Local Synaptic Plasticity Research Articles

Large-Scale Persistent Network Reconfiguration Induced by Ketamine in Anesthetized Monkeys: Relevance to Mood Disorders

BackgroundKetamine is a highly attractive candidate for developing fast-onset antidepressant agents; however, the relevant brain circuits that underlie sustained, efficacious antidepressant effects remain largely unknown. MethodsWe used a holistic scheme combining whole-brain resting-state fMRI and graph theoretical analysis to examine the sustained effects on brain networks after administration of a single dose of ketamine and to identify the brain regions and circuits preferentially targeted by ketamine. Topological differences in functional networks of anesthetized macaque monkeys were compared between ketamine (.5 mg/kg) and saline treatment after 18 hours. ResultsWe observed persistent global reconfiguration of small-world properties in response to ketamine intake, accompanied by large-scale downregulation of functional connectivity, most prominently in the orbital prefrontal cortex, the subgenual and posterior cingulate cortices, and the nucleus accumbens. Intriguingly, intrinsic connectivity with the medial prefrontal areas in the reward circuits were selectively downregulated. Global and regional regulations of the brain networks precisely opposed the maladaptive alterations in the depressed brain. ConclusionsOur results demonstrated that local synaptic plasticity triggered by blockade of N-methyl-D-aspartic acid receptors was capable of translating into prolonged network reconfiguration in the distributed cortico-limbic-striatal circuit, providing mechanistic insight into developing specific loci or circuit-targeted, long-term therapeutics.

Fear extinction as a model for synaptic plasticity in major depressive disorder.

BackgroundThe neuroplasticity hypothesis of major depressive disorder proposes that a dysfunction of synaptic plasticity represents a basic pathomechanism of the disorder. Animal models of depression indicate enhanced plasticity in a ventral emotional network, comprising the amygdala. Here, we investigated fear extinction learning as a non-invasive probe for amygdala-dependent synaptic plasticity in patients with major depressive disorder and healthy controls.MethodsDifferential fear conditioning was measured in 37 inpatients with severe unipolar depression (International Classification of Diseases, 10th revision, criteria) and 40 healthy controls. The eye-blink startle response, a subcortical output signal that is modulated by local synaptic plasticity in the amygdala in fear acquisition and extinction learning, was recorded as the primary outcome parameter.ResultsAfter robust and similar fear acquisition in both groups, patients with major depressive disorder showed significantly enhanced fear extinction learning in comparison to healthy controls, as indicated by startle responses to conditioned stimuli. The strength of extinction learning was positively correlated with the total illness duration.ConclusionsThe finding of enhanced fear extinction learning in major depressive disorder is consistent with the concept that the disorder is characterized by enhanced synaptic plasticity in the amygdala and the ventral emotional network. Clinically, the observation emphasizes the potential of successful extinction learning, the basis of exposure therapy, in anxiety-related disorders despite the frequent comorbidity of major depressive disorder.

Inhibitory Interneurons Decorrelate Excitatory Cells to Drive Sparse Code Formation in a Spiking Model of V1

Sparse coding models of natural scenes can account for several physiological properties of primary visual cortex (V1), including the shapes of simple cell receptive fields (RFs) and the highly kurtotic firing rates of V1 neurons. Current spiking network models of pattern learning and sparse coding require direct inhibitory connections between the excitatory simple cells, in conflict with the physiological distinction between excitatory (glutamatergic) and inhibitory (GABAergic) neurons (Dale's Law). At the same time, the computational role of inhibitory neurons in cortical microcircuit function has yet to be fully explained. Here we show that adding a separate population of inhibitory neurons to a spiking model of V1 provides conformance to Dale's Law, proposes a computational role for at least one class of interneurons, and accounts for certain observed physiological properties in V1. When trained on natural images, this excitatory-inhibitory spiking circuit learns a sparse code with Gabor-like RFs as found in V1 using only local synaptic plasticity rules. The inhibitory neurons enable sparse code formation by suppressing predictable spikes, which actively decorrelates the excitatory population. The model predicts that only a small number of inhibitory cells is required relative to excitatory cells and that excitatory and inhibitory input should be correlated, in agreement with experimental findings in visual cortex. We also introduce a novel local learning rule that measures stimulus-dependent correlations between neurons to support "explaining away" mechanisms in neural coding.

Rapid, parallel path planning by propagating wavefronts of spiking neural activity

Efficient path planning and navigation is critical for animals, robotics, logistics and transportation. We study a model in which spatial navigation problems can rapidly be solved in the brain by parallel mental exploration of alternative routes using propagating waves of neural activity. A wave of spiking activity propagates through a hippocampus-like network, altering the synaptic connectivity. The resulting vector field of synaptic change then guides a simulated animal to the appropriate selected target locations. We demonstrate that the navigation problem can be solved using realistic, local synaptic plasticity rules during a single passage of a wavefront. Our model can find optimal solutions for competing possible targets or learn and navigate in multiple environments. The model provides a hypothesis on the possible computational mechanisms for optimal path planning in the brain, at the same time it is useful for neuromorphic implementations, where the parallelism of information processing proposed here can fully be harnessed in hardware.

Branch-Specific Plasticity Enables Self-Organization of Nonlinear Computation in Single Neurons

It has been conjectured that nonlinear processing in dendritic branches endows individual neurons with the capability to perform complex computational operations that are needed to solve for example the binding problem. However, it is not clear how single neurons could acquire such functionality in a self-organized manner, because most theoretical studies of synaptic plasticity and learning concentrate on neuron models without nonlinear dendritic properties. In the meantime, a complex picture of information processing with dendritic spikes and a variety of plasticity mechanisms in single neurons has emerged from experiments. In particular, new experimental data on dendritic branch strength potentiation in rat hippocampus have not yet been incorporated into such models. In this article, we investigate how experimentally observed plasticity mechanisms, such as depolarization-dependent spike-timing-dependent plasticity and branch-strength potentiation, could be integrated to self-organize nonlinear neural computations with dendritic spikes. We provide a mathematical proof that, in a simplified setup, these plasticity mechanisms induce a competition between dendritic branches, a novel concept in the analysis of single neuron adaptivity. We show via computer simulations that such dendritic competition enables a single neuron to become member of several neuronal ensembles and to acquire nonlinear computational capabilities, such as the capability to bind multiple input features. Hence, our results suggest that nonlinear neural computation may self-organize in single neurons through the interaction of local synaptic and dendritic plasticity mechanisms.

Proteomics, Ultrastructure, and Physiology of Hippocampal Synapses in a Fragile X Syndrome Mouse Model Reveal Presynaptic Phenotype

Fragile X syndrome (FXS), the most common form of hereditary mental retardation, is caused by a loss-of-function mutation of the Fmr1 gene, which encodes fragile X mental retardation protein (FMRP). FMRP affects dendritic protein synthesis, thereby causing synaptic abnormalities. Here, we used a quantitative proteomics approach in an FXS mouse model to reveal changes in levels of hippocampal synapse proteins. Sixteen independent pools of Fmr1 knock-out mice and wild type mice were analyzed using two sets of 8-plex iTRAQ experiments. Of 205 proteins quantified with at least three distinct peptides in both iTRAQ series, the abundance of 23 proteins differed between Fmr1 knock-out and wild type synapses with a false discovery rate (q-value) <5%. Significant differences were confirmed by quantitative immunoblotting. A group of proteins that are known to be involved in cell differentiation and neurite outgrowth was regulated; they included Basp1 and Gap43, known PKC substrates, and Cend1. Basp1 and Gap43 are predominantly expressed in growth cones and presynaptic terminals. In line with this, ultrastructural analysis in developing hippocampal FXS synapses revealed smaller active zones with corresponding postsynaptic densities and smaller pools of clustered vesicles, indicative of immature presynaptic maturation. A second group of proteins involved in synaptic vesicle release was up-regulated in the FXS mouse model. In accordance, paired-pulse and short-term facilitation were significantly affected in these hippocampal synapses. Together, the altered regulation of presynaptically expressed proteins, immature synaptic ultrastructure, and compromised short-term plasticity points to presynaptic changes underlying glutamatergic transmission in FXS at this stage of development.

A Cognitive Model for Generalization during Sequential Learning

Traditional artificial neural network models of learning suffer fromcatastrophic interference. They are commonly trained to perform only one specific task, and, when trained on a new task, they forget the original task completely. It has been shown that the foundational neurocomputational principles embodied by the Leabra cognitive modeling framework, specifically fast lateral inhibition and a local synaptic plasticity model that incorporates both correlational and error-based components, are sufficient to largely overcome this limitation during the sequential learning of multiple motor skills. Evidence has also provided that Leabra is able to generalize the subsequences of motor skills, when doing so is appropriate. In this paper, we provide a detailed analysis of the extent of generalization possible with Leabra during sequential learning of multiple tasks. For comparison, we measure the generalization exhibited by the backpropagation of error learning algorithm. Furthermore, we demonstrate the applicability of sequential learning to a pair of movement tasks using a simulated robotic arm.

Erratum to: Self-influencing synaptic plasticity: recurrent changes of synaptic weights can lead to specific functional properties

Recent experimental results suggest that dendritic and back-propagating spikes can influence synaptic plasticity in different ways (Holthoff, 2004; Holthoff et al., 2005). In this study we investigate how these signals could interact at dendrites in space and time leading to changing plasticity properties at local synapse clusters. Similar to a previous study (Saudargiene et al., 2004) we employ a differential Hebbian learning rule to emulate spike-timing dependent plasticity and investigate how the interaction of dendritic and back-propagating spikes, as the post-synaptic signals, could influence plasticity. Specifically, we will show that local synaptic plasticity driven by spatially confined dendritic spikes can lead to the emergence of synaptic clusters with different properties. If one of these clusters can drive the neuron into spiking, plasticity may change and the now arising global influence of a back-propagating spike can lead to a further segregation of the clusters and possibly the dying-off of some of them leading to more functional specificity. These results suggest that through plasticity being a spatial and temporal local process, the computational properties of dendrites or complete neurons can be substantially augmented.

A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback

Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how behaviorally relevant adaptive changes in complex networks of spiking neurons could be achieved in a self-organizing manner through local synaptic plasticity. However, the capabilities and limitations of this learning rule could so far only be tested through computer simulations. This article provides tools for an analytic treatment of reward-modulated STDP, which allows us to predict under which conditions reward-modulated STDP will achieve a desired learning effect. These analytical results imply that neurons can learn through reward-modulated STDP to classify not only spatial but also temporal firing patterns of presynaptic neurons. They also can learn to respond to specific presynaptic firing patterns with particular spike patterns. Finally, the resulting learning theory predicts that even difficult credit-assignment problems, where it is very hard to tell which synaptic weights should be modified in order to increase the global reward for the system, can be solved in a self-organizing manner through reward-modulated STDP. This yields an explanation for a fundamental experimental result on biofeedback in monkeys by Fetz and Baker. In this experiment monkeys were rewarded for increasing the firing rate of a particular neuron in the cortex and were able to solve this extremely difficult credit assignment problem. Our model for this experiment relies on a combination of reward-modulated STDP with variable spontaneous firing activity. Hence it also provides a possible functional explanation for trial-to-trial variability, which is characteristic for cortical networks of neurons but has no analogue in currently existing artificial computing systems. In addition our model demonstrates that reward-modulated STDP can be applied to all synapses in a large recurrent neural network without endangering the stability of the network dynamics.

P.3.a.017 Decreased serum pro BDNF/BDNF ratio in patients with schizophrenia

Spontaneous activity fine-tunes neuronal connections in the developing brain. To explore the underlying synaptic plasticity mechanisms, we monitored naturally occurring changes in spontaneous activity at individual synapses with whole-cell patch-clamp recordings and simultaneous calcium imaging in the mouse visual cortex in vivo. Analyzing activity changes across large populations of synapses revealed a simple and efficient local plasticity rule: synapses that exhibit low synchronicity with nearby neighbors (<12 μm) become depressed in their transmission frequency. Asynchronous electrical stimulation of individual synapses in hippocampal slices showed that this is due to a decrease in synaptic transmission efficiency. Accordingly, experimentally increasing local synchronicity, by stimulating synapses in response to spontaneous activity at neighboring synapses, stabilized synaptic transmission. Finally, blockade of the high-affinity proBDNF receptor p75NTR prevented the depression of asynchronously stimulated synapses. Thus, spontaneous activity drives local synaptic plasticity at individual synapses in an “out-of-sync, lose-your-link” fashion through proBDNF/p75NTR signaling to refine neuronal connectivity.

The effect of Hebbian plasticity on the attractors of a dynamical system

A central problem in neuroscience is to bridge local synaptic plasticity and the global behavior of a system. It has been shown that Hebbian learning of connections in a feedforward network performs PCA on its inputs [1]. In recurrent Hopfield network with binary units, the Hebbian-learnt patterns form the attractors of the network [2]. Starting from a random recurrent network, Hebbian learning reduces system complexity from chaotic to fixed point [3].

Fiberoptic System for Recording Dendritic Calcium Signals in Layer 5 Neocortical Pyramidal Cells in Freely Moving Rats

Calcium influx into the dendritic tufts of layer 5 neocortical pyramidal neurons modifies a number of important cellular mechanisms. It can trigger local synaptic plasticity and switch the firing properties from regular to burst firing. Due to methodological limitations, our knowledge about Ca2+ spikes in the dendritic tuft stems mostly from in vitro experiments. However, it has been speculated that regenerative Ca2+ events in the distal dendrites correlate with distinct behavioral states. Therefore it would be most desirable to be able to record these Ca2+ events in vivo, preferably in the behaving animal. Here, we present a novel approach for recording Ca2+ signals in the dendrites of populations of layer 5 pyramidal neurons in vivo, which ensures that all recorded fluorescence changes are due to intracellular Ca2+ signals in the apical dendrites. The method has two main features: 1) bolus loading of layer 5 with a membrane-permeant Ca2+ dye resulting in specific loading of pyramidal cell dendrites in the upper layers and 2) a fiberoptic cable attached to a gradient index lens and a prism reflecting light horizontally at 90 degrees to the angle of the apical dendrites. We demonstrate that the in vivo signal-to-noise ratio recorded with this relatively inexpensive and easy-to-implement fiberoptic-based device is comparable to conventional camera-based imaging systems used in vitro. In addition, the device is flexible and lightweight and can be used for recording Ca2+ signals in the distal dendritic tuft of freely behaving animals.

Developing velocity sensitivity in a model neuron by local synaptic plasticity

Sensor neurons, like those in the visual cortex, display specific functional properties, e.g., tuning for the orientation, direction and velocity of a moving stimulus. It is still unclear how these properties arise from the processing of the inputs which converge at a given cell. Specifically, little is known how such properties can develop by ways of synaptic plasticity. In this study we investigate the hypothesis that velocity sensitivity can develop at a neuron from different types of synaptic plasticity at different dendritic sub-structures. Specifically we are implementing spike-timing dependent plasticity at one dendritic branch and conventional long-term potentiation at another branch, both driven by dendritic spikes triggered by moving inputs. In the first part of the study, we show how velocity sensitivity can arise from such a spatially localized difference in the plasticity. In the second part we show how this scenario is augmented by the interaction between dendritic spikes and back-propagating spikes also at different dendritic branches. Recent theoretical (Saudargiene et al. in Neural Comput 16:595-626, 2004) and experimental (Froemke et al. in Nature 434:221-225, 2005) results on spatially localized plasticity suggest that such processes may play a major role in determining how synapses will change depending on their site. The current study suggests that such mechanisms could be used to develop the functional specificities of a neuron.

Self-influencing synaptic plasticity: Recurrent changes of synaptic weights can lead to specific functional properties

Recent experimental results suggest that dendritic and back-propagating spikes can influence synaptic plasticity in different ways (Holthoff, 2004; Holthoff et al., 2005). In this study we investigate how these signals could interact at dendrites in space and time leading to changing plasticity properties at local synapse clusters. Similar to a previous study (Saudargiene et al., 2004) we employ a differential Hebbian learning rule to emulate spike-timing dependent plasticity and investigate how the interaction of dendritic and back-propagating spikes, as the post-synaptic signals, could influence plasticity. Specifically, we will show that local synaptic plasticity driven by spatially confined dendritic spikes can lead to the emergence of synaptic clusters with different properties. If one of these clusters can drive the neuron into spiking, plasticity may change and the now arising global influence of a back-propagating spike can lead to a further segregation of the clusters and possibly the dying-off of some of them leading to more functional specificity. These results suggest that through plasticity being a spatial and temporal local process, the computational properties of dendrites or complete neurons can be substantially augmented.

Decreased ability of rat temporal hippocampal CA1 region to produce long-term potentiation

Tetanic stimulation of Schaffer collaterals in the CA1 region of transverse slices, taken from the septal (dorsal) part of young rat hippocampus, produced N-Methyl- d-aspartate-dependent long-term potentiation (LTP) of the rising slope of excitatory postsynaptic potential (mean 38%). Under identical conditions of stimulation (100 Hz, 1 s) slices taken from the temporal (ventral) third of hippocampus presented a substantially reduced ability for LTP (mean 5%). The defect appeared to lie with the induction rather than the maintenance phase of LTP. These results suggest that a significant functional differentiation at the local synaptic plasticity level occurs between the two poles of hippocampus, which together with the substantial differences in their extrinsic connections, may help explain the reported differential participation of neurons in these parts of hippocampus during animal memory tests.