The amount of neurotransmitter released from a presynaptic terminal is the product of the quantal content (number of vesicles) and the presynaptic quantal size (QSpre, amount of transmitter per vesicle). QSpre varies with synaptic use, but its regulation is poorly understood. The motor nerve terminals at the neuromuscular junction (NMJ) contain TGF-β receptors. We present evidence that TGF-β2 regulates QSpre at the NMJ. Application of TGF-β2 to the rat diaphragm NMJ increased the postsynaptic response to both spontaneous and evoked release of acetylcholine, whereas antibodies to TGF-β2 or its receptor had the converse effect. L-vesamicol and bafilomycin blocked the actions of TGF-β2, indicating that TGF-β2 acts by altering the extent of vesicular filling. Recordings of the postsynaptic currents from the diaphragm were consistent with TGF-β2 having this presynaptic action and a lesser postsynaptic effect on input resistance. TGF-β2 also decreased quantal content by an atropine-sensitive pathway, indicating that this change is secondary to cholinergic feedback on vesicular release. Consequently, the net actions of TGF-β2 at the NMJ were to amplify the postsynaptic effects of spontaneous transmission and to diminish the number of vesicles used per evoked stimulus, without diminishing the amount of acetylcholine released.motoneuron | quantal size | synaptic plasticity | synaptic vesicle | motor nerve terminal S ynapses need to be efficient and malleable to account for the dynamic features of neuronal networks. The number of synaptic vesicles released with each stimulus (quantal content) and the postsynaptic response to each vesicle (quantal size) are actively regulated to achieve this malleability and are major determinants of whether synaptic transmission occurs (1). The amount of neurotransmitter released from each vesicle (presynaptic quantal size, QSpre) appears to vary inversely with synaptic activity and is a component of quantal size (2-4). However, it is unclear whether QSpre is actively regulated as an important component of the characteristics of a synapse.QSpre cannot be measured directly at most synapses; variation in QSpre is inferred from the analysis of either evoked (EPP) or spontaneous postsynaptic potentials. This inference tacitly assumes that the function of QSpre is to influence postsynaptic quantal size. Although this influence may occur in many circumstances, the existence of negative feedback on vesicle release (5) challenges the universality of this assumption. Because any QSpre-dependent change in the amount of neurotransmitter released should be attenuated rapidly by an opposite change in quantal content, upregulation of QSpre would restrict the number of vesicles released per evoked stimulus but would not acutely affect evoked postsynaptic currents unless the negative feedback loop was ineffectual. In that case, the regulation of QSpre would not be readily apparent from studies of EPPs.The major features of synapses are profoundly shaped by extracellular signals from the pre-and postsynap...
Intuitively population based algorithms such as genetic programming provide a natural environment for supporting solutions that learn to decompose the overall task between multiple individuals, or a team. This work presents a framework for evolving teams without recourse to prespecifying the number of cooperating individuals. To do so, each individual evolves a mapping to a distribution of outcomes that, following clustering, establishes the parameterization of a (Gaussian) local membership function. This gives individuals the opportunity to represent subsets of tasks, where the overall task is that of classification under the supervised learning domain. Thus, rather than each team member representing an entire class, individuals are free to identify unique subsets of the overall classification task. The framework is supported by techniques from evolutionary multiobjective optimization (EMO) and Pareto competitive coevolution. EMO establishes the basis for encouraging individuals to provide accurate yet nonoverlaping behaviors; whereas competitive coevolution provides the mechanism for scaling to potentially large unbalanced datasets. Benchmarking is performed against recent examples of nonlinear SVM classifiers over 12 UCI datasets with between 150 and 200,000 training instances. Solutions from the proposed coevolutionary multiobjective GP framework appear to provide a good balance between classification performance and model complexity, especially as the dataset instance count increases.
Classification under large attribute spaces represents a dual learning problem in which attribute subspaces need to be identified at the same time as the classifier design is established. Embedded as opposed to filter or wrapper methodologies address both tasks simultaneously. The motivation for this work stems from the observation that team based approaches to Genetic Programming (GP) have the potential to design multiple classifiers per class-each with a potentially unique attribute subspace-without recourse to filter or wrapper style preprocessing steps. Specifically, competitive coevolution provides the basis for scaling the algorithm to data sets with large instance counts; whereas cooperative coevolution provides a framework for problem decomposition under a bid-based model for establishing program context. Symbiosis is used to separate the tasks of team/ ensemble composition from the design of specific team members. Team composition is specified in terms of a combinatorial search performed by a Genetic Algorithm (GA); whereas the properties of individual team members and therefore subspace identification is established under an independent GP population. Teaming implies that the members of the resulting ensemble of classifiers should have explicitly non-overlapping behaviour. Performance evaluation is conducted over data sets taken from the UCI repository with 649-102,660 attributes and 2-10 classes. The resulting teams identify attribute spaces 1-4 orders of magnitude smaller than under the original data set. Moreover, team members generally consist of less than 10 instructions; thus, small attribute subspaces are not being traded for opaque models.
A framework is introduced for applying GP to streaming data classification tasks under label budgets. This is a fundamental requirement if GP is going to adapt to the challenge of streaming data environments. The framework proposes three elements: a sampling policy, a data subset and a data archiving policy. The sampling policy establishes on what basis data is sampled from the stream, and therefore when label information is requested. The data subset is used to define what GP individuals evolve against. The composition of such a subset is a mixture of data forwarded under the sampling policy and historical data identified through the data archiving policy. The combination of sampling policy and the data subset achieve a decoupling between the rate at which the stream passes and the rate at which evolution commences. Benchmarking is performed on two artificial data sets with specific forms of sudden shift and gradual drift as well as a well known real-world data set.
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