Calcium homeostasis and modulation of synaptic plasticity in the aged brain

The level of intracellular Ca2+ plays a central role in normal and pathological signaling within and between neurons. These processes involve a cascade of events for locally raising and lowering cytosolic Ca2+. As the mechanisms for age-related alteration in Ca2+ dysregulation have been illuminated, hypotheses concerning Ca2+ homeostasis and brain aging have been modified. The idea that senescence is due to pervasive cell loss associated with elevated resting Ca2+ has been replaced by concepts concerning changes in local Ca2+ levels associated with neural activity. This article reviews evidence for a shift in the sources of intracellular Ca2+ characterized by a diminished role for N-methyl-D-aspartate receptors and an increased role for intracellular stores and voltage-dependent Ca2+ channels. Physiological and biological models are outlined, which relate a shift in Ca2+ regulation with changes in cell excitability and synaptic plasticity, resulting in a functional lesion of the hippocampus.     

Spike-timing-dependent BDNF secretion and synaptic plasticity

In acute hippocampal slices, we found that the presence of extracellular brain-derived neurotrophic factor (BDNF) is essential for the induction of spike-timing-dependent long-term potentiation (tLTP). To determine whether BDNF could be secreted from postsynaptic dendrites in a spike-timing-dependent manner, we used a reduced system of dissociated hippocampal neurons in culture. Repetitive pairing of iontophoretically applied glutamate pulses at the dendrite with neuronal spikes could induce persistent alterations of glutamate-induced responses at the same dendritic site in a manner that mimics spike-timing-dependent plasticity (STDP)—the glutamate-induced responses were potentiated and depressed when the glutamate pulses were applied 20 ms before and after neuronal spiking, respectively. By monitoring changes in the green fluorescent protein (GFP) fluorescence at the dendrite of hippocampal neurons expressing GFP-tagged BDNF, we found that pairing of iontophoretic glutamate pulses with neuronal spiking resulted in BDNF secretion from the dendrite at the iontophoretic site only when the glutamate pulses were applied within a time window of approximately 40 ms prior to neuronal spiking, consistent with the timing requirement of synaptic potentiation via STDP. Thus, BDNF is required for tLTP and BDNF secretion could be triggered in a spike-timing-dependent manner from the postsynaptic dendrite.     

A model for synaptic development regulated by NMDA receptor subunit expression

Activation of NMDA receptors (NMDARs) is highly involved in the potentiation and depression of synaptic transmission. NMDARs comprise NR1 and NR2B subunits in the neonatal forebrain, while the expression of NR2A subunit is increased over time, leading to shortening of NMDAR-mediated synaptic currents. It has been suggested that the developmental switch in the NMDAR subunit composition regulates synaptic plasticity, but its physiological role remains unclear. In this study, we examine the effects of the NMDAR subunit switch on the spike-timing-dependent plasticity and the synaptic weight dynamics and demonstrate that the subunit switch contributes to inducing two consecutive processes—the potentiation of weak synapses and the induction of the competition between them—at an adequately rapid rate. Regulation of NMDAR subunit expression can be considered as a mechanism that promotes rapid and stable growth of immature synapses. Action Editor: Upinder Bhalla     

Stability of complex spike timing-dependent plasticity in cerebellar learning

Dynamics of spike-timing dependent synaptic plasticity are analyzed for excitatory and inhibitory synapses onto cerebellar Purkinje cells. The purpose of this study is to place theoretical constraints on candidate synaptic learning rules that determine the changes in synaptic efficacy due to pairing complex spikes with presynaptic spikes in parallel fibers and inhibitory interneurons. Constraints are derived for the timing between complex spikes and presynaptic spikes, constraints that result from the stability of the learning dynamics of the learning rule. Potential instabilities in the parallel fiber synaptic learning rule are found to be stabilized by synaptic plasticity at inhibitory synapses if the inhibitory learning rules are stable, and conditions for stability of inhibitory plasticity are given. Combining excitatory with inhibitory plasticity provides a mechanism for minimizing the overall synaptic input. Stable learning rules are shown to be able to sculpt simple-spike patterns by regulating the excitability of neurons in the inferior olive that give rise to climbing fibers.     

Alternative roles for Cdk5 in learning and synaptic plasticity

Protein kinases mediate the intracellular signal transduction pathways controlling synaptic plasticity in the central nervous system. While the majority of protein kinases achieve this function via the phosphorylation of synaptic substrates, some kinases may contribute through alternative mechanisms in addition to enzymatic activity. There is growing evidence that protein kinases may often play structural roles in plasticity as well. Cyclin-dependent kinase 5 (Cdk5) has been implicated in learning and synaptic plasticity. Initial scrutiny focused on its enzymatic activity using pharmacological inhibitors and genetic modifications of Cdk5 cofactors. Quite recently Cdk5 has been shown to govern learning and plasticity via regulation of glutamate receptor degradation, a function that may not dependent on phosphorylation of downstream effectors. From these new studies, two roles emerge for Cdk5 in plasticity: one in which it controls structural plasticity via phosphorylation of synaptic substrates, and a second where it regulates functional plasticity via protein-protein interactions.     

Neural morphogenesis, synaptic plasticity, and evolution

Summary Morphology plays an important role in the computational properties of neural systems, affecting both their functionality and the way in which this functionality is developed during life. In computer-based models of neural networks, artificial evolution is often used as a method to explore the space of suitable morphologies. In this paper we critically review the most common methods used to evolve neural morphologies and argue that a more effective, and possibly biologically plausible, method consists of genetically encoding rules of synaptic plasticity along with rules of neural morphogenesis. Some preliminary experiments with autonomous robots are described in order to show the feasibility and advantages of the approach.     

Phenomenological models of synaptic plasticity based on spike timing

Synaptic plasticity is considered to be the biological substrate of learning and memory. In this document we review phenomenological models of short-term and long-term synaptic plasticity, in particular spike-timing dependent plasticity (STDP). The aim of the document is to provide a framework for classifying and evaluating different models of plasticity. We focus on phenomenological synaptic models that are compatible with integrate-and-fire type neuron models where each neuron is described by a small number of variables. This implies that synaptic update rules for short-term or long-term plasticity can only depend on spike timing and, potentially, on membrane potential, as well as on the value of the synaptic weight, or on low-pass filtered (temporally averaged) versions of the above variables. We examine the ability of the models to account for experimental data and to fulfill expectations derived from theoretical considerations. We further discuss their relations to teacher-based rules (supervised learning) and reward-based rules (reinforcement learning). All models discussed in this paper are suitable for large-scale network simulations.     

Strength training during pregnancy influences hippocampal plasticity but not body development in neonatal rats

Objective:To describe the effects of strength exercise practice during pregnancy on the offspring’s development parameters: growth and motor performance, hippocampal neuroplasticity, and stress levels.Methods:Pregnant Wistar rats were divided into two groups: sedentary and exercised rats. Exercised pregnant rats were subjected to a strength training protocol (vertical ladder climbing) throughout the gestational period. Male offspring’s body weight, length, and head size were evaluated during the neonatal period (postnatal days [P]2–P21), as well as motor milestones during P0–P8. At P8, a set of male pups were subjected to global hippocampal DNA methylation, hippocampal cell proliferation, and plasma corticosterone concentration.Results:Offspring from trained mothers presented a transient change in body morphometric evaluations, no differences in milestone assessments, enhancement of cell proliferation in the dentate gyrus of the hippocampus, and decreased global hippocampal DNA methylation compared with the offspring from sedentary mothers. Furthermore, strength training during pregnancy did not change the corticosterone concentration of exercised mothers and their offspring.Conclusions:These data indicate that strength training can protect offspring’s development and could impact positively on parameters linked to cognitive function. This study provides a greater understanding of the effects of strength exercise practiced during pregnancy on the offspring’s health.     

与突触可塑性相关的神经振荡和信息流研究

作为一种有节律的神经活动,神经振荡现象发生在所有的神经系统中,例如大脑皮层、海马、皮层下神经核团以及感觉器官.本综述首先给出了已有的研究结果,即基于theta和gamma频段的同步神经振荡揭示了认知过程的起源与本质,如学习与记忆.然后介绍了关于神经振荡分析的新技术和算法,如表征神经元突触可塑性的神经信息流方向指数,并例...     

Effects of Poisson noise in a IF model with STDP and spontaneous replay of periodic spatiotemporal patterns,in absence of cue stimulation

We consider a network of leaky integrate and fire neurons, whose learning mechanism is based on the Spike-Timing-Dependent Plasticity. The spontaneous temporal dynamic of the system is studied, including its storage and replay properties, when a Poissonian noise is added to the post-synaptic potential of the units. The temporal patterns stored in the network are periodic spatiotemporal patterns of spikes. We observe that, even in absence of a cue stimulation, the spontaneous dynamics induced by the noise is a sort of intermittent replay of the patterns stored in the connectivity and a phase transition between a replay and non-replay regime exists at a critical value of the spiking threshold. We characterize this transition by measuring the order parameter and its fluctuations.     

A neural circuit model of emotional learning using two pathways with different granularity and speed of information processing

We propose a neural circuit model of emotional learning using two pathways with different granularity and speed of information processing. In order to derive a precise time process, we utilized a spiking model neuron proposed by Izhikevich and spike-timing-dependent synaptic plasticity (STDP) of both excitatory and inhibitory synapses. We conducted computer simulations to evaluate the proposed model. We demonstrate some aspects of emotional learning from the perspective of the time process. The agreement of the results with the previous behavioral experiments suggests that the structure and learning process of the proposed model are appropriate.     

Derivation of a field equation of brain activity

We present a nonlinear field theory of the brain under realistic anatomical connectivity conditions describing the interaction between functional units within the brain. This macroscopic field theory is derived from the quasi-microscopic conversion properties of neural populations occurring at synapses and somas. Functional units are treated as inhomogeneities within a nonlinear neural tissue.     

Neural network modeling and control of type 1 diabetes mellitus

This paper presents a developed and validated dynamic simulation model of type 1 diabetes, that simulates the progression of the disease and the two term controller that is responsible for the insulin released to stabilize the glucose level. The modeling and simulation of type 1 diabetes mellitus is based on an artificial neural network approach. The methodology builds upon an existing rich database on the progression of type 1 diabetes for a group of diabetic patients. The model was found to perform well at estimating the next glucose level over time without control. A neural controller that mimics the pancreas secretion of insulin into the body was also developed. This controller is of the two term type: one stage is responsible for short-term and the other for mid-term insulin delivery. It was found that the controller designed predicts an adequate amount of insulin that should be delivered into the body to obtain a normalization of the elevated glucose level. This helps to achieve the main objective of insulin therapy: to obtain an accurate estimate of the amount of insulin to be delivered in order to compensate for the increase in glucose concentration.     

Bidirectional synaptic mechanisms of ocular dominance plasticity in visual cortex

As in other mammals with binocular vision, monocular lid suture in mice induces bidirectional plasticity: rapid weakening of responses evoked through the deprived eye followed by delayed strengthening of responses through the open eye. It has been proposed that these bidirectional changes occur through three distinct processes: first, deprived-eye responses rapidly weaken through homosynaptic long-term depression (LTD); second, as the period of deprivation progresses, the modification threshold determining the boundary between synaptic depression and synaptic potentiation becomes lower, favouring potentiation; and third, facilitated by the decreased modification threshold, open-eye responses are strengthened via homosynaptic long-term potentiation (LTP). Of these processes, deprived-eye depression has received the greatest attention, and although several alternative hypotheses are also supported by current research, evidence suggests that alpha-amino-3- hydroxy-5-methyl-4-isoxazolepropionate (AMPA) receptor endocytosis through LTD is a key mechanism. The change in modification threshold appears to occur partly through changes in N-methyl-D-aspartate (NMDA) receptor subunit composition, with decreases in the ratio of NR2A to NR2B facilitating potentiation. Although limited research has directly addressed the question of open-eye potentiation, several studies suggest that LTP could account for observed changes in vivo. This review will discuss evidence supporting this three-stage model, along with outstanding issues in the field.     

Kinetic models of spike-timing dependent plasticity and their functional consequences in detecting correlations

Spike-timing dependent plasticity (STDP) is a type of synaptic modification found relatively recently, but the underlying biophysical mechanisms are still unclear. Several models of STDP have been proposed, and differ by their implementation, and in particular how synaptic weights saturate to their minimal and maximal values. We analyze here kinetic models of transmitter-receptor interaction and derive a series of STDP models. In general, such kinetic models predict progressive saturation of the weights. Various forms can be obtained depending on the hypotheses made in the kinetic model, and these include a simple linear dependence on the value of the weight (“soft bounds”), mixed soft and abrupt saturation (“hard bound”), or more complex forms. We analyze in more detail simple soft-bound models of Hebbian and anti-Hebbian STDPs, in which nonlinear spike interactions (triplets) are taken into account. We show that Hebbian STDPs can be used to selectively potentiate synapses that are correlated in time, while anti-Hebbian STDPs depress correlated synapses, despite the presence of nonlinear spike interactions. This correlation detection enables neurons to develop a selectivity to correlated inputs. We also examine different versions of kinetics-based STDP models and compare their sensitivity to correlations. We conclude that kinetic models generally predict soft-bound dynamics, and that such models seem ideal for detecting correlations among large numbers of inputs.     

The biochemical basis of synaptic plasticity and neurocomputation: a new theory.

The recent finding that dendritic spines (on which 90% of all excitatory synapses on pyramidal cells are formed) are not permanent structures but are continually being formed and adsorbed has implications for the present theoretical basis of neurocomputation, which is largely based on the concept of fixed nerve nets. This evidence would tend to support the recent theories of Edelman, Freeman, Globus, Pribram and others that neuronal networks in the brain operate mainly as nonlinear dynamic, chaotic systems. This paper presents a hypothesis of a possible neurochemical mechanism underlying this synaptic plasticity based on reactive oxygen species and toxic 0-semiquinones derived from catecholamines (i) by the enzyme prostaglandin H synthetase induced by glutamatergic NMDA receptor activation and (ii) by reactive nitrogen species derived from nitric oxide in a low ascorbate environment. A key factor in this neuromodulation may be the fact that catecholamines are potent antioxidants and free radical scavengers and are thus able to affect the redox mediated balance at the glutamate receptors between synapse formation and synapse removal that may be a key factor in neurocomputational plasticity. But catecholamines are also easily oxidized to neurotoxic 0-semiquinones and this may be relevant to the pathology of several diseases including schizophrenia. The relationship between dopamine release and positive reinforcement is relevant to this hypothesis.     

General mean‐field theory for mechanical hysteresis of network‐like macromolecules

In the framework of the mean‐field theory for the order parameter, which characterizes the degree of deviating the structure of a network‐like macromolecule from its natural state under the action of the external force, its mechanical hysteresis is considered. Elastic hysterical properties of a macromolecule are studied as a function of the applied force, temperature and some other parameters controlling the viscous‐elastic behavior of network‐like macromolecules. Proteins 2014; 82:3188–3193. © 2014 Wiley Periodicals, Inc.     

Training theory and taper: validation in triathlon athletes

This paper defines a training theory with which to predict the effectiveness of various formats of taper in optimizing physical performance from a standardized period of training and taper. Four different taper profiles: step reduction vs exponential (exp) decay and fast vs slow exp decay tapers, were simulated in a systems model to predict performance p(t) resulting from a standard square-wave quantity of training for 28 days. The relative effectiveness of each of the profiles in producing optimal physical improvement above pre-taper criterion physical test standards (running and cycle ergometry) was determined. Simulation showed that an exp taper was better than a step-reduction taper, and a fast exp decay taper was superior to a slow exp decay taper. The results of the simulation were tested experimentally in field trials to assess the correspondence between simulation and real-training criterion physical tests in triathlon athletes. The results showed that the exp taper (tau = 5 days) group made a significantly greater improvement above a pre-taper standard (P < or = 0.05) than the step-reduction taper group in cycle ergometry, and was better, but not significantly so, in a 5-km run. A fast exp taper group B (tau = 4 days) performed significantly better (P < or = 0.05) in maximal, cycle ergometry above a pre-taper training standard than a slow exp taper group A (tau = 8 days) and was improved more, but not significantly so, than group A in a 5-km criterion run. The mean improvement on both physical tests by exp decay taper groups all increased significantly (P < or = 0.05) above their pre-taper training standard. Maximum oxygen uptake increased significantly in a group of eight remaining athletes during 2 weeks of final taper after three athletes left early for final preparations at the race site.