锥体神经元的连接模式对突触后局部电位的依赖

脑皮层的功能连接模式与突触可塑性密切相关,受突触空间分布和刺激模式等多种因素的影响。尽管越来越多的证据表明突触可塑性不仅受突触后动作电位而且还受突触后局部树突电位的影响,但是目前尚不清楚神经元的功能连接模式是否和怎样依赖于突触后局部电位的。为此,本文建立了一个无需硬边界设置的、突触后局部膜电位依赖的可塑性模型。该模型具有突触强度的自平衡能力并且能够再现多种突触可塑性实验结果。基于该模型对两个锥体神经元的功能连接模式进行仿真的结果表明,当突触后局部电位都处于亚阈值时两个神经元无功能连接,如果一个神经元的突触后膜电位高于阈值电位则产生向该神经元的单向连接,当两个神经元的突触后膜电位都超过阈值电位时则产生双向连接,说明突触后局部膜电位分布是神经元功能连接模式形成的关键。研究结果加深了神经网络连接模式形成机制的理解,对学习和记忆的研究具有重要意义。     

Developmental changes in the brain-stem serotonergic nuclei of teleost fish and neural plasticity

Summary 1. During early ontogeny, the serotonergic neurons in the brain stem of the three-spined stickleback shows a temporal and spatial developmental pattern that closely resembles that of amniotes.2. However, in the adult fish, only the midline nuclei of the rostral group (dorsal and median raphe nuclei) and the dorsal lateral tegmental nucleus are consistently serotonin-immunoreactive (5-HTir), whereas the groups of the upper and lower rhombencephalon (raphe pontis, raphe magnus, and raphe pallidus/obscurus nuclei) are variable and, when present, contain relatively small numbers of 5-HTir neurons.3. Using specific antisera against tryptophan 5-hydroxylase and aromaticl-amino acid decarboxylase, we have shown that the lateral B9 group and the groups of the upper and lower rhombencephalon are consistently present in adult sticklebacks. The results are discussed in relation to other known instances of neurotransmitter plasticity or transient neurotransmitter expression in teleost fish.4. While there are several instances of transient expression of neurotransmitter markers by discrete neuronal populations, there is so far no evidence of changes from one neurotransmitter phenotype to another in the brain of teleost fish. However, there are indications of plasticity of expression of catecholamines and indoleamines, and their respective synthesizing enzymes, as reflected in age-dependent changes and variation between individuals of different physiological status.5. As the brain grows continuously in teleost fish, and new neurons are added from proliferative regions, synaptic connections may be expected to undergo remodeling in all brain regions throughout life. Thus, the teleostean brain may be considered a suitable model for experimental studies of different aspects of neural plasticity.     

Role of glia-derived cholesterol in synaptogenesis: new revelations in the synapse-glia affair.

Brain development and function relies on the exchange of signals between neurons and glial cells. Here we review a series of recent studies on cultures of purified retinal ganglion cells (RGCs) that point to a new role of glial cells in the formation and plasticity of synaptic connections. The results suggest that neurons must import glia-derived cholesterol via lipoproteins to form numerous and efficient synaptic connections. This finding may explain why throughout the central nervous system (CNS) the main phase of synaptogenesis starts synchronously after glia differentiation and why astrocytes produce apolipoprotein E (apoE) and cholesterol-containing lipoproteins. Experimental tests of these hypotheses may further our understanding of the cholesterol metabolism in the brain and may help to explain neurologic symptoms resulting from defective cholesterol and lipoprotein metabolism.     

Target neuron specification of short-term synaptic facilitation and depression in the cricket CNS

We investigated the role of retrograde signals in the regulation of short-term synaptic depression and facilitation by characterizing the form of plasticity expressed at novel synapses on four giant interneurons in the cricket cercal sensory system. We induced the formation of novel synapses by transplanting a mesothoracic leg and its associated sensory neurons to the cricket terminal abdominal segment. Axons of ectopic leg sensory neurons regenerated and innervated the host terminal abdominal ganglion forming monosynaptic connections with the medial giant interneuron (MGI), lateral giant interneuron (LGI), and interneurons 7-1a and 9-2a. The plasticity expressed by these synapses was characterized by stimulating a sensory neuron with pairs of stimuli at various frequencies or with trains of 10 stimuli delivered at 100 Hz and measuring the change in excitatory postsynaptic potential amplitude recorded in the postsynaptic neuron. Novel synapses of a leg tactile hair on 7-1a depressed, as did control synapses of cercal sensory neurons on this interneuron. Novel synapses of leg campaniform sensilla (CS) sensory neurons on MGI, like MGI's control synapses, always facilitated. The form of plasticity expressed by novel synapses is thus consistent with that observed at control synapses. Leg CS synapses with 9-2a also facilitated; however, the plasticity expressed by these sensory neurons is dependent on the identity of the postsynaptic cell since the synapses these same sensory neurons formed with LGI always depressed. We conclude that the form of plasticity expressed at these synaptic connections is determined retrogradely by the postsynaptic cell. © 1998 John Wiley & Sons, Inc. J Neurobiol 37: 700–714, 1998     

Developmental study of rat vestibular neuronal circuits during a spaceflight of 17 days

The aim of this study was to investigate the potential plasticity of the vestibular system, in structural and biochemical terms, at the level of the gravity receptors (the sensory hair cells), the primary neurons relaying the sensory signals (the vestibular ganglion neurons) and their projections into the vestibular nuclei. We studied the biochemical differentiation of the sensory cells and of the vestibular ganglion by investigating which calcium-binding proteins were present. We studied the development of peripheral synaptic connections of the efferent system by investigating the distribution of CGRP (calcitonin-gene related-peptide) and we also studied the cerebellar synaptic connections in the vestibular nuclei, as identified by the presence of calbindin. Putative changes were studied after a 17-day episode of microgravity (Neurolab STS-90), in developing rats between postnatal days 8 and 25. The extent to which these changes could be caused by alterations in gravity was determined by examining sensory and nervous structures not involved in gravity detection, the cochlea and the cochlear nuclei.     

一个基于能量的突触可塑性模型

研究表明能量可能是支配神经元活动的统一原则,编码能力与能量成本的比率最大化被认为是突触连接在选择性压力下改变的关键原则之一,这意味着突触范围内能量的变化与突触可塑性有关。为此,建立一个基于能量的突触可塑性模型。当突触后膜瞬时功率高于功率阈值时突触权重增加,反之突触权重下降。该模型可再现脉冲频率依赖可塑性以及脉冲时间依赖可塑性这两种主要的突触可塑性实验结果,并且和其他公认的突触可塑性模型相比具有优越性。结果表明,能量是影响突触可塑性的关键因素,对进一步理解突触连接的选择性和神经网络动力学特征提供了一个新思路。     

Role of GABAA-Mediated Inhibition and Functional Assortment of Synapses onto Individual Layer 4 Neurons in Regulating Plasticity Expression in Visual Cortex

Layer 4 (L4) of primary visual cortex (V1) is the main recipient of thalamocortical fibers from the dorsal lateral geniculate nucleus (LGN_d). Thus, it is considered the main entry point of visual information into the neocortex and the first anatomical opportunity for intracortical visual processing before information leaves L4 and reaches supra- and infragranular cortical layers. The strength of monosynaptic connections from individual L4 excitatory cells onto adjacent L4 cells (unitary connections) is highly malleable, demonstrating that the initial stage of intracortical synaptic transmission of thalamocortical information can be altered by previous activity. However, the inhibitory network within L4 of V1 may act as an internal gate for induction of excitatory synaptic plasticity, thus providing either high fidelity throughput to supragranular layers or transmittal of a modified signal subject to recent activity-dependent plasticity. To evaluate this possibility, we compared the induction of synaptic plasticity using classical extracellular stimulation protocols that recruit a combination of excitatory and inhibitory synapses with stimulation of a single excitatory neuron onto a L4 cell. In order to induce plasticity, we paired pre- and postsynaptic activity (with the onset of postsynaptic spiking leading the presynaptic activation by 10ms) using extracellular stimulation (ECS) in acute slices of primary visual cortex and comparing the outcomes with our previously published results in which an identical protocol was used to induce synaptic plasticity between individual pre- and postsynaptic L4 excitatory neurons. Our results indicate that pairing of ECS with spiking in a L4 neuron fails to induce plasticity in L4-L4 connections if synaptic inhibition is intact. However, application of a similar pairing protocol under GABA_ARs inhibition by bath application of 2μM bicuculline does induce robust synaptic plasticity, long term potentiation (LTP) or long term depression (LTD), similar to our results with pairing of pre- and postsynaptic activation between individual excitatory L4 neurons in which inhibitory connections are not activated. These results are consistent with the well-established observation that inhibition limits the capacity for induction of plasticity at excitatory synapses and that pre- and postsynaptic activation at a fixed time interval can result in a variable range of plasticity outcomes. However, in the current study by virtue of having two sets of experimental data, we have provided a new insight into these processes. By randomly mixing the assorting of individual L4 neurons according to the frequency distribution of the experimentally determined plasticity outcome distribution based on the calculated convergence of multiple individual L4 neurons onto a single postsynaptic L4 neuron, we were able to compare then actual ECS plasticity outcomes to those predicted by randomly mixing individual pairs of neurons. Interestingly, the observed plasticity profiles with ECS cannot account for the random assortment of plasticity behaviors of synaptic connections between individual cell pairs. These results suggest that connections impinging onto a single postsynaptic cell may be grouped according to plasticity states.     

A trace of silence: memory and microRNA at the synapse

Identifying the neural circuits that mediate particular behaviors and uncovering their plasticity is an endeavor at the heart of neuroscience. This effort is allied with the elucidation of plasticity mechanisms, because the molecular determinants of plasticity can be markers for the neurons and synapses that are modified by experience. Of particular interest is protein synthesis localized to the synapse, which might establish and maintain the stable modification of neuronal properties, including the pattern and strength of synaptic connections. Recent studies reveal that microRNAs and the RISC pathway regulate synaptic protein synthesis. Is synaptic activity of the RISC pathway a molecular signature of memory?     

Effects of phase on homeostatic spike rates

Recent experimental results by Talathi et al. (Neurosci Lett 455:145–149, 2009) showed a divergence in the spike rates of two types of population spike events, representing the putative activity of the excitatory and inhibitory neurons in the CA1 area of an animal model for temporal lobe epilepsy. The divergence in the spike rate was accompanied by a shift in the phase of oscillations between these spike rates leading to a spontaneous epileptic seizure. In this study, we propose a model of homeostatic synaptic plasticity which assumes that the target spike rate of populations of excitatory and inhibitory neurons in the brain is a function of the phase difference between the excitatory and inhibitory spike rates. With this model of homeostatic synaptic plasticity, we are able to simulate the spike rate dynamics seen experimentally by Talathi et al. in a large network of interacting excitatory and inhibitory neurons using two different spiking neuron models. A drift analysis of the spike rates resulting from the homeostatic synaptic plasticity update rule allowed us to determine the type of synapse that may be primarily involved in the spike rate imbalance in the experimental observation by Talathi et al. We find excitatory neurons, particularly those in which the excitatory neuron is presynaptic, have the most influence in producing the diverging spike rates and causing the spike rates to be anti-phase. Our analysis suggests that the excitatory neuronal population, more specifically the excitatory to excitatory synaptic connections, could be implicated in a methodology designed to control epileptic seizures.     

Learning-Induced Synchronization and Plasticity of a Developing Neural Network

Learning-induced synchronization of a neural network at various developing stages is studied by computer simulations using a pulse-coupled neural network model in which the neuronal activity is simulated by a one-dimensional map. Two types of Hebbian plasticity rules are investigated and their differences are compared. For both models, our simulations show a logarithmic increase in the synchronous firing frequency of the network with the culturing time of the neural network. This result is consistent with recent experimental observations. To investigate how to control the synchronization behavior of a neural network after learning, we compare the occurrence of synchronization for four networks with different designed patterns under the influence of an external signal. The effect of such a signal on the network activity highly depends on the number of connections between neurons. We discuss the synaptic plasticity and enhancement effects for a random network after learning at various developing stages.     

Transient calcium and dopamine increase PKA activity and DARPP-32 phosphorylation

Reinforcement learning theorizes that strengthening of synaptic connections in medium spiny neurons of the striatum occurs when glutamatergic input (from cortex) and dopaminergic input (from substantia nigra) are received simultaneously. Subsequent to learning, medium spiny neurons with strengthened synapses are more likely to fire in response to cortical input alone. This synaptic plasticity is produced by phosphorylation of AMPA receptors, caused by phosphorylation of various signalling molecules. A key signalling molecule is the phosphoprotein DARPP-32, highly expressed in striatal medium spiny neurons. DARPP-32 is regulated by several neurotransmitters through a complex network of intracellular signalling pathways involving cAMP (increased through dopamine stimulation) and calcium (increased through glutamate stimulation). Since DARPP-32 controls several kinases and phosphatases involved in striatal synaptic plasticity, understanding the interactions between cAMP and calcium, in particular the effect of transient stimuli on DARPP-32 phosphorylation, has major implications for understanding reinforcement learning. We developed a computer model of the biochemical reaction pathways involved in the phosphorylation of DARPP-32 on Thr34 and Thr75. Ordinary differential equations describing the biochemical reactions were implemented in a single compartment model using the software XPPAUT. Reaction rate constants were obtained from the biochemical literature. The first set of simulations using sustained elevations of dopamine and calcium produced phosphorylation levels of DARPP-32 similar to that measured experimentally, thereby validating the model. The second set of simulations, using the validated model, showed that transient dopamine elevations increased the phosphorylation of Thr34 as expected, but transient calcium elevations also increased the phosphorylation of Thr34, contrary to what is believed. When transient calcium and dopamine stimuli were paired, PKA activation and Thr34 phosphorylation increased compared with dopamine alone. This result, which is robust to variation in model parameters, supports reinforcement learning theories in which activity-dependent long-term synaptic plasticity requires paired glutamate and dopamine inputs.     

Effect of stimulus-driven pruning on the detection of spatiotemporal patterns of activity in large neural networks

Adult patterns of neuronal connectivity develop from a transient embryonic template characterized by exuberant projections to both appropriate and inappropriate target regions in a process known as synaptic pruning. Trigger signals able to induce synaptic pruning could be related to dynamic functions that depend on the timing of action potentials. We stimulated locally connected random networks of spiking neurons and observed the effect of a spike-timing-dependent synaptic plasticity (STDP)-driven pruning process on the emergence of cell assemblies. The spike trains of the simulated excitatory neurons were recorded. We searched for spatiotemporal firing patterns as potential markers of the build-up of functionally organized recurrent activity associated with spatially organized connectivity.     

Spike-Timing Dependence of Structural Plasticity Explains Cooperative Synapse Formation in the Neocortex

Structural plasticity governs the long-term development of synaptic connections in the neocortex. While the underlying processes at the synapses are not fully understood, there is strong evidence that a process of random, independent formation and pruning of excitatory synapses can be ruled out. Instead, there must be some cooperation between the synaptic contacts connecting a single pre- and postsynaptic neuron pair. So far, the mechanism of cooperation is not known. Here we demonstrate that local correlation detection at the postsynaptic dendritic spine suffices to explain the synaptic cooperation effect, without assuming any hypothetical direct interaction pathway between the synaptic contacts. Candidate biomolecular mechanisms for dendritic correlation detection have been identified previously, as well as for structural plasticity based thereon. By analyzing and fitting of a simple model, we show that spike-timing correlation dependent structural plasticity, without additional mechanisms of cross-synapse interaction, can reproduce the experimentally observed distributions of numbers of synaptic contacts between pairs of neurons in the neocortex. Furthermore, the model yields a first explanation for the existence of both transient and persistent dendritic spines and allows to make predictions for future experiments.     

Neurotrophins as synaptic modulators

The role of neurotrophins as regulatory factors that mediate the differentiation and survival of neurons has been well described. More recent evidence indicates that neurotrophins may also act as synaptic modulators. Here, I review the evidence that synaptic activity regulates the synthesis, secretion and action of neurotrophins, which can in turn induce immediate changes in synaptic efficacy and morphology. By this account, neurotrophins may participate in activity-dependent synaptic plasticity, linking synaptic activity with long-term functional and structural modification of synaptic connections.     

Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity

In rodent visual cortex, synaptic connections between orientation-selective neurons are unspecific at the time of eye opening, and become to some degree functionally specific only later during development. An explanation for this two-stage process was proposed in terms of Hebbian plasticity based on visual experience that would eventually enhance connections between neurons with similar response features. For this to work, however, two conditions must be satisfied: First, orientation selective neuronal responses must exist before specific recurrent synaptic connections can be established. Second, Hebbian learning must be compatible with the recurrent network dynamics contributing to orientation selectivity, and the resulting specific connectivity must remain stable for unspecific background activity. Previous studies have mainly focused on very simple models, where the receptive fields of neurons were essentially determined by feedforward mechanisms, and where the recurrent network was small, lacking the complex recurrent dynamics of large-scale networks of excitatory and inhibitory neurons. Here we studied the emergence of functionally specific connectivity in large-scale recurrent networks with synaptic plasticity. Our results show that balanced random networks, which already exhibit highly selective responses at eye opening, can develop feature-specific connectivity if appropriate rules of synaptic plasticity are invoked within and between excitatory and inhibitory populations. If these conditions are met, the initial orientation selectivity guides the process of Hebbian learning and, as a result, functionally specific and a surplus of bidirectional connections emerge. Our results thus demonstrate the cooperation of synaptic plasticity and recurrent dynamics in large-scale functional networks with realistic receptive fields, highlight the role of inhibition as a critical element in this process, and paves the road for further computational studies of sensory processing in neocortical network models equipped with synaptic plasticity.     

Intrinsic versus extrinsic influences in the development of neuronal maps

Accumulating evidence suggests that the plasticity of extrinsic thalamocortical inputs in cortical layer IV may be guided or instructed by earlier plasticity events in the intrinsic, horizontal connections within the extragranular cortical layers. We analyse a rate-based model of the plasticity of a set of extrinsic afferents in the presence of a pre-existing (and fixed) plexus of intrinsic, overall excitatory horizontal connections between a set of target neurons. We determine conditions under which afferent synaptic pattern formation respects this pre-existing lateral structure. We find three broad regimes under which extrinsic afferent plasticity may violate this structure: the initial pattern of extrinsic afferent innervation of the target cells is far from balanced; the gain of the extrinsic afferents greatly exceeds the overall scale of the strength of lateral excitation; the target cell horizontal coupling matrix is sparse. If none of these conditions is satisfied, then extrinsic afferent plasticity respects the pre-existing lateral connectivity, so that afferent synaptic pattern formation conforms to the pattern of lateral excitation.     

Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons

The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field’s Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks.     

Network Self-Organization Explains the Statistics and Dynamics of Synaptic Connection Strengths in Cortex

The information processing abilities of neural circuits arise from their synaptic connection patterns. Understanding the laws governing these connectivity patterns is essential for understanding brain function. The overall distribution of synaptic strengths of local excitatory connections in cortex and hippocampus is long-tailed, exhibiting a small number of synaptic connections of very large efficacy. At the same time, new synaptic connections are constantly being created and individual synaptic connection strengths show substantial fluctuations across time. It remains unclear through what mechanisms these properties of neural circuits arise and how they contribute to learning and memory. In this study we show that fundamental characteristics of excitatory synaptic connections in cortex and hippocampus can be explained as a consequence of self-organization in a recurrent network combining spike-timing-dependent plasticity (STDP), structural plasticity and different forms of homeostatic plasticity. In the network, associative synaptic plasticity in the form of STDP induces a rich-get-richer dynamics among synapses, while homeostatic mechanisms induce competition. Under distinctly different initial conditions, the ensuing self-organization produces long-tailed synaptic strength distributions matching experimental findings. We show that this self-organization can take place with a purely additive STDP mechanism and that multiplicative weight dynamics emerge as a consequence of network interactions. The observed patterns of fluctuation of synaptic strengths, including elimination and generation of synaptic connections and long-term persistence of strong connections, are consistent with the dynamics of dendritic spines found in rat hippocampus. Beyond this, the model predicts an approximately power-law scaling of the lifetimes of newly established synaptic connection strengths during development. Our results suggest that the combined action of multiple forms of neuronal plasticity plays an essential role in the formation and maintenance of cortical circuits.     

Identifying and Tracking Simulated Synaptic Inputs from Neuronal Firing: Insights from In Vitro Experiments

Accurately describing synaptic interactions between neurons and how interactions change over time are key challenges for systems neuroscience. Although intracellular electrophysiology is a powerful tool for studying synaptic integration and plasticity, it is limited by the small number of neurons that can be recorded simultaneously in vitro and by the technical difficulty of intracellular recording in vivo. One way around these difficulties may be to use large-scale extracellular recording of spike trains and apply statistical methods to model and infer functional connections between neurons. These techniques have the potential to reveal large-scale connectivity structure based on the spike timing alone. However, the interpretation of functional connectivity is often approximate, since only a small fraction of presynaptic inputs are typically observed. Here we use in vitro current injection in layer 2/3 pyramidal neurons to validate methods for inferring functional connectivity in a setting where input to the neuron is controlled. In experiments with partially-defined input, we inject a single simulated input with known amplitude on a background of fluctuating noise. In a fully-defined input paradigm, we then control the synaptic weights and timing of many simulated presynaptic neurons. By analyzing the firing of neurons in response to these artificial inputs, we ask 1) How does functional connectivity inferred from spikes relate to simulated synaptic input? and 2) What are the limitations of connectivity inference? We find that individual current-based synaptic inputs are detectable over a broad range of amplitudes and conditions. Detectability depends on input amplitude and output firing rate, and excitatory inputs are detected more readily than inhibitory. Moreover, as we model increasing numbers of presynaptic inputs, we are able to estimate connection strengths more accurately and detect the presence of connections more quickly. These results illustrate the possibilities and outline the limits of inferring synaptic input from spikes.