Self-organization in Balanced State Networks by STDP and Homeostatic Plasticity

Structural inhomogeneities in synaptic efficacies have a strong impact on population response dynamics of cortical networks and are believed to play an important role in their functioning. However, little is known about how such inhomogeneities could evolve by means of synaptic plasticity. Here we present an adaptive model of a balanced neuronal network that combines two different types of plasticity, STDP and synaptic scaling. The plasticity rules yield both long-tailed distributions of synaptic weights and firing rates. Simultaneously, a highly connected subnetwork of driver neurons with strong synapses emerges. Coincident spiking activity of several driver cells can evoke population bursts and driver cells have similar dynamical properties as leader neurons found experimentally. Our model allows us to observe the delicate interplay between structural and dynamical properties of the emergent inhomogeneities. It is simple, robust to parameter changes and able to explain a multitude of different experimental findings in one basic network.     

Homeostatic Intrinsic Plasticity Is Functionally Altered in Fmr1 KO Cortical Neurons

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Synaptic Plasticity Enables Adaptive Self-Tuning Critical Networks

During rest, the mammalian cortex displays spontaneous neural activity. Spiking of single neurons during rest has been described as irregular and asynchronous. In contrast, recent in vivo and in vitro population measures of spontaneous activity, using the LFP, EEG, MEG or fMRI suggest that the default state of the cortex is critical, manifested by spontaneous, scale-invariant, cascades of activity known as neuronal avalanches. Criticality keeps a network poised for optimal information processing, but this view seems to be difficult to reconcile with apparently irregular single neuron spiking. Here, we simulate a 10,000 neuron, deterministic, plastic network of spiking neurons. We show that a combination of short- and long-term synaptic plasticity enables these networks to exhibit criticality in the face of intrinsic, i.e. self-sustained, asynchronous spiking. Brief external perturbations lead to adaptive, long-term modification of intrinsic network connectivity through long-term excitatory plasticity, whereas long-term inhibitory plasticity enables rapid self-tuning of the network back to a critical state. The critical state is characterized by a branching parameter oscillating around unity, a critical exponent close to -3/2 and a long tail distribution of a self-similarity parameter between 0.5 and 1.     

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity

Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.     

Coordination of Protein Phosphorylation and Dephosphorylation in Synaptic Plasticity

A central theme in nervous system function is equilibrium: synaptic strengths wax and wane, neuronal firing rates adjust up and down, and neural circuits balance excitation with inhibition. This push/pull regulatory theme carries through to the molecular level at excitatory synapses, where protein function is controlled through phosphorylation and dephosphorylation by kinases and phosphatases. However, these opposing enzymatic activities are only part of the equation as scaffolding interactions and assembly of multi-protein complexes are further required for efficient, localized synaptic signaling. This review will focus on coordination of postsynaptic serine/threonine kinase and phosphatase signaling by scaffold proteins during synaptic plasticity.     

DCC Expression by Neurons Regulates Synaptic Plasticity in the Adult Brain

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Highlights? DCC and netrin-1 are enriched at synapses in the adult mouse forebrain ? DCC is enriched in the PSD and regulates dendritic spine morphology ? LTP induction and memory formation require DCC expression by neurons ? DCC activation of Src is required for NMDAR-dependent LTP in adult CNS     

A Diffusive Homeostatic Signal Maintains Neural Heterogeneity and Responsiveness in Cortical Networks

Gaseous neurotransmitters such as nitric oxide (NO) provide a unique and often overlooked mechanism for neurons to communicate through diffusion within a network, independent of synaptic connectivity. NO provides homeostatic control of intrinsic excitability. Here we conduct a theoretical investigation of the distinguishing roles of NO-mediated diffusive homeostasis in comparison with canonical non-diffusive homeostasis in cortical networks. We find that both forms of homeostasis provide a robust mechanism for maintaining stable activity following perturbations. However, the resulting networks differ, with diffusive homeostasis maintaining substantial heterogeneity in activity levels of individual neurons, a feature disrupted in networks with non-diffusive homeostasis. This results in networks capable of representing input heterogeneity, and linearly responding over a broader range of inputs than those undergoing non-diffusive homeostasis. We further show that these properties are preserved when homeostatic and Hebbian plasticity are combined. These results suggest a mechanism for dynamically maintaining neural heterogeneity, and expose computational advantages of non-local homeostatic processes.     

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.     

Functional Calcium Imaging in Developing Cortical Networks

A hallmark pattern of activity in developing nervous systems is spontaneous, synchronized network activity. Synchronized activity has been observed in intact spinal cord, brainstem, retina, cortex and dissociated neuronal culture preparations. During periods of spontaneous activity, neurons depolarize to fire single or bursts of action potentials, activating many ion channels. Depolarization activates voltage-gated calcium channels on dendrites and spines that mediate calcium influx. Highly synchronized electrical activity has been measured from local neuronal networks using field electrodes. This technique enables high temporal sampling rates but lower spatial resolution due to integrated read-out of multiple neurons at one electrode. Single cell resolution of neuronal activity is possible using patch-clamp electrophysiology on single neurons to measure firing activity. However, the ability to measure from a network is limited to the number of neurons patched simultaneously, and typically is only one or two neurons. The use of calcium-dependent fluorescent indicator dyes has enabled the measurement of synchronized activity across a network of cells. This technique gives both high spatial resolution and sufficient temporal sampling to record spontaneous activity of the developing network.A key feature of newly-forming cortical and hippocampal networks during pre- and early postnatal development is spontaneous, synchronized neuronal activity (Katz & Shatz, 1996; Khaziphov & Luhmann, 2006). This correlated network activity is believed to be essential for the generation of functional circuits in the developing nervous system (Spitzer, 2006). In both primate and rodent brain, early electrical and calcium network waves are observed pre- and postnatally in vivo and in vitro (Adelsberger et al., 2005; Garaschuk et al., 2000; Lamblin et al., 1999). These early activity patterns, which are known to control several developmental processes including neuronal differentiation, synaptogenesis and plasticity (Rakic & Komuro, 1995; Spitzer et al., 2004) are of critical importance for the correct development and maturation of the cortical circuitry.In this JoVE video, we demonstrate the methods used to image spontaneous activity in developing cortical networks. Calcium-sensitive indicators, such as Fura 2-AM ester diffuse across the cell membrane where intracellular esterase activity cleaves the AM esters to leave the cell-impermeant form of indicator dye. The impermeant form of indicator has carboxylic acid groups which are able to then detect and bind calcium ions intracellularly.. The fluorescence of the calcium-sensitive dye is transiently altered upon binding to calcium. Single or multi-photon imaging techniques are used to measure the change in photons being emitted from the dye, and thus indicate an alteration in intracellular calcium. Furthermore, these calcium-dependent indicators can be combined with other fluorescent markers to investigate cell types within the active network.     

Shp2 in Forebrain Neurons Regulates Synaptic Plasticity,Locomotion, and Memory Formation in Mice

Shp2 (Src homology 2 domain-containing protein tyrosine phosphatase 2) regulates neural cell differentiation. It is also expressed in postmitotic neurons, however, and mutations of Shp2 are associated with clinical syndromes characterized by mental retardation. Here we show that conditional-knockout (cKO) mice lacking Shp2 specifically in postmitotic forebrain neurons manifest abnormal behavior, including hyperactivity. Novelty-induced expression of immediate-early genes and activation of extracellular-signal-regulated kinase (Erk) were attenuated in the cerebral cortex and hippocampus of Shp2 cKO mice, suggestive of reduced neuronal activity. In contrast, ablation of Shp2 enhanced high-K⁺-induced Erk activation in both cultured cortical neurons and synaptosomes, whereas it inhibited that induced by brain-derived growth factor in cultured neurons. Posttetanic potentiation and paired-pulse facilitation were attenuated and enhanced, respectively, in hippocampal slices from Shp2 cKO mice. The mutant mice also manifested transient impairment of memory formation in the Morris water maze. Our data suggest that Shp2 contributes to regulation of Erk activation and synaptic plasticity in postmitotic forebrain neurons and thereby controls locomotor activity and memory formation.     

Matrix Metalloproteinase-9 Overexpression Regulates Hippocampal Synaptic Plasticity and Decreases Alcohol Consumption and Preference in Mice

Neurochemical Research - Brain matrix metalloproteinases (MMPs) have been recently implicated in alcohol addiction; however, the molecular mechanisms remain poorly understood. Matrix...     

Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector

Hebbian changes of excitatory synapses are driven by and further enhance correlations between pre- and postsynaptic activities. Hence, Hebbian plasticity forms a positive feedback loop that can lead to instability in simulated neural networks. To keep activity at healthy, low levels, plasticity must therefore incorporate homeostatic control mechanisms. We find in numerical simulations of recurrent networks with a realistic triplet-based spike-timing-dependent plasticity rule (triplet STDP) that homeostasis has to detect rate changes on a timescale of seconds to minutes to keep the activity stable. We confirm this result in a generic mean-field formulation of network activity and homeostatic plasticity. Our results strongly suggest the existence of a homeostatic regulatory mechanism that reacts to firing rate changes on the order of seconds to minutes.     

Rapid Turnover of Cortical NCAM1 Regulates Synaptic Reorganization after Peripheral Nerve Injury

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