Homeostatic regulation of spontaneous and evoked synaptic transmission in two steps

Background: During development both Hebbian and homeostatic mechanisms regulate synaptic efficacy, usually working in opposite directions in response to neuronal activity. Homeostatic plasticity has often been investigated by assaying changes in spontaneous synaptic transmission resulting from chronic circuit inactivation. However, effects of inactivation on evoked transmission have been less frequently reported. Importantly, contributions from the effects of circuit inactivation and reactivation on synaptic efficacy have not been individuated. Results: Here we show for developing hippocampal neurons in primary culture that chronic inactivation with TTX results in increased mean amplitude of miniature synaptic currents (mEPSCs), but not evoked synaptic currents (eEPSCs). However, changes in quantal properties of transmission, partially reflected in mEPSCs, accurately predicted higher-order statistical properties of eEPSCs. The classical prediction of homeostasis - increased strength of evoked transmission - was realized after explicit circuit reactivation, in the form of cells' pairwise connection probability. In contrast, distributions of eEPSC amplitudes for control and inactivated-then- reactivated groups matched throughout. Conclusions: Homeostatic up-regulation of evoked synaptic transmission in developing hippocampal neurons in primary culture requires both the inactivation and reactivation stages, leading to a net increase in functional circuit connectivity. © 2013 Gerkin et al

Cassiterite oxygen isotopes in magmatic-hydrothermal systems: in situ microanalysis, fractionation factor, and applications

Tin and tungsten are important metals for the industrializing society. Deciphering the origin and evolution of hydrothermal fluids responsible for their formation is critical to underpin genetic models of ore formation. Traditional approaches obtain isotopic information mainly from bulk analysis of both ore and gangue minerals, or less frequently from in situ analysis of gangue minerals, which either bear inherited complexities and uncertainties or are indirect constraints. Hence, directly obtaining isotopic information from ore minerals such as cassiterite by in situ techniques is warranted. However, this has been hampered by challenges from both analytical and applicational aspects. In this study, we first demonstrate a lack of crystallographic orientation effects during cassiterite ion microprobe oxygen isotope analysis. Along with our newly developed matrix-matched reference material, the Yongde-Cst, which has a recommended δ18O value of 1.36 ± 0.16‰ (VSMOW) as defined by gas source isotope ratio mass spectrometry, in situ oxygen isotope analysis of cassiterite now is possible. We further refine the oxygen isotope fractionation (1000 ln α) for quartz-cassiterite by first-principles calculations, which is given by the equation of 1.259 × 106/T2 + 8.15 × 103/T − 4.72 (T is temperature in Kelvin). The 1000 ln α for quartz-cassiterite has a sensitive response to temperature, and makes cassiterite-quartz an excellent mineral pair in oxygen isotope thermometry, as described by the equation of T (℃) = 2427 × (δ18Oqtz − δ18Ocst)−0.4326 − 492.4. Using the well-established 1000 ln α of quartz-water, 1000 ln α of cassiterite-water is derived as 2.941 × 106/T2 − 11.45 × 103/T + 4.72 (T in Kelvin), which shows a weak response to temperature. This makes cassiterite an ideal mineral from which to derive δ18O of fluids as robust temperature estimates are no longer a prerequisite. We have applied oxygen isotope analysis to cassiterite samples from six Sn(-W) deposits in China. The results show considerable variability in δ18O values both within a single deposit and among studied deposits. Combining the δ18O of cassiterite samples and the equilibrium oxygen isotope fractionation, we find that the δ18O values of ore-forming fluids show a strong magmatic affinity with variable but mostly no to low degree involvements (~0-10%) of meteoric water, hence our results invite a reassessment on the extent and role of meteoric water in Sn-W mineralization. This study demonstrates that in situ oxygen isotope analysis of cassiterite is a promising tool to refine sources of ore-forming fluids, and to decode hydrothermal dynamics controlling tin and tungsten mineralization

SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo

The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work

Searching for plasticity in dissociated cortical cultures on multi-electrode arrays

We attempted to induce functional plasticity in dense cultures of cortical cells using stimulation through extracellular electrodes embedded in the culture dish substrate (multi-electrode arrays, or MEAs). We looked for plasticity expressed in changes in spontaneous burst patterns, and in array-wide response patterns to electrical stimuli, following several induction protocols related to those used in the literature, as well as some novel ones. Experiments were performed with spontaneous culture-wide bursting suppressed by either distributed electrical stimulation or by elevated extracellular magnesium concentrations as well as with spontaneous bursting untreated. Changes concomitant with induction were no larger in magnitude than changes that occurred spontaneously, except in one novel protocol in which spontaneous bursts were quieted using distributed electrical stimulation

UAV detection : a STDP trained deep convolutional spiking neural network retina-neuromorphic approach

The Dynamic Vision Sensor (DVS) has many attributes, such as sub-millisecond response time along with a good low light dy- namic range, that allows it to be well suited to the task for UAV De- tection. This paper proposes a system that exploits the features of an event camera solely for UAV detection while combining it with a Spik- ing Neural Network (SNN) trained using the unsupervised approach of Spike Time-Dependent Plasticity (STDP), to create an asynchronous, low power system with low computational overhead. Utilising the unique features of both the sensor and the network, this result in a system that is robust to a wide variety in lighting conditions, has a high temporal resolution, propagates only the minimal amount of information through the network, while training using the equivalent of 43,000 images. The network returns a 91% detection rate when shown other objects and can detect a UAV with less than 1% of pixels on the sensor being used for processing

Annexin-A5 assembled into two-dimensional arrays promotes cell membrane repair

Eukaryotic cells possess a universal repair machinery that ensures rapid resealing of plasma membrane disruptions. Before resealing, the torn membrane is submitted to considerable tension, which functions to expand the disruption. Here we show that annexin-A5 (AnxA5), a protein that self-assembles into two-dimensional (2D) arrays on membranes upon Ca2+ activation, promotes membrane repair. Compared with wild-type mouse perivascular cells, AnxA5-null cells exhibit a severe membrane repair defect. Membrane repair in AnxA5-null cells is rescued by addition of AnxA5, which binds exclusively to disrupted membrane areas. In contrast, an AnxA5 mutant that lacks the ability of forming 2D arrays is unable to promote membrane repair. We propose that AnxA5 participates in a previously unrecognized step of the membrane repair process: triggered by the local influx of Ca2+, AnxA5 proteins bind to torn membrane edges and form a 2D array, which prevents wound expansion and promotes membrane resealing

Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains

Experimental studies have observed Long Term synaptic Potentiation (LTP) when a presynaptic neuron fires shortly before a postsynaptic neuron, and Long Term Depression (LTD) when the presynaptic neuron fires shortly after, a phenomenon known as Spike Timing Dependant Plasticity (STDP). When a neuron is presented successively with discrete volleys of input spikes STDP has been shown to learn ‘early spike patterns’, that is to concentrate synaptic weights on afferents that consistently fire early, with the result that the postsynaptic spike latency decreases, until it reaches a minimal and stable value. Here, we show that these results still stand in a continuous regime where afferents fire continuously with a constant population rate. As such, STDP is able to solve a very difficult computational problem: to localize a repeating spatio-temporal spike pattern embedded in equally dense ‘distractor’ spike trains. STDP thus enables some form of temporal coding, even in the absence of an explicit time reference. Given that the mechanism exposed here is simple and cheap it is hard to believe that the brain did not evolve to use it

A Fokker-Planck formalism for diffusion with finite increments and absorbing boundaries

Gaussian white noise is frequently used to model fluctuations in physical systems. In Fokker-Planck theory, this leads to a vanishing probability density near the absorbing boundary of threshold models. Here we derive the boundary condition for the stationary density of a first-order stochastic differential equation for additive finite-grained Poisson noise and show that the response properties of threshold units are qualitatively altered. Applied to the integrate-and-fire neuron model, the response turns out to be instantaneous rather than exhibiting low-pass characteristics, highly non-linear, and asymmetric for excitation and inhibition. The novel mechanism is exhibited on the network level and is a generic property of pulse-coupled systems of threshold units.Comment: Consists of two parts: main article (3 figures) plus supplementary text (3 extra figures