is intimately tied to metabolism. Maintaining a critical level of energy in the brain is required to sustain consciousness, and the organisation of this energy distinguishes conscious from unconscious states. Meanwhile, contemporary physicists often regard energy as an abstract mathematical property. But this view neglects energy's causal efficacy and actuality, as identified by Aristotle and later appreciated by many important biologists, psychologists and physicists. By reconsidering the nature of energy and recasting its role in neural activity, we arrive at a theory of consciousness that is consistent with the laws of physics, chemistry and biology. The argument draws on the integrated information theory (IIT) developed by Tononi et al. but reinterprets their findings from the perspective of energy exchange. In IIT, the conscious state in a system, such as a brain, is defined by the quantity of integrated differences, or information, it contains. According to the approach outlined here, it is in the nature of energy to manifest differences of motion and tension. The level of complexity of the energy differences in a system determines its conscious state. Consciousness occurs because, in Nagel's terminology, there is 'something it is like' to be a sufficiently complex state of energy differences.
{"title":"Consciousness and integrated energy differences in the brain","authors":"R. Pepperell","doi":"10.31234/osf.io/fvjt2","DOIUrl":"https://doi.org/10.31234/osf.io/fvjt2","url":null,"abstract":"To understand consciousness within the framework of natural science we must acknowledge the role of energy in the brain. Many contemporary neuroscientists regard the brain as an information processor. However, evidence from brain imaging experiments demonstrates that the brain is actually a voracious consumer of energy, and that functionality is intimately tied to metabolism. Maintaining a critical level of energy in the brain is required to sustain consciousness, and the organisation of this energy distinguishes conscious from unconscious states. Meanwhile, contemporary physicists often regard energy as an abstract mathematical property. But this view neglects energy's causal efficacy and actuality, as identified by Aristotle and later appreciated by many important biologists, psychologists and physicists. By reconsidering the nature of energy and recasting its role in neural activity, we arrive at a theory of consciousness that is consistent with the laws of physics, chemistry and biology. The argument draws on the integrated information theory (IIT) developed by Tononi et al. but reinterprets their findings from the perspective of energy exchange. In IIT, the conscious state in a system, such as a brain, is defined by the quantity of integrated differences, or information, it contains. According to the approach outlined here, it is in the nature of energy to manifest differences of motion and tension. The level of complexity of the energy differences in a system determines its conscious state. Consciousness occurs because, in Nagel's terminology, there is 'something it is like' to be a sufficiently complex state of energy differences.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132496203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The number of neurons that can be simultaneously recorded doubles every seven years. This ever increasing number of recorded neurons opens up the possibility to address new questions and extract higher dimensional stimuli from the recordings. Modeling neural spike trains as point processes, this task of extracting dynamical signals from spike trains is commonly set in the context of nonlinear filtering theory. Particle filter methods relying on importance weights are generic algorithms that solve the filtering task numerically, but exhibit a serious drawback when the problem dimensionality is high: they are known to suffer from the 'curse of dimensionality' (COD), i.e. the number of particles required for a certain performance scales exponentially with the observable dimensions. Here, we first briefly review the theory on filtering with point process observations in continuous time. Based on this theory, we investigate both analytically and numerically the reason for the COD of weighted particle filtering approaches: Similarly to particle filtering with continuous-time observations, the COD with point-process observations is due to the decay of effective number of particles, an effect that is stronger when the number of observable dimensions increases. Given the success of unweighted particle filtering approaches in overcoming the COD for continuous- time observations, we introduce an unweighted particle filter for point-process observations, the spike-based Neural Particle Filter (sNPF), and show that it exhibits a similar favorable scaling as the number of dimensions grows. Further, we derive rules for the parameters of the sNPF from a maximum likelihood approach learning. We finally employ a simple decoding task to illustrate the capabilities of the sNPF and to highlight one possible future application of our inference and learning algorithm.
{"title":"Particle-filtering approaches for nonlinear Bayesian decoding of neuronal spike trains","authors":"A. Kutschireiter, J. Pfister","doi":"10.5167/UZH-168551","DOIUrl":"https://doi.org/10.5167/UZH-168551","url":null,"abstract":"The number of neurons that can be simultaneously recorded doubles every seven years. This ever increasing number of recorded neurons opens up the possibility to address new questions and extract higher dimensional stimuli from the recordings. Modeling neural spike trains as point processes, this task of extracting dynamical signals from spike trains is commonly set in the context of nonlinear filtering theory. Particle filter methods relying on importance weights are generic algorithms that solve the filtering task numerically, but exhibit a serious drawback when the problem dimensionality is high: they are known to suffer from the 'curse of dimensionality' (COD), i.e. the number of particles required for a certain performance scales exponentially with the observable dimensions. Here, we first briefly review the theory on filtering with point process observations in continuous time. Based on this theory, we investigate both analytically and numerically the reason for the COD of weighted particle filtering approaches: Similarly to particle filtering with continuous-time observations, the COD with point-process observations is due to the decay of effective number of particles, an effect that is stronger when the number of observable dimensions increases. Given the success of unweighted particle filtering approaches in overcoming the COD for continuous- time observations, we introduce an unweighted particle filter for point-process observations, the spike-based Neural Particle Filter (sNPF), and show that it exhibits a similar favorable scaling as the number of dimensions grows. Further, we derive rules for the parameters of the sNPF from a maximum likelihood approach learning. We finally employ a simple decoding task to illustrate the capabilities of the sNPF and to highlight one possible future application of our inference and learning algorithm.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122903668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Selfridge, along with Sutherland and Marr provided some of the earliest proposals for how to program computers to recognize shapes. Their emphasis on filtering for contour features, especially the orientation of boundary segments, was reinforced by the Nobel Prize winning work of Hubel & Wiesel who discovered that neurons in primary visual cortex selectively respond as a function of contour orientation. Countless investigators and theorists have continued to build on this approach. These models are often described as neuromorphic, which implies that the computational methods are based on biologically plausible principles. Recent work from the present lab has challenged the emphasis on orientation selectivity and the use of neural network principles. The goal of the present report is not to relitigate those issues, but to provide an alternative concept for encoding of shape information that may be useful to neuromorphic modelers.
{"title":"Scan transcription of two-dimensional shapes as an alternative neuromorphic concept","authors":"E. Greene, Yash J. Patel","doi":"10.36959/643/301","DOIUrl":"https://doi.org/10.36959/643/301","url":null,"abstract":"Selfridge, along with Sutherland and Marr provided some of the earliest proposals for how to program computers to recognize shapes. Their emphasis on filtering for contour features, especially the orientation of boundary segments, was reinforced by the Nobel Prize winning work of Hubel & Wiesel who discovered that neurons in primary visual cortex selectively respond as a function of contour orientation. Countless investigators and theorists have continued to build on this approach. These models are often described as neuromorphic, which implies that the computational methods are based on biologically plausible principles. Recent work from the present lab has challenged the emphasis on orientation selectivity and the use of neural network principles. The goal of the present report is not to relitigate those issues, but to provide an alternative concept for encoding of shape information that may be useful to neuromorphic modelers.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123526524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-25DOI: 10.3390/fractalfract2010006
S. Vitali, F. Mainardi, G. Castellani
Fractional extensions of the cable equation have been proposed in the literature to describe transmembrane potential in spiny dendrites. The anomalous behavior has been related in the literature to the geometrical properties of the system, in particular, the density of spines, by experiments, computer simulations, and in comb-like models. The same PDE can be related to more than one stochastic process leading to anomalous diffusion behavior. The time-fractional diffusion equation can be associated to a continuous time random walk (CTRW) with power-law waiting time probability or to a special case of the Erdely-Kober fractional diffusion, described by the ggBm. In this work, we show that time fractional generalization of the cable equation arises naturally in the CTRW by considering a superposition of Markovian processes and in a ggBm-like construction of the random variable.
{"title":"Emergence of Fractional Kinetics in Spiny Dendrites","authors":"S. Vitali, F. Mainardi, G. Castellani","doi":"10.3390/fractalfract2010006","DOIUrl":"https://doi.org/10.3390/fractalfract2010006","url":null,"abstract":"Fractional extensions of the cable equation have been proposed in the literature to describe transmembrane potential in spiny dendrites. The anomalous behavior has been related in the literature to the geometrical properties of the system, in particular, the density of spines, by experiments, computer simulations, and in comb-like models. The same PDE can be related to more than one stochastic process leading to anomalous diffusion behavior. The time-fractional diffusion equation can be associated to a continuous time random walk (CTRW) with power-law waiting time probability or to a special case of the Erdely-Kober fractional diffusion, described by the ggBm. In this work, we show that time fractional generalization of the cable equation arises naturally in the CTRW by considering a superposition of Markovian processes and in a ggBm-like construction of the random variable.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117087201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-19DOI: 10.1007/978-3-319-73198-8_14
A. Costa, M. J. Amon, O. Sporns, Luis H. Favela
{"title":"Fractal analyses of networks of integrate-and-fire stochastic spiking neurons","authors":"A. Costa, M. J. Amon, O. Sporns, Luis H. Favela","doi":"10.1007/978-3-319-73198-8_14","DOIUrl":"https://doi.org/10.1007/978-3-319-73198-8_14","url":null,"abstract":"","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133954047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We discuss the two moments of human cognition, namely, apprehension (A), whereby a coherent perception emerges from the recruitment of neuronal groups, and judgment(B),that entails the comparison of two apprehensions acquired at different times, coded in a suitable language and retrieved by memory. (B) entails self-consciousness, in so far as the agent who expresses the judgment must be aware that the two apprehensions are submitted to his/her own scrutiny and that it is his/her task to extract a mutual relation. Since (B) lasts around 3 seconds, the semantic value of the pieces under comparison must be decided within that time. This implies a fast search of the memory contents. As a fact, exploring human subjects with sequences of simple words, we find evidence of a limited time window , corresponding to the memory retrieval of a linguistic item in order to match it with the next one in a text flow (be it literary, or musical,or figurative). While apprehension is globally explained as a Bayes inference, judgment tresults from an inverse Bayes inference. As a consequence, two hermeneutics emerge (called respectively circle and coil). The first one acts in a pre-assigned space of features. The second one provides the discovery of novel features, thus unveiling previously unknown aspects and hence representing the road to reality.
{"title":"Cognition and Reality","authors":"F. Arecchi","doi":"10.13128/SUBSTANTIA-40","DOIUrl":"https://doi.org/10.13128/SUBSTANTIA-40","url":null,"abstract":"We discuss the two moments of human cognition, namely, apprehension (A), whereby a coherent perception emerges from the recruitment of neuronal groups, and judgment(B),that entails the comparison of two apprehensions acquired at different times, coded in a suitable language and retrieved by memory. (B) entails self-consciousness, in so far as the agent who expresses the judgment must be aware that the two apprehensions are submitted to his/her own scrutiny and that it is his/her task to extract a mutual relation. Since (B) lasts around 3 seconds, the semantic value of the pieces under comparison must be decided within that time. This implies a fast search of the memory contents. As a fact, exploring human subjects with sequences of simple words, we find evidence of a limited time window , corresponding to the memory retrieval of a linguistic item in order to match it with the next one in a text flow (be it literary, or musical,or figurative). While apprehension is globally explained as a Bayes inference, judgment tresults from an inverse Bayes inference. As a consequence, two hermeneutics emerge (called respectively circle and coil). The first one acts in a pre-assigned space of features. The second one provides the discovery of novel features, thus unveiling previously unknown aspects and hence representing the road to reality.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":"64 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123185495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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