scholarly journals Event-Driven Simulation Scheme for Spiking Neural Networks Using Lookup Tables to Characterize Neuronal Dynamics

2006 ◽  
Vol 18 (12) ◽  
pp. 2959-2993 ◽  
Author(s):  
Eduardo Ros ◽  
Richard Carrillo ◽  
Eva M. Ortigosa ◽  
Boris Barbour ◽  
Rodrigo Agís
Keyword(s):  
Synaptic Plasticity ◽  
High Speed ◽  
Large Scale ◽  
Computational Cost ◽  
Brain Structures ◽  
Spike Timing ◽  
Neuronal Dynamics ◽  
Event Driven ◽  
Synaptic Dynamics

Nearly all neuronal information processing and interneuronal communication in the brain involves action potentials, or spikes, which drive the short-term synaptic dynamics of neurons, but also their long-term dynamics, via synaptic plasticity. In many brain structures, action potential activity is considered to be sparse. This sparseness of activity has been exploited to reduce the computational cost of large-scale network simulations, through the development of event-driven simulation schemes. However, existing event-driven simulations schemes use extremely simplified neuronal models. Here, we implement and evaluate critically an event-driven algorithm (ED-LUT) that uses precalculated look-up tables to characterize synaptic and neuronal dynamics. This approach enables the use of more complex (and realistic) neuronal models or data in representing the neurons, while retaining the advantage of high-speed simulation. We demonstrate the method's application for neurons containing exponential synaptic conductances, thereby implementing shunting inhibition, a phenomenon that is critical to cellular computation. We also introduce an improved two-stage event-queue algorithm, which allows the simulations to scale efficiently to highly connected networks with arbitrary propagation delays. Finally, the scheme readily accommodates implementation of synaptic plasticity mechanisms that depend on spike timing, enabling future simulations to explore issues of long-term learning and adaptation in large-scale networks.

Stable Competitive Dynamics Emerge from Multispike Interactions in a Stochastic Model of Spike-Timing-Dependent Plasticity

2006 ◽  
Vol 18 (10) ◽  
pp. 2414-2464 ◽  
Author(s):  
Peter A. Appleby ◽  
Terry Elliott
Keyword(s):  
Synaptic Plasticity ◽  
Stochastic Model ◽  
Higher Order ◽  
Spike Timing ◽  
Competitive Dynamics ◽  
Spike Timing Dependent Plasticity ◽  
Interaction Function ◽  
Dependent Plasticity ◽  
Order Interaction ◽  
Synaptic Dynamics

In earlier work we presented a stochastic model of spike-timing-dependent plasticity (STDP) in which STDP emerges only at the level of temporal or spatial synaptic ensembles. We derived the two-spike interaction function from this model and showed that it exhibits an STDP-like form. Here, we extend this work by examining the general n-spike interaction functions that may be derived from the model. A comparison between the two-spike interaction function and the higher-order interaction functions reveals profound differences. In particular, we show that the two-spike interaction function cannot support stable, competitive synaptic plasticity, such as that seen during neuronal development, without including modifications designed specifically to stabilize its behavior. In contrast, we show that all the higher-order interaction functions exhibit a fixed-point structure consistent with the presence of competitive synaptic dynamics. This difference originates in the unification of our proposed “switch” mechanism for synaptic plasticity, coupling synaptic depression and synaptic potentiation processes together. While three or more spikes are required to probe this coupling, two spikes can never do so. We conclude that this coupling is critical to the presence of competitive dynamics and that multispike interactions are therefore vital to understanding synaptic competition.

scholarly journals A neuromorphic implementation of multiple spike-timing synaptic plasticity rules for large-scale neural networks

2015 ◽  
Vol 9 ◽  
Author(s):  
Runchun M. Wang ◽  
Tara J. Hamilton ◽  
Jonathan C. Tapson ◽  
André van Schaik
Keyword(s):  
Neural Networks ◽  
Synaptic Plasticity ◽  
Large Scale ◽  
Spike Timing

Modeling the short-term dynamics of in vivo excitatory spike transmission

2018 ◽  
Author(s):  
Abed Ghanbari ◽  
Naixin Ren ◽  
Christian Keine ◽  
Carl Stoelzel ◽  
Bernhard Englitz ◽  
...  
Keyword(s):  
Synaptic Plasticity ◽  
Large Scale ◽  
Auditory Brainstem ◽  
Postsynaptic Neuron ◽  
Intracellular Recordings ◽  
Short Term ◽  
Functional Connection ◽  
Synaptic Dynamics ◽  
Synaptic Effects

AbstractInformation transmission in neural networks is influenced by both short-term synaptic plasticity (STP) as well as non-synaptic factors, such as after-hyperpolarization currents and changes in excitability. Although these effects have been widely characterized in vitro using intracellular recordings, how they interact in vivo is unclear. Here we develop a statistical model of the short-term dynamics of spike transmission that aims to disentangle the contributions of synaptic and non-synaptic effects based only on observed pre- and postsynaptic spiking. The model includes a dynamic functional connection with short-term plasticity as well as effects due to the recent history of postsynaptic spiking and slow changes in postsynaptic excitability. Using paired spike recordings, we find that the model accurately describes the short-term dynamics of in vivo spike transmission at a diverse set of identified and putative excitatory synapses, including a thalamothalamic connection in mouse, a thalamocortical connection in a female rabbit, and an auditory brainstem synapse in a female gerbil. We illustrate the utility of this modeling approach by showing how the spike transmission patterns captured by the model may be sufficient to account for stimulus-dependent differences in spike transmission in the auditory brainstem (endbulb of Held). Finally, we apply this model to large-scale multi-electrode recordings to illustrate how such an approach has the potential to reveal cell-type specific differences in spike transmission in vivo. Although short-term synaptic plasticity parameters estimated from ongoing pre- and postsynaptic spiking are highly uncertain, our results are partially consistent with previous intracellular observations in these synapses.Significance StatementAlthough synaptic dynamics have been extensively studied and modeled using intracellular recordings of post-synaptic currents and potentials, inferring synaptic effects from extracellular spiking is challenging. Whether or not a synaptic current contributes to postsynaptic spiking depends not only on the amplitude of the current, but also on many other factors, including the activity of other, typically unobserved, synapses, the overall excitability of the postsynaptic neuron, and how recently the postsynaptic neuron has spiked. Here we developed a model that, using only observations of pre- and postsynaptic spiking, aims to describe the dynamics of in vivo spike transmission by modeling both short-term synaptic plasticity and non-synaptic effects. This approach may provide a novel description of fast, structured changes in spike transmission.

scholarly journals Video rate volumetric Ca2+ imaging across cortical layers using Seeded Iterative Demixing (SID) microscopy

2017 ◽  
Author(s):  
Tobias Nöbauer ◽  
Oliver Skocek ◽  
Alejandro J. Pernía-Andrade ◽  
Lukas Weilguny ◽  
Francisca Martinez Traub ◽  
...  
Keyword(s):  
High Speed ◽  
Computational Cost ◽  
Neuronal Dynamics ◽  
Tissue Scattering ◽  
Mouse Cortex ◽  
New Applications ◽  
Source Extraction ◽  
Neuroscience Community ◽  
Wide Dissemination

Light-field microscopy (LFM) is a scalable approach for volumetric Ca2+ imaging with the highest volumetric acquisition rates (up to 100 Hz). While this has enabled high-speed whole-brain Ca2+ imaging in small semi-transparent specimen, tissue scattering has limited its application in the rodent brain. Here we introduce Seeded Iterative Demixing (SID), a computational source extraction technique that extends LFM to the scattering mammalian cortex. Using GCaMP-expressing mice we demonstrate SID’s ability to capture neuronal dynamics in vivo within a volume of 900×900×260μm located as deep as 380 μm in the mouse cortex and hippocampus at 30 Hz volume rate while faithfully discriminating signals from neurons as close as 20 μm, at three orders of magnitude reduced computational cost. The simplicity and scalability of LFM, coupled with the performance of SID opens up a range of new applications including closed-loop experiments and is expected to propel its wide dissemination within the neuroscience community.

scholarly journals Endocannabinoid dynamics gate spike-timing dependent depression and potentiation

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Yihui Cui ◽  
Ilya Prokin ◽  
Hao Xu ◽  
Bruno Delord ◽  
Stephane Genet ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Synaptic Plasticity ◽  
Learning And Memory ◽  
Long Term Potentiation ◽  
Cellular Mechanism ◽  
Spike Timing ◽  
Long Term Depression ◽  
Term Potentiation ◽  
Short And Long Term

Synaptic plasticity is a cardinal cellular mechanism for learning and memory. The endocannabinoid (eCB) system has emerged as a pivotal pathway for synaptic plasticity because of its widely characterized ability to depress synaptic transmission on short- and long-term scales. Recent reports indicate that eCBs also mediate potentiation of the synapse. However, it is not known how eCB signaling may support bidirectionality. Here, we combined electrophysiology experiments with mathematical modeling to question the mechanisms of eCB bidirectionality in spike-timing dependent plasticity (STDP) at corticostriatal synapses. We demonstrate that STDP outcome is controlled by eCB levels and dynamics: prolonged and moderate levels of eCB lead to eCB-mediated long-term depression (eCB-tLTD) while short and large eCB transients produce eCB-mediated long-term potentiation (eCB-tLTP). Moreover, we show that eCB-tLTD requires active calcineurin whereas eCB-tLTP necessitates the activity of presynaptic PKA. Therefore, just like glutamate or GABA, eCB form a bidirectional system to encode learning and memory.

scholarly journals Long-term population spike-timing-dependent plasticity promotes synaptic tagging but not cross-tagging in rat hippocampal area CA1

2019 ◽  
Vol 116 (12) ◽  
pp. 5737-5746 ◽  
Author(s):  
Karen Ka Lam Pang ◽  
Mahima Sharma ◽  
Kumar Krishna-K. ◽  
Thomas Behnisch ◽  
Sreedharan Sajikumar
Keyword(s):  
Synaptic Plasticity ◽  
Long Term Potentiation ◽  
Neuronal Population ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Adult Rat ◽  
Term Potentiation ◽  
Hippocampal Ca3

In spike-timing-dependent plasticity (STDP), the direction and degree of synaptic modification are determined by the coherence of pre- and postsynaptic activities within a neuron. However, in the adult rat hippocampus, it remains unclear whether STDP-like mechanisms in a neuronal population induce synaptic potentiation of a long duration. Thus, we asked whether the magnitude and maintenance of synaptic plasticity in a population of CA1 neurons differ as a function of the temporal order and interval between pre- and postsynaptic activities. Modulation of the relative timing of Schaffer collateral fibers (presynaptic component) and CA1 axons (postsynaptic component) stimulations resulted in an asymmetric population STDP (pSTDP). The resulting potentiation in response to 20 pairings at 1 Hz was largest in magnitude and most persistent (4 h) when presynaptic activity coincided with or preceded postsynaptic activity. Interestingly, when postsynaptic activation preceded presynaptic stimulation by 20 ms, an immediate increase in field excitatory postsynaptic potentials was observed, but it eventually transformed into a synaptic depression. Furthermore, pSTDP engaged in selective forms of late-associative activity: It facilitated the maintenance of tetanization-induced early long-term potentiation (LTP) in neighboring synapses but not early long-term depression, reflecting possible mechanistic differences with classical tetanization-induced LTP. The data demonstrate that a pairing of pre- and postsynaptic activities in a neuronal population can greatly reduce the required number of synaptic plasticity-evoking events and induce a potentiation of a degree and duration similar to that with repeated tetanization. Thus, pSTDP determines synaptic efficacy in the hippocampal CA3–CA1 circuit and could bias the CA1 neuronal population toward potentiation in future events.

Source Term Based Modeling of Rotating Cavitation in Turbopumps

2018 ◽  
Author(s):  
A. G. Vermes ◽  
C. Lettieri
Keyword(s):  
Price Competition ◽  
High Speed ◽  
Large Scale ◽  
Source Term ◽  
Experimental Testing ◽  
Rocket Engine ◽  
Computational Cost ◽  
Flow Coefficient ◽  
Rocket Engines ◽  
Launch Vehicles

The recent growth of private options in launch vehicles has substantially raised price competition in the space launch market. This has increased the need to deliver reliable launch vehicles at reduced engine development cost, and has led to increased industrial interest in reduced order models. Large-scale liquid rocket engines require high-speed turbopumps to inject cryogenic propellants into the combustion chamber. These pumps can experience cavitation instabilities even when operating near design conditions. Of particular concern is rotating cavitation, which is characterized by an asymmetric cavity rotating at the pump inlet, which can cause severe vibration, breaking of the pump and loss of the mission. Despite much work in the field, there are limited guidelines to avoid rotating cavitation during design and its occurrence is often assessed through costly experimental testing. This paper presents a source term based model for stability assessment of rocket engine turbopumps. The approach utilizes mass and momentum source terms to model cavities and hydrodynamic blockage in inviscid, single-phase numerical calculations, reducing the computational cost of the calculations by an order of magnitude compared to traditional numerical methods. Comparison of the results from the model with experiments and high-fidelity calculations indicates agreement of the head coefficient and cavity blockage within 0.26% and 5% respectively. The computations capture rotating cavitation in a 2D inducer at the expected flow coefficient and cavitation number. The mechanism of formation and propagation of the instability is correctly reproduced.

scholarly journals Meso/macroscopically multifunctional surface interfaces, ridges, and vortex-modified anode/cathode cuticles as force-driven modulation of high-energy density of LIB electric vehicles

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
H. Khalifa ◽  
S. A. El-Safty ◽  
A. Reda ◽  
M. A. Shenashen ◽  
M. M. Selim ◽  
...  
Keyword(s):  
Energy Density ◽  
High Speed ◽  
Large Scale ◽  
Coulombic Efficiency ◽  
Scale Up ◽  
Lithium Ion ◽  
High Energy ◽  
High Energy Density ◽  
Diverse Range

Abstract Modulation of lithium-ion battery (LIB) anodes/cathodes with three-dimensional (3D) topographical hierarchy ridges, surface interfaces, and vortices promotes the power tendency of LIBs in terms of high-energy density and power density. Large-scale meso-geodesics offer a diverse range of spatial LIB models along the geodetically shaped downward/upward curvature, leading to open-ended movement gate options, and diffusible space orientations. Along with the primary 3D super-scalable hierarchy, the formation of structural features of building block egress/ingress, curvature cargo-like sphere vehicles, irregularly located serrated cuticles with abundant V-undulated rigidness, feathery tube pipe conifers, and a band of dagger-shaped needle sticks on anode/cathode electrode surfaces provides high performance LIB modules. The geodetically-shaped anode/cathode design enables the uniqueness of all LIB module configurations in terms of powerful lithium ion (Li+) movement revolving in out-/in- and up-/downward diffusion regimes and in hovering electron density for high-speed discharge rates. The stability of built-in anode//cathode full-scale LIB-model meso-geodesics affords an outstanding long-term cycling performance. The full-cell LIB meso-geodesics offered 91.5% retention of the first discharge capacity of 165.8 mAhg−1 after 2000 cycles, Coulombic efficiency of ~99.6% at the rate of 1 C and room temperature, and high specific energy density of ≈119 Wh kg−1. This LIB meso-geodesic module configuration may align perfectly with the requirements of the energy density limit mandatory for long-term EV driving range and the scale-up commercial manufactures.

Temporal Dynamics of Rate-Based Synaptic Plasticity Rules in a Stochastic Model of Spike-Timing-Dependent Plasticity

2008 ◽  
Vol 20 (9) ◽  
pp. 2253-2307 ◽  
Author(s):  
Terry Elliott
Keyword(s):  
Synaptic Plasticity ◽  
Stochastic Model ◽  
Parameter Space ◽  
Synaptic Strength ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Firing Rates ◽  
Typical Sequence ◽  
Synaptic Dynamics

In a recently proposed, stochastic model of spike-timing-dependent plasticity, we derived general expressions for the expected change in synaptic strength, ΔSn, induced by a typical sequence of precisely n spikes. We found that the rules ΔSn, n ≥ 3, exhibit regions of parameter space in which stable, competitive interactions between afferents are present, leading to the activity-dependent segregation of afferents on their targets. The rules ΔSn, however, allow an indefinite period of time to elapse for the occurrence of precisely n spikes, while most measurements of changes in synaptic strength are conducted over definite periods of time during which a potentially unknown number of spikes may occur. Here, therefore, we derive an expression, ΔS(t), for the expected change in synaptic strength of a synapse experiencing an average sequence of spikes of typical length occurring during a fixed period of time, t. We find that the resulting synaptic plasticity rule Δ S(t) exhibits a number of remarkable properties. It is an entirely self-stabilizing learning rule in all regions of parameter space. Further, its parameter space is carved up into three distinct, contiguous regions in which the exhibited synaptic interactions undergo different transitions as the time t is increased. In one region, the synaptic dynamics change from noncompetitive to competitive to entirely depressing. In a second region, the dynamics change from noncompetitive to competitive without the second transition to entirely depressing dynamics. In a third region, the dynamics are always noncompetitive. The locations of these regions are not fixed in parameter space but may be modified by changing the mean presynaptic firing rates. Thus, neurons may be moved among these three different regions and so exhibit different sets of synaptic dynamics depending on their mean firing rates.

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