Optimization of input parameters to a CN neuron model to simulate its activity during and between epileptic absence seizures

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Combining machine learning and simulations of a morphologically realistic model to study modulation of neuronal activity in cerebellar nuclei

Abstract from 23rd Annual Computational Neuroscience Meeting: CNS 2014 © 2014 Alva et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http:// creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Epileptic absence seizures are characterized by synchronized oscillatory activity in the cerebral cortex that can be recorded as so-called spike-and-wave discharges (SWDs) by electroencephalogram. Although the cerebral cortex and the directly connected thalamus are paramount to this particular form of epilepsy, several other parts of the mammalian brain are likely to influence this oscillatory activity. We have recently shown that some of the cerebellar nuclei (CN) neurons, which form the main output of the cerebellum, show synchronized oscillatory activity during episodes of cortical SWDs in two independent mouse models of absence epilepsy [1]. The CN neurons that show this significant correlation with the SWDs are deemed to “participate” in the seizure activity and are therefore used in our current study designed to unravel the potential causes of such oscillatory firing patternsPeer reviewe

The effect of non-specific LTD on pattern recognition in cerebellar Purkinje cells

© 2010 Safaryan et al; licensee BioMed Central Ltd.Poster presented at CNS 2010Peer reviewe

Determinants of synaptic integration and heterogeneity in rebound firing explored with data-driven models of deep cerebellar nucleus cells

Significant inroads have been made to understand cerebellar cortical processing but neural coding at the output stage of the cerebellum in the deep cerebellar nuclei (DCN) remains poorly understood. The DCN are unlikely to just present a relay nucleus because Purkinje cell inhibition has to be turned into an excitatory output signal, and DCN neurons exhibit complex intrinsic properties. In particular, DCN neurons exhibit a range of rebound spiking properties following hyperpolarizing current injection, raising the question how this could contribute to signal processing in behaving animals. Computer modeling presents an ideal tool to investigate how intrinsic voltage-gated conductances in DCN neurons could generate the heterogeneous firing behavior observed, and what input conditions could result in rebound responses. To enable such an investigation we built a compartmental DCN neuron model with a full dendritic morphology and appropriate active conductances. We generated a good match of our simulations with DCN current clamp data we recorded in acute slices, including the heterogeneity in the rebound responses. We then examined how inhibitory and excitatory synaptic input interacted with these intrinsic conductances to control DCN firing. We found that the output spiking of the model reflected the ongoing balance of excitatory and inhibitory input rates and that changing the level of inhibition performed an additive operation. Rebound firing following strong Purkinje cell input bursts was also possible, but only if the chloride reversal potential was more negative than −70 mV to allow de-inactivation of rebound currents. Fast rebound bursts due to T-type calcium current and slow rebounds due to persistent sodium current could be differentially regulated by synaptic input, and the pattern of these rebounds was further influenced by HCN current. Our findings suggest that active properties of DCN neurons could play a crucial role for signal processing in the cerebellum

Cerebellar Nuclear Neurons Use Time and Rate Coding to Transmit Purkinje Neuron Pauses

Copyright: © 2015 Sudhakar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedNeurons of the cerebellar nuclei convey the final output of the cerebellum to their targets in various parts of the brain. Within the cerebellum their direct upstream connections originate from inhibitory Purkinje neurons. Purkinje neurons have a complex firing pattern of regular spikes interrupted by intermittent pauses of variable length. How can the cerebellar nucleus process this complex input pattern? In this modeling study, we investigate different forms of Purkinje neuron simple spike pause synchrony and its influence on candidate coding strategies in the cerebellar nuclei. That is, we investigate how different alignments of synchronous pauses in synthetic Purkinje neuron spike trains affect either time-locking or rate-changes in the downstream nuclei. We find that Purkinje neuron synchrony is mainly represented by changes in the firing rate of cerebellar nuclei neurons. Pause beginning synchronization produced a unique effect on nuclei neuron firing, while the effect of pause ending and pause overlapping synchronization could not be distinguished from each other. Pause beginning synchronization produced better time-locking of nuclear neurons for short length pauses. We also characterize the effect of pause length and spike jitter on the nuclear neuron firing. Additionally, we find that the rate of rebound responses in nuclear neurons after a synchronous pause is controlled by the firing rate of Purkinje neurons preceding it.Peer reviewedFinal Published versio

Nonspecific synaptic plasticity improves the recognition of sparse patterns degraded by local noise

Safaryan, K. et al. Nonspecific synaptic plasticity improves the recognition of sparse patterns degraded by local noise. Sci. Rep. 7, 46550; doi: 10.1038/srep46550 (2017). This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ © The Author(s) 2017.Many forms of synaptic plasticity require the local production of volatile or rapidly diffusing substances such as nitric oxide. The nonspecific plasticity these neuromodulators may induce at neighboring non-active synapses is thought to be detrimental for the specificity of memory storage. We show here that memory retrieval may benefit from this non-specific plasticity when the applied sparse binary input patterns are degraded by local noise. Simulations of a biophysically realistic model of a cerebellar Purkinje cell in a pattern recognition task show that, in the absence of noise, leakage of plasticity to adjacent synapses degrades the recognition of sparse static patterns. However, above a local noise level of 20 %, the model with nonspecific plasticity outperforms the standard, specific model. The gain in performance is greatest when the spatial distribution of noise in the input matches the range of diffusion-induced plasticity. Hence non-specific plasticity may offer a benefit in noisy environments or when the pressure to generalize is strong.Peer reviewe

Computational Models of Timing Mechanisms in the Cerebellar Granular Layer

A long-standing question in neuroscience is how the brain controls movement that requires precisely timed muscle activations. Studies using Pavlovian delay eyeblink conditioning provide good insight into this question. In delay eyeblink conditioning, which is believed to involve the cerebellum, a subject learns an interstimulus interval (ISI) between the onsets of a conditioned stimulus (CS) such as a tone and an unconditioned stimulus such as an airpuff to the eye. After a conditioning phase, the subject’s eyes automatically close or blink when the ISI time has passed after CS onset. This timing information is thought to be represented in some way in the cerebellum. Several computational models of the cerebellum have been proposed to explain the mechanisms of time representation, and they commonly point to the granular layer network. This article will review these computational models and discuss the possible computational power of the cerebellum

Sensory Stimulation-Dependent Plasticity in the Cerebellar Cortex of Alert Mice

In vitro studies have supported the occurrence of cerebellar long-term depression (LTD), an interaction between the parallel fibers and Purkinje cells (PCs) that requires the combined activation of the parallel and climbing fibers. To demonstrate the existence of LTD in alert animals, we investigated the plasticity of local field potentials (LFPs) evoked by electrical stimulation of the whisker pad. The recorded LFP showed two major negative waves corresponding to trigeminal (broken into the N2 and N3 components) and cortical responses. PC unitary extracellular recording showed that N2 and N3 occurred concurrently with PC evoked simple spikes, followed by an evoked complex spike. Polarity inversion of the N3 component at the PC level and N3 amplitude reduction after electrical stimulation of the parallel fiber volley applied on the surface of the cerebellum 2 ms earlier strongly suggest that N3 was related to the parallel fiber–PC synapse activity. LFP measurements elicited by single whisker pad stimulus were performed before and after trains of electrical stimuli given at a frequency of 8 Hz for 10 min. We demonstrated that during this later situation, the stimulation of the PC by parallel and climbing fibers was reinforced. After 8-Hz stimulation, we observed long-term modifications (lasting at least 30 min) characterized by a specific decrease of the N3 amplitude accompanied by an increase of the N2 and N3 latency peaks. These plastic modifications indicated the existence of cerebellar LTD in alert animals involving both timing and synaptic modulations. These results corroborate the idea that LTD may underlie basic physiological functions related to calcium-dependent synaptic plasticity in the cerebellum

A Synoptical Classification of the Bivalvia (Mollusca)

The following classification summarizes the suprageneric taxono-my of the Bivalvia for the upcoming revision of the Bivalvia volumes of the Treatise on Invertebrate Paleontology, Part N. The development of this classification began with Carter (1990a), Campbell, Hoeks-tra, and Carter (1995, 1998), Campbell (2000, 2003), and Carter, Campbell, and Campbell (2000, 2006), who, with assistance from the United States National Science Foundation, conducted large-scale morphological phylogenetic analyses of mostly Paleozoic bivalves, as well as molecular phylogenetic analyses of living bivalves. Dur-ing the past several years, their initial phylogenetic framework has been revised and greatly expanded through collaboration with many students of bivalve biology and paleontology, many of whom are coauthors. During this process, all available sources of phylogenetic information, including molecular, anatomical, shell morphological, shell microstructural, bio- and paleobiogeographic as well as strati-graphic, have been integrated into the classification. The more recent sources of phylogenetic information include, but are not limited to, Carter (1990a), Malchus (1990), J. Schneider (1995, 1998a, 1998b, 2002), T. Waller (1998), Hautmann (1999, 2001a, 2001b), Giribet and Wheeler (2002), Giribet and Distel (2003), Dreyer, Steiner, and Harper (2003), Matsumoto (2003), Harper, Dreyer, and Steiner (2006), Kappner and Bieler (2006), Mikkelsen and others (2006), Neulinger and others (2006), Taylor and Glover (2006), Kříž (2007), B. Morton (2007), Taylor, Williams, and Glover (2007), Taylor and others (2007), Giribet (2008), and Kirkendale (2009). This work has also benefited from the nomenclator of bivalve families by Bouchet and Rocroi (2010) and its accompanying classification by Bieler, Carter, and Coan (2010).This classification strives to indicate the most likely phylogenetic position for each taxon. Uncertainty is indicated by a question mark before the name of the taxon. Many of the higher taxa continue to undergo major taxonomic revision. This is especially true for the superfamilies Sphaerioidea and Veneroidea, and the orders Pectinida and Unionida. Because of this state of flux, some parts of the clas-sification represent a compromise between opposing points of view. Placement of the Trigonioidoidea is especially problematic. This Mesozoic superfamily has traditionally been placed in the order Unionida, as a possible derivative of the superfamily Unionoidea (see Cox, 1952; Sha, 1992, 1993; Gu, 1998; Guo, 1998; Bieler, Carter, & Coan, 2010). However, Chen Jin-hua (2009) summarized evi-dence that Trigonioidoidea was derived instead from the superfamily Trigonioidea. Arguments for these alternatives appear equally strong, so we presently list the Trigonioidoidea, with question, under both the Trigoniida and Unionida, with the contents of the superfamily indicated under the Trigoniida.Fil: Carter, Joseph G.. University of North Carolina; Estados UnidosFil: Altaba, Cristian R.. Universidad de las Islas Baleares; EspañaFil: Anderson, Laurie C.. South Dakota School of Mines and Technology; Estados UnidosFil: Araujo, Rafael. Consejo Superior de Investigaciones Cientificas. Museo Nacional de Ciencias Naturales; EspañaFil: Biakov, Alexander S.. Russian Academy of Sciences; RusiaFil: Bogan, Arthur E.. North Carolina State Museum of Natural Sciences; Estados UnidosFil: Campbell, David. Paleontological Research Institution; Estados UnidosFil: Campbell, Matthew. Charleston Southern University; Estados UnidosFil: Chen, Jin Hua. Chinese Academy of Sciences. Nanjing Institute of Geology and Palaeontology; República de ChinaFil: Cope, John C. W.. National Museum of Wales. Department of Geology; Reino UnidoFil: Delvene, Graciela. Instituto Geológico y Minero de España; EspañaFil: Dijkstra, Henk H.. Netherlands Centre for Biodiversity; Países BajosFil: Fang, Zong Jie. Chinese Academy of Sciences; República de ChinaFil: Gardner, Ronald N.. No especifica;Fil: Gavrilova, Vera A.. Russian Geological Research Institute; RusiaFil: Goncharova, Irina A.. Russian Academy of Sciences; RusiaFil: Harries, Peter J.. University of South Florida; Estados UnidosFil: Hartman, Joseph H.. University of North Dakota; Estados UnidosFil: Hautmann, Michael. Paläontologisches Institut und Museum; SuizaFil: Hoeh, Walter R.. Kent State University; Estados UnidosFil: Hylleberg, Jorgen. Institute of Biology; DinamarcaFil: Jiang, Bao Yu. Nanjing University; República de ChinaFil: Johnston, Paul. Mount Royal University; CanadáFil: Kirkendale, Lisa. University Of Wollongong; AustraliaFil: Kleemann, Karl. Universidad de Viena; AustriaFil: Koppka, Jens. Office de la Culture. Section d’Archéologie et Paléontologie; SuizaFil: Kříž, Jiří. Czech Geological Survey. Department of Sedimentary Formations. Lower Palaeozoic Section; República ChecaFil: Machado, Deusana. Universidade Federal do Rio de Janeiro; BrasilFil: Malchus, Nikolaus. Institut Català de Paleontologia; EspañaFil: Márquez Aliaga, Ana. Universidad de Valencia; EspañaFil: Masse, Jean Pierre. Universite de Provence; FranciaFil: McRoberts, Christopher A.. State University of New York at Cortland. Department of Geology; Estados UnidosFil: Middelfart, Peter U.. Australian Museum; AustraliaFil: Mitchell, Simon. The University of the West Indies at Mona; JamaicaFil: Nevesskaja, Lidiya A.. Russian Academy of Sciences; RusiaFil: Özer, Sacit. Dokuz Eylül University; TurquíaFil: Pojeta, John Jr.. National Museum of Natural History; Estados UnidosFil: Polubotko, Inga V.. Russian Geological Research Institute; RusiaFil: Pons, Jose Maria. Universitat Autònoma de Barcelona; EspañaFil: Popov, Sergey. Russian Academy of Sciences; RusiaFil: Sanchez, Teresa Maria. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba; ArgentinaFil: Sartori, André F.. Field Museum of National History; Estados UnidosFil: Scott, Robert W.. Precision Stratigraphy Associates; Estados UnidosFil: Sey, Irina I.. Russian Geological Research Institute; RusiaFil: Signorelli, Javier Hernan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; ArgentinaFil: Silantiev, Vladimir V.. Kazan Federal University; RusiaFil: Skelton, Peter W.. Open University. Department of Earth and Environmental Sciences; Reino UnidoFil: Steuber, Thomas. The Petroleum Institute; Emiratos Arabes UnidosFil: Waterhouse, J. Bruce. No especifica;Fil: Wingard, G. Lynn. United States Geological Survey; Estados UnidosFil: Yancey, Thomas. Texas A&M University; Estados Unido