Spiking neural networks (SNNs) are the next-generation neural networks where biologically plausible neurons communicate through spikes. SNNs can be trained to perform various tasks by modifying the plastic parameters, including weights and time delays. However, SNNs have not yet achieved the same level of performance as typical artificial neural networks (ANNs). One possible solution to improve the performance of SNNs is to consider more plastic parameters from the biological neural system than just weights and time delays in SNNs.

Neurons in the brain communicate with each other by sending trains of spikes that can encode information using the timings of the spikes. Spiking Neural Networks (SNNs) are biologically plausible neural networks that can model this transfer of information and that, by incorporating plasticity, can detect repeating patterns that may be embedded in a train of spikes with a noisy background. Although existing model networks are capable of learning to detect the occurrence of repeated patterns, they are not tailored to recover the entirety of the time sequence of the spikes in the pattern. Here we present a network that, in addition to typical parameters of plasticity such as weights and time delays, uses a new plasticity on the time domain that can recover most of the spikes in a pattern. This new plasticity acts by modifying the timings of reference spikes that come from a hypothesized upstream network that could potentially encode preexisting memories. The model is shown to be robust under several noise perturbation scenarios, and its overall performance demonstrates the benefits of using reference spikes to improve the temporal information processing ability of SNNs.

Spiking neural networks (SNNs) are the next-generation neural networks composed of biologically plausible neurons that communicate through trains of spikes. By modifying the plastic parameters of SNNs, including weights and time delays, SNNs can be trained to perform various AI tasks, although in general not at the same level of performance as typical artificial neural networks (ANNs). One possible solution to improve the performance of SNNs is to consider plastic parameters other than just weights and time delays drawn from the inherent complexity of the neural system of the brain, which may help SNNs improve their information processing ability and achieve brainlike functions. Here, we propose reference spikes as a new type of plastic parameters in a supervised learning scheme in SNNs. A neuron receives reference spikes through synapses providing reference information independent of input to help during learning, whose number of spikes and timings are trainable by error backpropagation. Theoretically, reference spikes improve the temporal information processing of SNNs by modulating the integration of incoming spikes at a detailed level. Through comparative computational experiments, we demonstrate using supervised learning that reference spikes improve the memory capacity of SNNs to map input spike patterns to target output spike patterns and increase classification accuracy on the MNIST, Fashion-MNIST, and SHD data sets, where both input and target output are temporally encoded. Our results demonstrate that applying reference spikes improves the performance of SNNs by enhancing their temporal information processing ability.

Traveling waves of neuronal spiking activity are commonly observed across the brain, but their intrinsic function is still a matter of investigation. Experiments suggest that they may be valuable in the consolidation of memory or learning, indicating that consideration of traveling waves in the presence of plasticity might be important. A possible outcome of this consideration is that the synaptic pathways, necessary for the propagation of these waves, will be modified by the waves themselves. This will create a feedback loop where both the traveling waves and the strengths of the available synaptic pathways will change. To computationally investigate this, we model a sheet of cortical tissue by considering a quasi two-dimensional network of model neurons locally connected with plastic synaptic weights using Spike-Timing Dependent Plasticity (STDP). By using different stimulation conditions (central, stochastic, and alternating stimulation), we demonstrate that starting from a random network, traveling waves with STDP will form and strengthen propagation pathways. With progressive formation of traveling waves, we observe increases in synaptic weight along the direction of wave propagation, increases in propagation speed when pathways are strengthened over time, and an increase in the local order of synaptic weights. We also present evidence that the interaction between traveling waves and plasticity can serve as a mechanism of network-wide competition between available pathways. With an improved understanding of the interactions between traveling waves and synaptic plasticity, we can approach a fuller understanding of mechanisms of learning, computation, and processing within the brain.

Traveling waves of local field potential and neuronal spiking are commonly observed across various regions of the brain, but their potential function is still a matter of intense research. In addition, synaptic plasticity, most often Hebbian or dependent on the spike-timing of pre and postsynaptic neurons, is consistently observed as a mechanism for the restructuring and refinement of neuronal circuits. As traveling waves are ubiquitously observed, they are likely to interact with synaptic plasticity to affect the activity and structure of networks in the brain.

Traveling waves of neuronal activity in the cortex have been observed in vivo. These traveling waves have been correlated to various features of observed cortical dynamics, including spike timing variability and correlated fluctuations in neuron membrane potential. Although traveling waves are typically studied as either strictly one-dimensional or two-dimensional excitations, here we investigate the conditions for the existence of quasi-one-dimensional traveling waves that could be sustainable in parts of the brain containing cortical minicolumns. For that, we explore a quasi-one-dimensional network of heterogeneous neurons with a biologically influenced computational model of neuron dynamics and connectivity. We find that background stimulus reliably evokes traveling waves in networks with local connectivity between neurons. We also observe traveling waves in fully connected networks when a model for action potential propagation speed is incorporated. The biological properties of the neurons influence the generation and propagation of the traveling waves. Our quasi-one-dimensional model is not only useful for studying the basic properties of traveling waves in neuronal networks; it also provides a simplified representation of possible wave propagation in columnar or minicolumnar networks found in the cortex.

Neuronal connectivity at the cellular level in the cerebral cortex is far from random, with characteristics that point to a hierarchical design with intricately connected neuronal clusters. Here we investigate computationally the effects of varying neuronal cluster connectivity on network synchronization for two different spatial distributions of clusters: one where clusters are arranged in columns in a grid and the other where neurons from different clusters are spatially intermixed. We characterize each case by measuring the degree of neuronal spiking synchrony as a function of the number of connections per neuron and the degree of intercluster connectivity. We find that in both cases as the number of connections per neuron increases, there is an asynchronous to synchronous transition dependent only on intrinsic parameters of the biophysical model. We also observe in both cases that with very low intercluster connectivity clusters have independent firing dynamics yielding a low degree of synchrony. More importantly, we find that for a high number of connections per neuron but intermediate intercluster connectivity, the two spatial distributions of clusters differ in their response where the clusters in a grid have a higher degree of synchrony than the clusters that are intermixed.

Molecular dynamics simulations are used to provide insights into the molecular mechanisms accounting for binding of amyloid fibrils to lipid bilayers and to study the effect of cholesterol in this process. We show that electrostatic interactions play an important role in fibril-bilayer binding and cholesterol modulates this interaction. In particular, the interaction between positive residues and lipid head groups becomes more favorable in the presence of cholesterol. Consistent with experiments, we find that cholesterol enhances fibril-membrane binding.

Connectivity in the brain has long been explored on varying scales: from connectivity of large regions down to groups of only a few neurons. In this work we explore how a connectivity scheme inspired by columnar organization in the neocortex effects the synchronization of a system of neurons. Neurons are grouped based on (x,y) positions in a grid to form columnar groups, and a control system is grouped independent of position. These neurons are connected with a bias towards connections within their groups, based on bias parameter, δ. This scheme leads to a small-world network containing highly connected groups as well as inter-group connections. Systems were created with purely excitatory neurons as well as systems of 80% excitatory neurons and 20% inhibitory neurons. Simulations are run with external stimulation to each Hodgkin-Huxley type neuron, and distance-dependent delays are included in the propagation of each action potential. We find that initially the connectivity must reach a critical point in order for the system to synchronize. Above that point, the randomly grouped control system becomes less synchronous (measured by SPIKE-distance) than the system grouped in a columnar structure. This difference is significant in a particular region of our parameter phase space at very low signal propagation speeds. Additional work has been done to further characterize the dynamics of these systems including the frequency of global spiking events.

In this work we examine the dynamics of an intrinsically disordered protein fragment of the amyloid β, the Aβ21-30, under seven commonly used molecular dynamics force fields (OPLS-AA, CHARMM27-CMAP, AMBER99, AMBER99SB, AMBER99SB-ILDN, AMBER03, and GROMOS53A6), and three water models (TIP3P, TIP4P, and SPC/E). We find that the tested force fields and water models have little effect on the measures of radii of gyration and solvent accessible surface area (SASA); however, secondary structure measures and intrapeptide hydrogen-bonding are significantly modified, with AMBER (99, 99SB, 99SB-ILDN, and 03) and CHARMM22/27 force-fields readily increasing helical content and the variety of intrapeptide hydrogen bonds. On the basis of a comparison between the population of helical and β structures found in experiments, our data suggest that force fields that suppress the formation of helical structure might be a better choice to model the Aβ21-30 peptide.

ADVERTISEMENT RETURN TO ISSUEPREVAddition/CorrectionORIGINAL ARTICLEThis notice is a correctionCorrection to "Effect of Ionic Aqueous Environments on the Structure and Dynamics of the Aβ21–30 Fragment: A Molecular-Dynamics Study"Micholas Dean Smith and Luis Cruz*Cite this: J. Phys. Chem. B 2014, 118, 29, 8916Publication Date (Web):July 10, 2014Publication History Published online10 July 2014Published inissue 24 July 2014https://pubs.acs.org/doi/10.1021/jp506242uhttps://doi.org/10.1021/jp506242ucorrectionACS PublicationsCopyright © 2014 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views201Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (109 KB) Get e-Alertsclose Get e-Alerts

Constant temperature and replica-exchange molecular dynamics simulations of two Aβ21-30 decapeptides in explicit solvent reveal metastable dimer states that are abundant near physiological temperatures. As Alzheimer's disease is associated with the neurotoxic oligomers of amyloid β-protein, the formation of these dimers provides insight into oligomer assembly.

Understanding protein folding and stability in in vivo confined environments is a challenging problem from both experimental and computational points of views. Despite recent insights, an appreciation and complete understanding of how the solvent influences the structure and stability of proteins under complex confined environments is still lacking. Here, using all-atom molecular dynamics simulations in explicit solvent, we report the effects of confinement on the lifetime of a metastable β-hairpin structure in the Aβ(21-30) decapeptide. Our results show that the values of these lifetimes depend on the nature of the confining surface, where smooth and rough hydrophobic confining walls have solvent-mediated stabilizing and destabilizing effects, respectively. The source of the destabilization found inside atomically rough confining walls lies in surface-peptide interactions that break the β-hairpin in this peptide, whereas smooth confining walls stabilize it by forming well-ordered layers of water that keep the decapeptide solvated in the inner part of the pore and away from the surface. In addition, we show that the size of the confining pore can tune the value of the lifetimes where pore sizes comparable to the size of the decapeptide have the largest effects.

A population of neurons in the cerebral cortex of humans and other mammals organize themselves into vertical microcolumns perpendicular to the pial surface. Anatomical changes to these microcolumns have been correlated with neurological diseases and normal aging; in particular, in area 46 of the rhesus monkey brain, the strength of microcolumns was shown to decrease with age. These changes can be caused by alterations in the spatial distribution of the neurons in microcolumns and/or neuronal loss. Using a three-dimensional computational model of neuronal arrangements derived from thin tissue sections and validated in brain tissue from rhesus monkeys, we show that neuronal loss is inconsistent with the findings in aged individuals. In contrast, a model of simple random neuronal displacements, constrained in magnitude by restorative harmonic forces, is consistent with observed changes and provides mechanistic insights into the age-induced loss of microcolumnar structure. Connection of the model to normal aging and disease are discussed.

The structure and dynamics of the 21-30 fragment of the amyloid beta-protein (A beta(21-30)) and its Dutch [Glu22Gln], Arctic [Glu22Gly], and Iowa [Asp23Asn] isoforms are of considerable importance, as their folding may play an important role in the pathogenesis of sporadic and familial forms of Alzheimer's disease and cerebral amyloid angiopathy. A full understanding of this pathologic folding in in vivo environments is still elusive. Here we examine the interactions and effects of two neurobiologically relevant salts (CaCl2 and KCl) on the structure and dynamics of A beta(21-30) decapeptide monomers containing the Dutch, Arctic, and Iowa charge-modifying point mutations using isobaric isothermal (NPT) explicit water all-atom molecular-dynamics simulations. Measurements of secondary structure populations, intrapeptide hydrogen bonding, salt bridging, secondary structure lifetimes, cation-residue contracts, water-peptide hydrogen bonding, and hydration-shell water residence times reveal a variety of ion and mutation-dependent modifications to the decapeptide's structure and dynamics. In general, Ca2+ has the effect of increasing coil-state populations and lifetimes, modifying the behavior of the decapeptide's hydration shell and diminishing intrapeptide hydrogen bonding, while K+ is found to diminish coil populations and lifetimes and, for the case of the Iowa mutant, dramatically increase the decapeptide's propensity for beta secondary structures. Mutation-dependent effects highlight the different roles of the Glu22 and Asp23 residues in either solvating or enhancing turn structures, respectively. Taken together, our results provide insights into the differential roles of different ionic species as well as specific effects on the Glu22 and Asp23 residues of A beta(21-30) mediated by ion-decapeptide interactions and the solvent, which could be important interaction mechanisms relevant to the peptide's behavior in both in vitro and in vivo environments.

The amyloid β protein (Aβ) has been implicated in the pathogenesis of Alzheimer's disease. Previous in vitro experiments have shown that the central Aβ(21-30) fragment may have special importance because it may act as a folding nucleus of the full-length protein. Recently, experiments of the full-length wild type (WT) peptide under aqueous salt environments have revealed varied responses in both the structure and growth of aggregates of the full-length peptide under these environments. Here we use all-atom molecular dynamics simulations of monomeric Aβ(21-30) to examine pre-aggregate structural alterations under similar dissolved salt conditions (CaCl2 and KCl). Further, we make use of the wild-type and three common mutations of the decapeptide (Arctic[E22/G22], Dutch[E22/Q22], and Iowa[D23/N23]) to explore the possible dependence of charged side-chain and salt-ion interactions in driving structural changes under aqueous salt environments. Our results indicate that the production and stability of open (random-coil) structures is enhanced under CaCl2 for the wild-type decapeptide, and both Dutch and Iowa mutations; while KCl environments enhanced the production of turn structures for wild-type and β-structures for the Iowa and Dutch mutations. Additionally, we present a possible explanation for these differences in structural response as a combination of volume exclusion, ion-residue interactions, and ion effects on the hydration of the decapeptide.

Over the last few decades, the discovery of intrinsically disordered proteins (IDPs) has challenged conventional wisdom by demonstrating that they play biologically important roles while not holding any well-defined structure. The experimental characterization of their behaviour is complicated by the fact that they do not have a folded native state. In addition, an understanding of the mechanisms by which they operate in vivo is further obscured by the fact that cellular environments are confined spaces in which they perform their function: a daunting problem since even the folding of regular proteins in confined environments is still not yet fully understood. Here, we address the dynamics and possible structure stabilization of an IDP under confinement by using all-atom molecular dynamics simulations in explicit water to study the sporadic formation of secondary structure in the decapeptide fragment of the full-length amyloid β-protein (implicated in Alzheimer's disease), the Aβ(21-30) decapeptide. Metastable β-hairpin structures found in this decapeptide, and characterized by a lifetime in bulk water, are shown to become more stable or unstable when confined inside small pores with either polar or non-polar surfaces. For progressively smaller pores the stability of these β-hairpin structures is shown to depend on the nature of this surface rather than on the effects of confinement on the solvent water. Results are also presented using a familial mutation of the Aβ, the Iowa mutation, responsible for a more radical form of the disease.

Some areas in the cerebral cortex are characterized by microcolumns: arrays of interconnected neurons which may constitute a fundamental computational unit in the brain. These microcolumns (also known as minicolumns), are formed by small, vertical columns of neurons that can span all layers in the gray matter. Although correlative studies have established that microcolumns can lose their primary characteristics in normal aging, neurological, and neurodegenerative diseases, the exact function of these structures has not been established. Using computer simulations of highly detailed neuronal networks, we study whether there is a functional advantage in neuronal networks with a microcolumnar geometry as compared to other geometrically distributed networks. In particular, these simulations take into account microcolumnar, crystalline, and random distributions of neurons. At the assigned location of each neuron, we position unique, geometrically precise neurons and determine synaptic connectivity of the networks via three models: fully-connected, gaussian, and hypergeometric connectivity models, taking into account the physical distances between the neurons. By utilizing the neuronal simulation package NEURON, we perform functional tests on these neuronal networks, such as information maintenance and scaling effects of network growth. Results on the advantages and disadvantages of each network topology are presented, and arguments are formulated that could explain microcolumnar neuronal networks as a natural evolution to maximize information processing in the brain.

In vivo, proteins and peptides are exposed to radically different environments than those in bulk. Because of the abundance of other cellular components, proteins perform their function in crowded and confined spaces. Confinement has been shown to alter the structure, dynamics, and folding of proteins that possess a native fold. Little is known, however, of the effects of confinement on biologically important intrinsically disordered proteins or peptides (IDP). Here, we use extensive molecular dynamics simulations to investigate the effects of confinement in an IDP, the Aβ21-30, a central folding nucleus of the full length amyloid β-protein. In this study, we report results derived from 107 μs of molecular dynamics simulations that subjected the Aβ21-30 to two types of confinement: hydrophilic and hydrophobic pores. Results show that turn structures are enhanced as a function of decreasing pore size (increasing confinement) over other structures, including coils, β-hairpins, and bridges. However, the percentage occurrence of the dominant hydrogen bond between amino acids Asp23 and Ser26 shown to stabilize the turn in bulk simulations does not increase as a function of confinement signifying a disconnect between structure and internal hydrogen bonding. Differences in structure and dynamics of the decapeptide due to hydrophilic and hydrophobic confinement are more apparent at the extreme confinement conditions, where a reduction of the available phase space in hydrophilic confinement is explained in terms of interactions between the decapeptide and a layer of water at the interface between the decapeptide and the surface of the pore, and a smaller size of the decapeptide in the hydrophobic pores is rationalized in terms of peptide-surface interactions.