Model For Structural Plasticity Research Articles

Evaluating predisposition and training in shaping the musician's brain: the need for a developmental perspective.

The study of music training as a model for structural plasticity has evolved significantly over the past 15 years. Neuroimaging studies have identified characteristic structural brain alterations in musicians compared to nonmusicians in school-age children and adults, using primarily cross-sectional designs. Despite this emerging evidence and advances in pediatric neuroimaging techniques, hardly any studies have examined brain development in early childhood (before age 8) in association with musical training, and longitudinal studies starting in infancy or preschool are particularly scarce. Consequently, it remains unclear whether the characteristic "musician brain" is solely the result of musical training, or whether certain predispositions may have an impact on its development. Moving toward a developmental perspective, the present review considers various factors that may contribute to early brain structure prior to the onset of formal musical training. This review introduces a model for potential neurobiological pathways leading to the characteristic "musician brain," which involves a developmental interaction between predisposition and its temporal dynamics, environmental experience, and training-induced plasticity. This perspective illuminates the importance of studying the brain structure associated with musical training through a developmental lens, and the need for longitudinal studies in early childhood to advance our understanding of music training-induced structural plasticity.

Simulating structural plasticity of large scale networks in NEST

The brain is much less hard-wired as traditionally thought. Permanently, new synapses are formed, existing synapses are deleted or connectivity rewires by re-routing axonal branches (structural plasticity). However, all current large-scale neuronal network models are hard-wired with plasticity merely arising from changes in the strength of existing synapses, therefore missing an important aspect of the plasticity of brain networks. This project is to develop the first large-scale neuronal network model with structural plasticity in the neuronal network simulator NEST [1] and to make it scalable for HPC. Formation and deletion of synapses in the model for structural plasticity (MSP) [2] depends on the number of synaptic contact possibilities that each neuron has, i.e. the number of axonal boutons and dendritic spines. Therefore, we developed a framework that allows the addition of synaptic elements (i.e. axonal boutons or dendritic spines) for every neuron model already implemented in NEST. The user can then define its own synaptic elements and their corresponding growth dynamic depending on the electrical activity (see Figure ​Figure1).1). Synapses are formed by merging corresponding synaptic elements or are deleted when synaptic elements are lost. The update in connectivity depends on the availability of the synaptic elements in the entire networks. To make this model scalable for HPC, we developed a probabilistic approach that reduce both communication between compute nodes and their memory usage. Figure 1 Example of the definition of two synaptic elements for an integrate and fire neuron using PyNEST. Here, the growth curve of the synaptic element is a Gaussian defined by three parameters: υ (the growth rate), ηz and e (intersections ... This implementation of the MSP in NEST allows neuroscientists to address important scientific questions on how large-scale networks rewire their connectivity in response to distortions in electrical activity balances

Context-Dependent Remodeling of Structure in Two Large Protein Fragments

Protein folding involves the formation of secondary structural elements from the primary sequence and their association with tertiary assemblies. The relation of this primary sequence to a specific folded protein structure remains a central question in structural biology. An increasing body of evidence suggests that variations in homologous sequence ranging from point mutations to substantial insertions or deletions can yield stable proteins with markedly different folds. Here we report the structural characterization of domain IV (D4) and ΔD4 (polypeptides with 222 and 160 amino acids, respectively) that differ by virtue of an N-terminal deletion of 62 amino acids (28% of the overall D4 sequence). The high-resolution crystal structures of the monomeric D4 and the dimeric ΔD4 reveal substantially different folds despite an overall conservation of secondary structure. These structures show that the formation of tertiary structures, even in extended polypeptide sequences, can be highly context dependent, and they serve as a model for structural plasticity in protein isoforms.

Prediction of Protein-Protein Interface Sequence Diversity Using Flexible Backbone Computational Protein Design

A major challenge in computational protein design is to identify functional sequences as top predictions. One reason for design failures is conformational plasticity, as proteins frequently change their conformation in response to mutations. To advance protein design, here we describe a method employing flexible backbone ensembles to predict sequences tolerated for a protein-protein interface. We show that the predictions are enriched in functional proteins when compared to a phage display screen quantitatively mapping the energy landscape for the interaction between human growth hormone and its receptor. Our model for structural plasticity is inspired by coupled side chain-backbone "backrub" motions observed in high-resolution protein crystal structures. Although the modeled structural changes are subtle, our results on predicting sequence plasticity suggest that backrub sampling may capture a sizable fraction of localized conformational changes that occur in proteins. The described method has implications for predicting sequence libraries to enable challenging protein engineering problems.

A model for structural plasticity in neocortical associative networks trained by the hippocampus

The hippocampal formation plays a crucial role in organizing cortical long-term memory. It is believed that the hippocampus is capable of fast (one-shot) learning of new episodic information followed by extensive time periods where corresponding neocortical representations are trained and compressed [1]. Here, compression usually refers to processes such as chunking spatially and temporally distributed activity patterns. We take the complementary approach and optimize the synaptic network by structural plasticity, e.g., replacing unused synapses, thereby making full use of the potential connectivity [2]. We apply the frameworks of structural plasticity and hippocampus-induced learning to the training of neocortical associative networks [3]. Associative networks such as the Hopfield or Willshaw model are at the heart of many cortex theories and have been analyzed for a long time with respect to information storage capacity and plausible retrieval strategies [3,4]. For example, it is well known that a completely connected network can store about 0.7 bits per synapse. However, for incompletely connected networks the capacity per synapse can be massively reduced or even vanish, depending on the retrieval algorithm [4]. In this work we analyze how structural processes and synaptic consolidation [5] during hippocampal training can improve the performance of neocortical associative networks by emulating full (or increased) synaptic connectivity. In our model the hippocampus can store a set of activity patterns by one-shot learning. Then the hippocampus trains the neocortex by repeatedly replaying the patterns in a sequence. The synapses of the neocortical network are consolidated depending on Hebbian learning. In each time step a fraction of the unconsolidated synapses are removed and replaced by the same number of new synapses at random locations thereby maintaining total connectivity. We show that this procedure can massively increase the synaptic capacity of a cortical macrocolumn (factor 10–20 or even up to factor 200 for pattern capacity). In a second step we analyze the model with respect to the time (or number of repetitions) necessary to increase effective connectivity from base level to a desired level. The analysis shows that acceptable training time requires a certain fraction of unconsolidated synapses to keep the network plastic.