Efficient simulation scheme for spiking neural networks

Nearly all neuronal information processing and inter¬neuronal 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 design, implement and evaluate critically an event-driven algorithm (EDLUT) that uses pre-calculated lookup 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 upon spike timing, enabling future simulations to explore issues of long-term learning and adaptation in large-scale networks

Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation

Cerebellar Purkinje cells mediate accurate eye movement coordination. However, it remains unclear how oculomotor adaptation depends on the interplay between the characteristic Purkinje cell response patterns, namely tonic, bursting, and spike pauses. Here, a spiking cerebellar model assesses the role of Purkinje cell firing patterns in vestibular ocular reflex (VOR) adaptation. The model captures the cerebellar microcircuit properties and it incorporates spike-based synaptic plasticity at multiple cerebellar sites. A detailed Purkinje cell model reproduces the three spike-firing patterns that are shown to regulate the cerebellar output. Our results suggest that pauses following Purkinje complex spikes (bursts) encode transient disinhibition of target medial vestibular nuclei, critically gating the vestibular signals conveyed by mossy fibres. This gating mechanism accounts for early and coarse VOR acquisition, prior to the late reflex consolidation. In addition, properly timed and sized Purkinje cell bursts allow the ratio between long-term depression and potentiation (LTD/LTP) to be finely shaped at mossy fibre-medial vestibular nuclei synapses, which optimises VOR consolidation. Tonic Purkinje cell firing maintains the consolidated VOR through time. Importantly, pauses are crucial to facilitate VOR phase-reversal learning, by reshaping previously learnt synaptic weight distributions. Altogether, these results predict that Purkinje spike burst-pause dynamics are instrumental to VOR learning and reversal adaptation.This work was supported by the European Union (www.europa.eu), Project SpikeControl 658479 (recipient NL), the Spanish Agencia Estatal de Investigacio´n and European Regional Development Fund (www.ciencia.gob.es/ portal/site/MICINN/aei), Project CEREBROT TIN2016-81041-R (recipient ER), and the French National Research Agency (www.agence-nationalerecherche. fr) – Essilor International (www.essilor. com), Chair SilverSight ANR-14-CHIN-0001 (recipient AA)

A Metric for Evaluating Neural Input Representation in Supervised Learning Networks

Supervised learning has long been attributed to several feed-forward neural circuits within the brain, with particular attention being paid to the cerebellar granular layer. The focus of this study is to evaluate the input activity representation of these feed-forward neural networks. The activity of cerebellar granule cells is conveyed by parallel fibers and translated into Purkinje cell activity, which constitutes the sole output of the cerebellar cortex. The learning process at this parallel-fiber-to-Purkinje-cell connection makes each Purkinje cell sensitive to a set of specific cerebellar states, which are roughly determined by the granule-cell activity during a certain time window. A Purkinje cell becomes sensitive to each neural input state and, consequently, the network operates as a function able to generate a desired output for each provided input by means of supervised learning. However, not all sets of Purkinje cell responses can be assigned to any set of input states due to the network's own limitations (inherent to the network neurobiological substrate), that is, not all input-output mapping can be learned. A key limiting factor is the representation of the input states through granule-cell activity. The quality of this representation (e.g., in terms of heterogeneity) will determine the capacity of the network to learn a varied set of outputs. Assessing the quality of this representation is interesting when developing and studying models of these networks to identify those neuron or network characteristics that enhance this representation. In this study we present an algorithm for evaluating quantitatively the level of compatibility/interference amongst a set of given cerebellar states according to their representation (granule-cell activation patterns) without the need for actually conducting simulations and network training. The algorithm input consists of a real-number matrix that codifies the activity level of every considered granule-cell in each state. The capability of this representation to generate a varied set of outputs is evaluated geometrically, thus resulting in a real number that assesses the goodness of the representation

Local and Global Well-Posedness for Aggregation Equations and Patlak-Keller-Segel Models with Degenerate Diffusion

Recently, there has been a wide interest in the study of aggregation equations and Patlak-Keller-Segel (PKS) models for chemotaxis with degenerate diffusion. The focus of this paper is the unification and generalization of the well-posedness theory of these models. We prove local well-posedness on bounded domains for dimensions $d\geq 2$ and in all of space for $d\geq 3$, the uniqueness being a result previously not known for PKS with degenerate diffusion. We generalize the notion of criticality for PKS and show that subcritical problems are globally well-posed. For a fairly general class of problems, we prove the existence of a critical mass which sharply divides the possibility of finite time blow up and global existence. Moreover, we compute the critical mass for fully general problems and show that solutions with smaller mass exists globally. For a class of supercritical problems we prove finite time blow up is possible for initial data of arbitrary mass.Comment: 31 page

Vertically Extended Neutral Gas in the Massive Edge-on Spiral NGC 5746

We present Very Large Array 21-cm observations of the massive edge-on spiral galaxy NGC 5746. This galaxy has recently been reported to have a luminous X-ray halo, which has been taken as evidence of residual hot gas as predicted in galaxy formation scenarios. Such models also predict that some of this gas should undergo thermal instabilities, leading to a population of warm clouds falling onto the disk. If so, then one might expect to find a vertically extended neutral layer. We detect a substantial high-latitude component, but conclude that almost all of its mass of 1.2-1.6 billion solar masses most likely resides in a warp. Four features far from the plane containing about 100 million solar masses are found at velocities distinct from this warp. These clouds may be associated with the expected infall, although an origin in a disk-halo flow cannot be ruled out, except for one feature which is counter-rotating. The warp itself may be a result of infall according to recent models. But clearly this galaxy lacks a massive, lagging neutral halo as found in NGC 891. The disk HI is concentrated into two rings of radii 1.5 and 3 arcminutes. Radial inflow is found in the disk, probably due to the bar in this galaxy. A nearby member of this galaxy group, NGC 5740, is also detected. It shows a prominent one-sided extension which may be the result of ram pressure stripping.Comment: 55 pages, 20 figure

Retrograde semaphorin-plexin signalling drives homeostatic synaptic plasticity.

Homeostatic signalling systems ensure stable but flexible neural activity and animal behaviour. Presynaptic homeostatic plasticity is a conserved form of neuronal homeostatic signalling that is observed in organisms ranging from Drosophila to human. Defining the underlying molecular mechanisms of neuronal homeostatic signalling will be essential in order to establish clear connections to the causes and progression of neurological disease. During neural development, semaphorin-plexin signalling instructs axon guidance and neuronal morphogenesis. However, semaphorins and plexins are also expressed in the adult brain. Here we show that semaphorin 2b (Sema2b) is a target-derived signal that acts upon presynaptic plexin B (PlexB) receptors to mediate the retrograde, homeostatic control of presynaptic neurotransmitter release at the neuromuscular junction in Drosophila. Further, we show that Sema2b-PlexB signalling regulates presynaptic homeostatic plasticity through the cytoplasmic protein Mical and the oxoreductase-dependent control of presynaptic actin. We propose that semaphorin-plexin signalling is an essential platform for the stabilization of synaptic transmission throughout the developing and mature nervous system. These findings may be relevant to the aetiology and treatment of diverse neurological and psychiatric diseases that are characterized by altered or inappropriate neural function and behaviour

Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fninf. 2017.00007/full#supplementary-materialModeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under increasing levels of neural complexity.This study was supported by the European Union NR (658479-Spike Control), the Spanish National Grant NEUROPACT (TIN2013-47069-P) and by the Spanish National Grant PhD scholarship (AP2012-0906). We gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan GPUs for current EDLUT development