Relevant bibliographies by topics / Neuron activity

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Neuron activity'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Neuron activity"

1

Quinlan, E. M., K. Gregory, and A. D. Murphy. "An identified glutamatergic interneuron patterns feeding motor activity via both excitation and inhibition." Journal of Neurophysiology 73, no. 3 (March 1, 1995): 945–56. http://dx.doi.org/10.1152/jn.1995.73.3.945.

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1. Previously we demonstrated that glutamate is an important neurotransmitter in the CNS of Helisoma. Exogenous glutamate applied to the buccal ganglia mimicked both the excitatory and inhibitory effects of subunit 2 (S2) of the tripartite central pattern generator (CPG) on S2 postsynaptic motor neurons. Here we identify buccal interneuron B2 as an S2 interneuron by utilizing a combination of electrophysiology, pharmacology, and intracellular staining. In addition, neurons that were electrophysiologically and morphologically characterized as neuron B2 demonstrated antiglutamate immunoreactivity, suggesting that neuron B2 is a source of endogenous glutamate in the buccal ganglia. 2. Depolarization of neuron B2 evoked excitatory postsynaptic potentials in motor neurons excited by S2. The excitatory effects of B2 depolarization and S2 activation were reversibly antagonized by the ionotropic glutamate receptor antagonist 6-cyano-7-nitro-quinoxaline-2,3-dione, similar to the antagonism shown previously for application of exogenous glutamate. Depolarization of neuron B2 also evoked inhibitory postsynaptic potentials in motor neurons inhibited by S2. When such motor neurons were maintained in isolated cell culture, application of exogenous glutamate produced a direct hyperpolarization of the membrane potential. 3. The activity of neuron B2 is necessary for the production of the standard pattern of buccal motor neuron activity, which underlies functional feeding movements. The subunits of the tripartite buccal CPG must be active in the temporal sequence S1-S2-S3 to produce the standard feeding pattern. Rhythmic inhibition from neuron B2 terminated activity in S1 postsynaptic motor neurons and entrained the frequency of activity in S3 postsynaptic motor neurons. Hyperpolarization of neuron B2 disrupted the production of the standard motor pattern by eliminating S2 postsynaptic potentials in identified buccal motor neurons, thereby prolonging S1 activity and disrupting S3 bursting. 4. These data support the hypothesis that S2 neuron B2 is glutamatergic and demonstrate that glutamatergic transmission, and especially inhibition, is fundamental to the production of behaviorally critical motor neuron activity patterns in Helisoma.
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Segal, M. M. "Epileptiform activity in microcultures containing one excitatory hippocampal neuron." Journal of Neurophysiology 65, no. 4 (April 1, 1991): 761–70. http://dx.doi.org/10.1152/jn.1991.65.4.761.

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1. Paroxysmal depolarizing shifts (PDSs) occur during interictal epileptiform activity. Sustained depolarizations are characteristic of ictal activity, and events resembling PDSs also occur during the sustained depolarizations. To study these elements of epileptiform activity in a simpler context, I used the in vitro chronic-excitatory-block model of epilepsy of Furshpan and Potter and the microculture technique of Segal and Furshpan. 2. Intracellular recordings were made from 93 single-neuron microcultures. Forty of these solitary neurons were excitatory, their action potentials were replaced by PDS-like events or sustained depolarizations as kynurenate was removed from the perfusion solution. PDS-like events were similar to PDSs in intact cortex, mass cultures, and microcultures with more than one neuron. Small voltage fluctuations were also seen in solitary excitatory neurons in the absence of recorded action potentials. Sustained depolarizations developed in 5 of the 40 excitatory neurons. Forty-eight of the 93 solitary neurons were inhibitory, with bicuculline-sensitive hyperpolarizations after the action potential (ascribable to gamma-aminobutyric acid-A autapses). None of the solitary inhibitory neurons displayed sustained depolarizations. Five of the 93 neurons were insensitive to both kynurenate and bicuculline and were not placed in either the excitatory or the inhibitory category. 3. Both N-methyl-D-aspartate (NMDA) and non-NMDA glutamate receptors contributed to the PDS-like events and sustained depolarizations. Only a non-NMDA glutamate receptor component was evident for the small voltage fluctuations. 4. Intracellular recordings were also made from two-neuron microcultures, each containing one excitatory neuron and one inhibitory neuron. Sustained depolarizations developed in five microcultures, in each case only in the excitatory neuron.
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Wang, Rubin, Ichiro Tsuda, and Zhikang Zhang. "A New Work Mechanism on Neuronal Activity." International Journal of Neural Systems 25, no. 03 (April 8, 2015): 1450037. http://dx.doi.org/10.1142/s0129065714500373.

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By re-examining the neuronal activity energy model, we show the inadequacies in the current understanding of the energy consumption associated with neuron activity. Specifically, we show computationally that a neuron first absorbs and then consumes energy during firing action potential, and this result cannot be produced from any current neuron models or biological neural networks. Based on this finding, we provide an explanation for the observation that when neurons are excited in the brain, blood flow increases significantly while the incremental oxygen consumption is very small. We can also explain why external stimulation and perception emergence are synchronized. We also show that negative energy presence in neurons at the sub-threshold state is an essential reason that leads to blood flow incremental response time in the brain rather than neural excitation to delay.
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Weaver, Adam L., and Scott L. Hooper. "Follower Neurons in Lobster (Panulirus interruptus) Pyloric Network Regulate Pacemaker Period in Complementary Ways." Journal of Neurophysiology 89, no. 3 (March 1, 2003): 1327–38. http://dx.doi.org/10.1152/jn.00704.2002.

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Distributed neural networks (ones characterized by high levels of interconnectivity among network neurons) are not well understood. Increased insight into these systems can be obtained by perturbing network activity so as to study the functions of specific neurons not only in the network's “baseline” activity but across a range of network activities. We applied this technique to study cycle period control in the rhythmic pyloric network of the lobster, Panulirus interruptus. Pyloric rhythmicity is driven by an endogenous oscillator, the Anterior Burster (AB) neuron. Two network neurons feed back onto the pacemaker, the Lateral Pyloric (LP) neuron by inhibition and the Ventricular Dilator (VD) neuron by electrical coupling. LP and VD neuron effects on pyloric cycle period can be studied across a range of periods by altering period by injecting current into the AB neuron and functionally removing (by hyperpolarization) the LP and VD neurons from the network at each period. Within a range of pacemaker periods, the LP and VD neurons regulate period in complementary ways. LP neuron removal speeds the network and VD neuron removal slows it. Outside this range, network activity is disrupted because the LP neuron cannot follow slow periods, and the VD neuron cannot follow fast periods. These neurons thus also limit, in complementary ways, normal pyloric activity to a certain period range. These data show that follower neurons in pacemaker networks can play central roles in controlling pacemaker period and suggest that in some cases specific functions can be assigned to individual network neurons.
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Spencer, Robert M., and Dawn M. Blitz. "Network feedback regulates motor output across a range of modulatory neuron activity." Journal of Neurophysiology 115, no. 6 (June 1, 2016): 3249–63. http://dx.doi.org/10.1152/jn.01112.2015.

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Modulatory projection neurons alter network neuron synaptic and intrinsic properties to elicit multiple different outputs. Sensory and other inputs elicit a range of modulatory neuron activity that is further shaped by network feedback, yet little is known regarding how the impact of network feedback on modulatory neurons regulates network output across a physiological range of modulatory neuron activity. Identified network neurons, a fully described connectome, and a well-characterized, identified modulatory projection neuron enabled us to address this issue in the crab ( Cancer borealis) stomatogastric nervous system. The modulatory neuron modulatory commissural neuron 1 (MCN1) activates and modulates two networks that generate rhythms via different cellular mechanisms and at distinct frequencies. MCN1 is activated at rates of 5–35 Hz in vivo and in vitro. Additionally, network feedback elicits MCN1 activity time-locked to motor activity. We asked how network activation, rhythm speed, and neuron activity levels are regulated by the presence or absence of network feedback across a physiological range of MCN1 activity rates. There were both similarities and differences in responses of the two networks to MCN1 activity. Many parameters in both networks were sensitive to network feedback effects on MCN1 activity. However, for most parameters, MCN1 activity rate did not determine the extent to which network output was altered by the addition of network feedback. These data demonstrate that the influence of network feedback on modulatory neuron activity is an important determinant of network output and feedback can be effective in shaping network output regardless of the extent of network modulation.
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Suri, Roland E., and Wolfram Schultz. "Temporal Difference Model Reproduces Anticipatory Neural Activity." Neural Computation 13, no. 4 (April 1, 2001): 841–62. http://dx.doi.org/10.1162/089976601300014376.

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Anticipatory neural activity preceding behaviorally important events has been reported in cortex, striatum, and midbrain dopamine neurons. Whereas dopamine neurons are phasically activated by reward-predictive stimuli, anticipatory activity of cortical and striatal neurons is increased during delay periods before important events. Characteristics of dopa-mine neuron activity resemble those of the prediction error signal of the temporal difference (TD) model of Pavlovian learning (Sutton & Barto, 1990). This study demonstrates that the prediction signal of the TD model reproduces characteristics of cortical and striatal anticipatory neural activity. This finding suggests that tonic anticipatory activities may reflect prediction signals that are involved in the processing of dopamine neuron activity.
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Park, Jihoon, Koki Ichinose, Yuji Kawai, Junichi Suzuki, Minoru Asada, and Hiroki Mori. "Macroscopic Cluster Organizations Change the Complexity of Neural Activity." Entropy 21, no. 2 (February 23, 2019): 214. http://dx.doi.org/10.3390/e21020214.

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In this study, simulations are conducted using a network model to examine how the macroscopic network in the brain is related to the complexity of activity for each region. The network model is composed of multiple neuron groups, each of which consists of spiking neurons with different topological properties of a macroscopic network based on the Watts and Strogatz model. The complexity of spontaneous activity is analyzed using multiscale entropy, and the structural properties of the network are analyzed using complex network theory. Experimental results show that a macroscopic structure with high clustering and high degree centrality increases the firing rates of neurons in a neuron group and enhances intraconnections from the excitatory neurons to inhibitory neurons in a neuron group. As a result, the intensity of the specific frequency components of neural activity increases. This decreases the complexity of neural activity. Finally, we discuss the research relevance of the complexity of the brain activity.
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Hu, Xiaoyu, and Chongxin Liu. "Bursting and Synchronization of Coupled Neurons under Electromagnetic Radiation." Complexity 2019 (December 4, 2019): 1–10. http://dx.doi.org/10.1155/2019/4835379.

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Bursting is an important firing activity of neurons, which is caused by a slow process that modulates fast spiking activity. Based on the original second-order Morris-Lecar neuron model, an improved third-order Morris-Lecar neuron model can produce bursting activity is proposed, in which the effect of electromagnetic radiation is considered as a slow process and the original equation of Morris-Lecar neuron model as a fast process. Extensive numerical simulation results show that the improved neuron model can produce different types of bursting, and bursting activity shows a deep dependence on system parameters and electromagnetic radiation parameters. In addition, synchronization transitions of identical as well as no-identical coupled third-order Morris-Lecar neurons are studied, the results show that identical coupled neurons experience a complex synchronization process and reach complete synchronization finally with the increase of coupling intensity. For no-identical coupled neurons, only anti-phase synchronization and in-phase synchronization can be reached. The studies of bursting activity of single neuron and synchronization transition of coupled neurons have important guiding significance for further understanding the information processing of neurons and collective behaviors in neuronal network under electromagnetic radiation environment.
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Hooper, S. L., and M. Moulins. "Cellular and synaptic mechanisms responsible for a long-lasting restructuring of the lobster pyloric network." Journal of Neurophysiology 64, no. 5 (November 1, 1990): 1574–89. http://dx.doi.org/10.1152/jn.1990.64.5.1574.

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1. In the lobster Palinurus vulgaris a sensory input in the lateral posterolateral nerve (lpln) of the stomatogastric nervous system (STS) is able to turn on the cardiac sac (CS) network and to induce dramatic long-lasting alterations in the output of the pyloric network. This long-lasting alteration of pyloric network output consists primarily of changes in the activity of the two neurons that innervate the muscles of the cardiopyloric valve of the stomach, with the dilator neuron (the ventricular dilator, VD) transferring from the pyloric network to the CS network and the constrictor neuron (the inferior cardiac, IC) shifting to fire earlier in the pyloric pattern. 2. The inferior ventricular (IV) neurons of the CS network make complex multiaction synaptic connections onto several pyloric neurons in a related species, Panulirus interruptus. We show that many of the short-term alterations in pyloric activity observed during CS network bursts in Palinurus are due to similar IV neuron synaptic connections. However, the long-lasting effects of lpln stimulation on pyloric output are not due to this synaptic input, because 1) direct activation of the IV neurons does not induce long-lasting changes in pyloric activity and 2) pharmacologic disconnection of this synaptic input does not abolish lpln stimulation's long-lasting effects. Lpln stimulation therefore activates two different neuronal inputs to the pyloric network. 3. The transfer of the VD neuron from the pyloric to the CS network is the result of the concerted actions of these two inputs. Lpln stimulation turns on the CS network, and the IV neurons of the CS network excite the VD neuron and ensure it fires with the CS network. The second neuronal input (that not involving known CS network neurons) abolishes in a long-lasting fashion the VD neuron regenerative (plateau) properties, and thus suppresses the ability of the VD neuron to participate in the pyloric rhythmic pattern between CS network bursts. 4. Experimental manipulation of VD neuron activity can both mimic and reverse the effects of lpln stimulation on the IC neuron. The changes in IC neuron activity are therefore not due to direct lpln-activated synaptic input onto the IC neuron, but instead are indirect "network" effects arising from the changes in VD neuron activity.
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Weaver, Adam L., and Scott L. Hooper. "Relating Network Synaptic Connectivity and Network Activity in the Lobster (Panulirus interruptus) Pyloric Network." Journal of Neurophysiology 90, no. 4 (October 2003): 2378–86. http://dx.doi.org/10.1152/jn.00705.2002.

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The lobster pyloric network has a densely interconnected synaptic connectivity pattern, and the role individual synapses play in generating network activity is consequently difficult to discern. We examined this issue by quantifying the effect on pyloric network phasing and spiking activity of removing the Lateral Pyloric (LP) and Ventricular Dilator (VD) neurons, which synapse onto almost all pyloric neurons. A confounding factor in this work is that LP and VD neuron removal alters pyloric cycle period. To determine the effects of LP and VD neuron removal on pyloric activity independent of these period alterations, we altered network period by current injection into a pyloric pacemaker neuron, hyperpolarized the LP or VD neuron to functionally remove each from the network, and plotted various measures of pyloric neuron activity against period with and without the LP or VD neuron. In normal physiological saline, in many (or most) cases removing either neuron had surprisingly little effect on the activity of its postsynaptic partners, which suggests that under these conditions these neurons play a relatively small role in determining pyloric activity. In the cases in which removal did alter postsynaptic activity, the effects were inconsistent across preparations, which suggests that either despite producing very similar neural outputs, pyloric networks from different animals have different cellular and synaptic properties, or some synapses contribute to network activity only under certain modulatory conditions, and the “baseline” level of modulatory influence the network receives from higher centers varies from animal to animal.
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Dissertations / Theses on the topic "Neuron activity"

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Ponce, Alvarez Adrián. "Probabilistic models for studying variability in single-neuron and neuronal ensemble activity." Thesis, Aix-Marseille 2, 2010. http://www.theses.fr/2010AIX20706.

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Une des caractéristiques les plus singulières de l’activité corticale est son degré élevé de variabilité. Ma thèse dedoctorat s’est focalisée sur l’étude de (i) l’irrégularité des intervalles entre potentiels d’action (PAs)successivement émis par un neurone, et (ii) la variabilité dans l’évolution temporelle de l’activité d’un ensemblede neurones. Premièrement, j’ai étudié l’irrégularité des neurones enregistrés dans le cortex moteur de singesmacaques performant une tâche d’estimation du temps et de préparation à l’action. J’ai montré que l’irrégularitén’est pas un paramètre libre de l’activité neuronale, contrairement au taux de PAs, mais est déterminée par lescontraintes structurelles des réseaux neuronaux. Deuxièmement, j’ai utilisé le modèle de Markov caché (MMC)pour analyser l’activité d’ensembles de neurones enregistrés dans plusieurs aires corticales, sensorielles etmotrices, de singes exécutant une tâche de discrimination tactile. J’ai montré que les processus sensoriels etdécisionnels sont distribués dans plusieurs aires corticales. Les résultats suggèrent que l’action et la décision surlaquelle elle est basée sont reliées par une cascade d’évènements non stationnaires et stochastiques. Finalement,j’ai utilisé le MMC pour caractériser l’activité spontanée d’un ensemble de neurones du cortex préfrontal d’unrat. Les résultats montrèrent que l’alternance entre les états UP et DOWN est un processus stochastique etdynamique. La variabilité apparaît donc aussi bien pendant l’activité spontanée que pendant le comportementactif et semble être contrainte par des facteurs structurels qui, à leur tour, contraignent le mode d’opération desréseaux neuronaux
A hallmark of cortical activity is its high degree of variability. The present work focused on (i) the variability ofintervals between spikes that single neurons emit, called spike time irregularity (STI), and (ii) the variability inthe temporal evolution of the collective neuronal activity. First, I studied the STI of macaque motor corticalneurons during time estimation and movement preparation. I found that although the firing rate of the neuronstransmitted information about these processes, the STI of a neuron is not flexible and is determined by thebalance of excitatory and inhibitory inputs. These results were obtained by means of an irregularity measure thatI compared to other existing measures. Second, I analyzed the neuronal ensemble activity of severalsomatosensory and motor cortical areas of macaques during tactile discrimination. I showed that ensembleactivity can be effectively described by the Hidden Markov Model (HMM). Both sensory and decision-makingprocesses were distributed across many areas. Moreover, I showed that decision-related changes in neuronalactivity rely on a noise-driven mechanism and that the maintenance of the decision relies on transient dynamics,subtending the conversion of a decision into an action. Third, I characterized the statistics of spontaneous UP andDOWN states in the prefrontal cortex of a rat, using the HMM. I showed that state alternation is stochastic andthe activity during UP states is dynamic. Hence, variability is prominent both during active behavior andspontaneous activity and is determined by structural factors, thus rending it inherent to cortical organization andshaping the function of neural networks
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Sitnikov, Sergey. "Activity dependent neuron-glia interactions in health and disease." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708663.

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Hanna, Brian Dale. "Control of sympathetic neuron and cardiovascular effector activity by carbon dioxide." Thesis, McGill University, 1988. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=75884.

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The effect of CO$ sb2$ on sympathetic preganglionic neuron (SPN) activity and hindlimb neurogenic vascular resistance (HVR) was investigated in cats. Both variables increased as continuous functions of systemic arterial PCO$ sb2$, from hypocapnia to hypercapnia. Eucapnic PCO$ sb2$ was responsible for a significant component of SPN background activity and HVR. The carotid body chemoreceptors were shown to contribute to the CO$ sb2$ response of SPNs, since section of the carotid sinus nerves, after prior section of the aortic nerves, reduced the CO$ sb2$ response of SPNs. A significant ventral medullary contribution to this CO$ sb2$ relationship was demonstrated, since the CO$ sb2$ response persisted after peripheral chemodenervation, was lost after acute spinal transsection and was markedly attenuated by cold-block of either the entire exposed ventral surface of the medulla or the specific bilateral area "S". Superficial ventral medullary chemoreceptor involvement was confirmed, since changes in HVR, comparable to those caused by altering arterial PCO$ sb2$, occurred with changes in the (H$ sp+$) and PCO$ sb2$ in artificial CSF perfusing these structures.
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Spencer, Robert Michael. "Rhythmic motor system control by projection neuron activity pattern and rate." Miami University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=miami1461269867.

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Soofi, Wafa Ahmed. "Regulation of rhythmic activity in the stomatogastric ganglion of decapod crustaceans." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53440.

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Neuronal networks produce reliable functional output throughout the lifespan of an animal despite ceaseless molecular turnover and a constantly changing environment. The cellular and molecular mechanisms underlying the ability of these networks to maintain functional stability remain poorly understood. Central pattern generating circuits produce a stable, predictable rhythm, making them ideal candidates for studying mechanisms of activity maintenance. By identifying and characterizing the regulators of activity in small neuronal circuits, we not only obtain a clearer understanding of how neural activity is generated, but also arm ourselves with knowledge that may eventually be used to improve medical care for patients whose normal nervous system activity has been disrupted through trauma or disease. We utilize the pattern-generating pyloric circuit in the crustacean stomatogastric nervous system to investigate the general scientific question: How are specific aspects of rhythmic activity regulated in a small neuronal network? The first aim of this thesis poses this question in the context of a single neuron. We used a single-compartment model neuron database to investigate whether co-regulation of ionic conductances supports the maintenance of spike phase in rhythmically bursting “pacemaker” neurons. The second aim of the project extends the question to a network context. Through a combination of computational and electrophysiology studies, we investigated how the intrinsic membrane conductances of the pacemaker neuron influence its response to synaptic input within the framework of the Phase Resetting Curve (PRC). The third aim of the project further extends the question to a systems-level context. We examined how ambient temperatures affect the stability of the pyloric rhythm in the intact, behaving animal. The results of this work have furthered our understanding of the principles underlying the long-term stability of neuronal network function.
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Rüppell, Maximilian Alexander [Verfasser], and Ulrich [Akademischer Betreuer] Egert. "Single neuron dynamics and interaction in neuronal networks during synchronized spontaneous activity." Freiburg : Universität, 2019. http://d-nb.info/1237617685/34.

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Mitelut, Catalin C. "Characterizing single neuron activity patterns and dynamics using multi-scale spontaneous neuronal activity recordings of cat and mouse cortex." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/63570.

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Throughout most of the 20th century the brain has been studied as a reflexive system with ever improving recording methods being applied within a variety of sensory and behavioural paradigms. Yet the brains of most animals (and all mammals) are spontaneously active with incoming sensory stimuli modulating rather than driving neural activity. The aim of this thesis is to characterize spontaneous neural activity across multiple temporal and spatial scales relying on biophysical simulations, experiments and analysis of recordings from the visual cortex of cats and dorsal cortex and thalamus of mouse. Biophysically detailed simulations yielded novel datasets for testing spike sorting algorithms which are critical for isolating single neuron activity. Sorting algorithms tested provided low error rates with operator skill being as important as sorting suite. Simulated datasets have similar characteristics to in vivo acquired data and ongoing larger-scope efforts are proposed for developing the next generation of spike sorting algorithms and extracellular probes. Single neuron spontaneous activity was correlated to dorsal cortex neural activity in mice. Spike-triggered-maps revealed that spontaneously firing cortical neurons were co-activated with homotopic and mono-synaptically connected cortical areas, whereas thalamic neurons co-activated with more diversely connected areas. Both bursting and tonic firing modes yielded similar maps and the time courses of spike-triggered-maps revealed distinct patterns suggesting such dynamics may constitute intrinsic single neuron properties. The mapping technique extends previous work to further link spontaneous neural activity across temporal and spatial scales and suggests additional avenues of investigation. Synchronized state cat visual and mouse sensory cortex electrophysiological recordings revealed that spontaneously occurring activity UP-state transitions fall into stereotyped classes of events that can be grouped. Single visual cortex neurons active during UP-state transitions fire in a partially preserved order extending previous findings on high firing rate neurons in rat somatosensory and auditory cortex. The firing order for many neurons changes over periods longer than 30-minutes suggesting a complex non-stationary temporal neural code may underlie spontaneous and stimulus evoked neural activity. This thesis shows that ongoing spontaneous brain activity contains substantial structure that can be used to further our understanding of brain function.
Medicine, Faculty of
Graduate
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Haase, Stephanie Jean. "Exploring the relationship between circadian neuron activity patterns and behavioral output in Drosophila." Diss., University of Iowa, 2019. https://ir.uiowa.edu/etd/6754.

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Circadian clocks drive the daily patterns of behavior and physiology observed in most organisms. These internal clocks allow organisms to advantageously align their behavior to daily cycles in the environment such as light and temperature. The fruit fly Drosophila displays many robust, daily behavioral rhythms including discrete bouts of locomotor activity at dawn (i.e. morning activity) and dusk (i.e. evening activity). The molecular clocks that drive these daily activity bouts are found in approximately 150 circadian pacemaker neurons in the fly brain. Interestingly, the timing of the molecular clocks is synchronous between all pacemaker neurons, yet different subsets of these neurons appear to make quite different contributions to the regulation of morning vs. evening activity. It remains poorly understood how the molecular circadian clock drives daily rhythms in pacemaker neuron activity or how the activities of different groups of pacemaker neurons combine to produce complex behavioral output. The overall goal of this thesis is to characterize how different subsets of Drosophila pacemaker neurons contribute to daily behavioral regulation both individually and as a network. To examine daily patterns of neuronal activity in different groups of circadian clock neurons, we have established imaging methods using genetically encoded fluorescent sensors. For these sensors, changes in fluorescence levels correspond to changes in neuronal activity, thus allowing us to measure neuronal activity patterns in real-time and throughout the day. Using these tools, I have characterized the daily activity patterns of different groups of the clock neurons that agree with published rhythms in activity as assessed by patch-clamp electrophysiology and calcium imaging We have also used genetic and molecular approaches such as RNA interference (RNAi) to alter gene expression in a tissue-specific manner. These approaches allow us to manipulate the function of different groups of clock neurons and to determine how these manipulations affect rhythmic behavior and neuronal activity patterns. We have silenced different subsets of circadian pacemaker neurons using RNAi knockdown of the NARROW ABDOMEN (NA) sodium leak channel and identified a complex role for a subset of the posterior dorsal neurons 1 (DN1p) in regulating locomotor behavior. The DN1p are known to be involved in promoting morning behavior, and recent studies have shown that a subset of the DN1p regulate midday sleep bouts via downstream sleep regulating neurons. Our data suggest that the DN1p neurons likely suppress midday activity through inhibition of other circadian pacemaker neurons, and that this inhibitory role can be compensated for by light. Finally, we have also examined the intracellular mechanisms regulating circadian neuronal output. Rhythmic activity of the NA leak channel and its mammalian ortholog (NALCN) have been shown to contribute to daily excitability rhythms in circadian pacemaker neurons. We used temporally-restricted expression of RNAi and rescue constructs to identify a developmental requirement for the NA channel complex in Drosophila, and we demonstrate that channel complex proteins are very stable in the Drosophila brain. These data suggest that circadian regulation of the NA channel in adults may involve post-translational mechanisms that control activity and not just expression of the channel complex.
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Cherry, Cortnie Lauren. "Mechanisms of Depolarization Induced Dendritic Growth of Drosophila Motor Neurons." Diss., The University of Arizona, 2006. http://hdl.handle.net/10150/195475.

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MECHANISMS OF DEPOLARIZATION INDUCED DENDRITIC GROWTH OF DROSOPHILA MOTOR NEURONS Cortnie Lauren Cherry The University of Arizona, 2006 Director: Richard B. Levine The study of the cellular mechanisms underlying dendritic growth contributes to our understanding of nervous system development, function and disease. Electrical activity is a fundamental property of neurons, and this property is utilized to influence the mechanisms involved in dendrite formation and maturation. Here we employ the Drosophila transgenic system to quantify dendritic growth of identified motor neurons using both in vitro and in vivo techniques. Two novel techniques are introduced: one a system to visualize and measure dendritic outgrowth in cultured neurons using reporter proteins, and the other using 3D reconstruction to measure the arborization of identified motor neurons in vivo. Both transgenic manipulation of K+ channel function and depolarizing concentrations of K+ in the culture medium result in an acceleration of dendritic outgrowth. Depolarization induced outgrowth is dependent on Plectreurys Toxin (PLTX)-sensitive voltage-gated calcium current and protein synthesis in cultured motor neurons. Depolarization leads to direct induction of fos, a protein that heterodimerizes with jun to make the functional transcription factor, AP-1. Fos, but not jun, is necessary for basal levels of dendritic growth, while both are necessary for depolarization induced outgrowth. Over-expression of AP-1 in control cells is sufficient to cause dendritic outgrowth. The transcription factor Adf-1 is also necessary for basal and depolarization induced growth, but unlike AP-1 is not sufficient to cause outgrowth when over-expressed. Another transcription factor CREB, on the other hand, is not necessary for basal levels of dendritic growth, but is necessary for depolarization induced dendritic growth. Over-expression of CREB, like Adf-1, is not sufficient to cause dendritic outgrowth. These findings present exciting new techniques for the study of the field of dendritic regulation and contribute to our understanding of the cellular mechanisms underlying dendritic growth.
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Karameh, Fadi Nabih. "A model for cerebral cortical neuron group electric activity and its implications for cerebral function." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/27110.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
Includes bibliographical references (p. 245-265).
The electroencephalogram, or EEG, is a recording of the field potential generated by the electric activity of neuronal populations of the brain. Its utility has long been recognized as a monitor which reflects the vigilance states of the brain, such as arousal, drowsiness, and sleep stages. Moreover, it is used to detect pathological conditions such as seizures, to calibrate drug action during anesthesia, and to understand cognitive task signatures in healthy and abnormal subjects. Being an aggregate measure of neural activity, understanding the neural origins of EEG oscillations has been limited. With the advent of recording techniques, however, and as an influx of experimental evidence on cellular and network properties of the neocortex has become available, a closer look into the neuronal mechanisms for EEG generation is warranted. Accordingly, we introduce an effective neuronal skeleton circuit at a neuronal group level which could reproduce basic EEG-observable slow (< 15 Hz) oscillatory phenomenon. The circuit incorporates basic laminar organization principles of the cortex. Interaction between neuronal groups is defined on three scales, namely the columnar (0.3mm), columnar assembly (1-2mm) and areal (> 3mm). The effective circuit makes use of the dynamic properties of the layer 5 network to explain intra-cortically generated augmenting responses, restful alpha, slow wave (< 1Hz) oscillations, and disinhibition-induced seizures. Based on recent cellular evidence, we propose a hierarchical binding mechanism in tufted layer 5 cells which acts as a controlled gate between local cortical activity and inputs arriving from distant cortical areas. This gate is manifested by the switch in output firing patterns in tufted
(cont.) layer 5 cells between burst firing and regular spiking, with specific implications on local functional connectivity. This hypothesized mechanism provides an explanation of different alpha band (10Hz) oscillations observed recently under cognitive states. In particular, evoked alpha rhythms, which occur transiently after an input stimulus, could account for initial reogranization of local neural activity based on (mis)match between driving inputs and modulatory feedback of higher order cortical structures, or internal expectations. Emitted alpha rhythms, on the other hand, is an example of extreme attention where dominance of higher order control inputs could drive reorganization of local cortical activity. Finally, the model makes predictions on the role of burst firing patterns in tufted layer 5 cells in redefining local cortical dynamics, based on internal representations, as a prelude to high frequency oscillations observed in various sensory systems during cognition.
by Fadi Nabih Karameh.
Ph.D.
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Books on the topic "Neuron activity"

1

Lowe, Michael R. Perturbing the impulse activity of a single identified neuron augments the formation of long-term memory in a molluscan semi-intact preparation. St. Catharines, Ont: Brock University, Dept. of Biological Sciences, 2004.

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Brette, Romain, and Alain Destexhe. Handbook of neural activity measurement. Cambridge: Cambridge University Press, 2012.

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Brette, Romain, and Alain Destexhe, eds. Handbook of Neural Activity Measurement. Cambridge: Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511979958.

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Gomez-Pilar, Javier. Characterization of Neural Activity Using Complex Network Theory. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-49900-6.

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Neural activity and the growth of the brain. Cambridge [England]: Cambridge University Press, 1994.

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Brain dynamics: Synchronization and activity patterns in pulse-coupled neural nets with delays and noise. Berlin: Springer, 2002.

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Iku, Nwamaka. Activity of brainstem cholinergic neurons during 22 kHz ultrasonic vocalization in rats. St. Catharines, Ont: Brock University, Dept. of Biological Sciences, 2007.

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Roth, Gérard. Clinical motor electroneurography: Evoked responses beyond the M-wave ectopic activity. Amsterdam: Elsevier, 2000.

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Viau, François. Effects of neural activity on oxidative and glycolytic enzyme activity and myosin heavy chain expression within diaphragm muscle fibers. Sudbury, Ont: Laurentian University, 1999.

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The neural basis of motor control. New York: Oxford University Press, 1986.

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Book chapters on the topic "Neuron activity"

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Haider, M., E. Groll-Knapp, and M. Trimmel. "Cortical DC-Shifts Related to Sustained Sensory Stimulation and Motor Activity." In From Neuron to Action, 59–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-02601-4_7.

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Deecke, L., W. Lang, F. Uhl, and I. Podreka. "Looking Where the Action Is: Negative DC Shifts as Indicators of Cortical Activity." In From Neuron to Action, 25–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-02601-4_3.

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Szabolcs, M., M. Kopp, and G. Schaden. "Carbonic Anhydrase Activity of Primary Afferent Neurons in Rat: Attempt at Marking Functionally Related Subpopulations." In The Primary Afferent Neuron, 87–91. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-0579-8_8.

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Hooper, Scott L., Christoph Guschlbauer, Marcus Blümel, Arndt von Twickel, Kevin H. Hobbs, Jeffrey B. Thuma, and Ansgar Büschges. "Muscles: Non-linear Transformers of Motor Neuron Activity." In Neuromechanical Modeling of Posture and Locomotion, 163–94. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-3267-2_6.

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Aguirre, Carlos, Pedro Pascual, Doris Campos, and Eduardo Serrano. "Single Neuron Transient Activity Detection by Means of Tomography." In Advances in Computational Intelligence, 49–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21501-8_7.

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Diekmann, V., B. Grözinger, K. P. Westphal, W. Reinke, and H. H. Kornhuber. "The Order of EEG Activity of Schizophrenic Patients and the Influence of Haloperidol and Biperidene on the EEG Order of Healthy Subjects." In From Neuron to Action, 495–500. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-02601-4_59.

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Magistretti, R. J., L. Pellerin, and P. G. Bittar. "Role of Neuron-Glia Interactions in Coupling Neuronal Activity to Energy Metabolism." In Neurochemistry, 555–60. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-5405-9_93.

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Zettlmeißl, H., S. Häusermann, H. Maurer, and H. H. Kornhuber. "Neurotoxic Metabolites of Tyrosine/Dopamine in Cerebrospinal Fluid and Serum of Normal Men and Neurological Patients. A Sign of the Activity of free Oxygen Radicals?" In From Neuron to Action, 519–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-02601-4_63.

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dos Santos, Filipa, Peter Andras, and K. P. Lam. "Towards an Accurate Identification of Pyloric Neuron Activity with VSDi." In Artificial Neural Networks and Machine Learning – ICANN 2017, 121–28. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68600-4_15.

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Renaud, Leo P. "Intrinsic and Synaptic Factors Regulating Mammalian Magnocellular Neurosecretory Neuron Activity." In Neurosecretion, 219–26. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4684-5502-1_24.

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Conference papers on the topic "Neuron activity"

1

Masanotti, D., P. Langlois, and J. Taylor. "A Method to Model Neuron Activity." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.260440.

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Masanotti, D., P. Langlois, and J. Taylor. "A Method to Model Neuron Activity." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.4398375.

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Luo, Qingming, Shaoqun Zeng, and Hui Gong. "Optical imaging of neural activity: from neuron to brain." In Third International Conference on Photonics and Imaging in Biology and Medicine, edited by Qingming Luo, Valery V. Tuchin, Min Gu, and Lihong V. Wang. SPIE, 2003. http://dx.doi.org/10.1117/12.546095.

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Kishimoto, Tatsunori, Suguru N. Kudoh, Takahisa Taguchi, and Chie Hosokawa. "Neuronal electrical activity induced by optical trapping of neurotransmitter receptors on neuron." In Optical Manipulation and Structured Materials Conference, edited by Takashige Omatsu, Hajime Ishihara, Keiji Sasaki, and Kishan Dholakia. SPIE, 2020. http://dx.doi.org/10.1117/12.2573759.

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Michel, C., R. Nouvian, C. Azevedo-Coste, J. L. Puel, and J. Bourien. "A computational model of the primary auditory neuron activity." In 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010). IEEE, 2010. http://dx.doi.org/10.1109/iembs.2010.5626273.

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Lai, Jui-Hsin, Ruichi Yu, and Ching-Yung Lin. "Neuron Activity Extraction and Network Analysis on Mouse Brain Videos." In 2016 IEEE International Symposium on Multimedia (ISM). IEEE, 2016. http://dx.doi.org/10.1109/ism.2016.0102.

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Lee, Seung Woo, and Shelley I. Fried. "Magnetic control of cortical pyramidal neuron activity using a micro-coil." In 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2015. http://dx.doi.org/10.1109/ner.2015.7146611.

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Dugue, Pierre, Regine Le Bouquin-Jeannes, and Gerard Faucon. "Proposal of Synchronization Indexes of Single Neuron Activity on Periodic Stimulus." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.367018.

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Borisyuk, G. N., R. M. Borisyuk, A. B. Kirillov, V. I. Kryukov, and W. Singer. "Modeling of oscillatory activity of neuron assemblies of the visual cortex." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137750.

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Aarts, A. A. A., H. P. Neves, I. Ulbert, L. Wittner, L. Grand, M. B. A. Fontes, S. Herwik, et al. "A 3D slim-base probe array for in vivo recorded neuron activity." In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2008. http://dx.doi.org/10.1109/iembs.2008.4650532.

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Reports on the topic "Neuron activity"

1

Woodward, Donald J. Neostriatal Neuronal Activity and Behavior. Fort Belvoir, VA: Defense Technical Information Center, February 1992. http://dx.doi.org/10.21236/ada248576.

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Smith, Alec, B. Douglas Bernheim, Colin Camerer, and Antonio Rangel. Neural Activity Reveals Preferences Without Choices. Cambridge, MA: National Bureau of Economic Research, August 2013. http://dx.doi.org/10.3386/w19270.

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McClanahan, Tucker C., and Daniel T. Wakeford. Study of Gamma-ray Production from Neutron-induced Activity on Spacecraft for DIORAMA. Office of Scientific and Technical Information (OSTI), August 2018. http://dx.doi.org/10.2172/1467294.

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Majewska, Anna, and Edward B. Brown. The Influence of Neuronal Activity on Breast Tumor Metastasis to the Brain. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada502596.

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Majewska, Anna K., and Edward B. Brown. The Influence of Neuronal Activity on Breast Tumor Metastasis to the Brain. Fort Belvoir, VA: Defense Technical Information Center, September 2009. http://dx.doi.org/10.21236/ada513293.

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Mpitsos, George J. Parallel Processing and Learning: Variability and Chaos in Self- Organization of Activity in Groups of Neurons. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada264224.

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Idakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang, and Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41302.

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Abstract:
Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR activity classes (i.e., agonist, antagonist, inactive, and inconclusive) at the same distribution rates amongst the training, validation and test subsets. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p < 0.001, ANOVA) by 22–27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Further in-depth analyses of chemical scaffolding shed insights on structural alerts for AR agonists/antagonists and inactive/inconclusive compounds, which may aid in future drug discovery and improvement of toxicity prediction modeling.
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Millington, William R. The Regulation of a Post-Translational Peptide Acetyltransferase: Strategies for Selectively Modifying the Biological Activity of Neural and Endocrine Peptides. Fort Belvoir, VA: Defense Technical Information Center, May 1991. http://dx.doi.org/10.21236/ada237891.

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Raychev, Nikolay. Can human thoughts be encoded, decoded and manipulated to achieve symbiosis of the brain and the machine. Web of Open Science, October 2020. http://dx.doi.org/10.37686/nsrl.v1i2.76.

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This article discusses the current state of neurointerface technologies, not limited to deep electrode approaches. There are new heuristic ideas for creating a fast and broadband channel from the brain to artificial intelligence. One of the ideas is not to decipher the natural codes of nerve cells, but to create conditions for the development of a new language for communication between the human brain and artificial intelligence tools. Theoretically, this is possible if the brain "feels" that by changing the activity of nerve cells that communicate with the computer, it is possible to "achieve" the necessary actions for the body in the external environment, for example, to take a cup of coffee or turn on your favorite music. At the same time, an artificial neural network that analyzes the flow of nerve impulses must also be directed at the brain, trying to guess the body's needs at the moment with a minimum number of movements. The most important obstacle to further progress is the problem of biocompatibility, which has not yet been resolved. This is even more important than the number of electrodes and the power of the processors on the chip. When you insert a foreign object into your brain, it tries to isolate itself from it. This is a multidisciplinary topic not only for doctors and psychophysiologists, but also for engineers, programmers, mathematicians. Of course, the problem is complex and it will be possible to overcome it only with joint efforts.
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