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      Spike and burst coding in thalamocortical relay cells

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          Abstract

          Mammalian thalamocortical relay (TCR) neurons switch their firing activity between a tonic spiking and a bursting regime. In a combined experimental and computational study, we investigated the features in the input signal that single spikes and bursts in the output spike train represent and how this code is influenced by the membrane voltage state of the neuron. Identical frozen Gaussian noise current traces were injected into TCR neurons in rat brain slices as well as in a validated three-compartment TCR model cell. The resulting membrane voltage traces and spike trains were analyzed by calculating the coherence and impedance. Reverse correlation techniques gave the Event-Triggered Average (ETA) and the Event-Triggered Covariance (ETC). This demonstrated that the feature selectivity started relatively long before the events (up to 300 ms) and showed a clear distinction between spikes (selective for fluctuations) and bursts (selective for integration). The model cell was fine-tuned to mimic the frozen noise initiated spike and burst responses to within experimental accuracy, especially for the mixed mode regimes. The information content carried by the various types of events in the signal as well as by the whole signal was calculated. Bursts phase-lock to and transfer information at lower frequencies than single spikes. On depolarization the neuron transits smoothly from the predominantly bursting regime to a spiking regime, in which it is more sensitive to high-frequency fluctuations. The model was then used to elucidate properties that could not be assessed experimentally, in particular the role of two important subthreshold voltage-dependent currents: the low threshold activated calcium current ( I T ) and the cyclic nucleotide modulated h current ( I h ). The ETAs of those currents and their underlying activation/inactivation states not only explained the state dependence of the firing regime but also the long-lasting concerted dynamic action of the two currents. Finally, the model was used to investigate the more realistic “high-conductance state”, where fluctuations are caused by (synaptic) conductance changes instead of current injection. Under “standard” conditions bursts are difficult to initiate, given the high degree of inactivation of the T-type calcium current. Strong and/or precisely timed inhibitory currents were able to remove this inactivation.

          Author summary

          Neurons in the brain respond to (sensory) stimuli by generating electrical pulses called ‘spikes’ or ‘action potentials’. Spikes are organized in different temporal patterns, such as ‘bursts’ in which they occur at a high frequency followed by a period of silence. Bursts are ubiquitous in the nervous system: they occur in different parts of the brain and in different species. Different mechanisms that generate them have been pointed out. Why the nervous system uses bursts in its communication, or what type of information is represented by bursts, remains largely unknown. Here, we looked at bursting in thalamocortical relay (TCR) cells, neurons that form a bridge between early sensory processing and higher-order structures (cortex). These cells fire bursts as a result of the activation of two distinct subthreshold ionic currents: the T-type calcium current and the h-type current. We investigated experimentally and computationally what features in the input makes TCR cells respond with bursts, and what features with single spikes. Bursts are a response to low-frequency slowly increasing input; single spikes are a response to faster fluctuations. Moreover, bursts are rare and highly informative, in line with an earlier hypothesis that bursts could play a ‘wake-up call’ role in the nervous system.

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          Most cited references61

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          Bursts as a unit of neural information: making unreliable synapses reliable.

          J E Lisman (1997)
          Several lines of evidence indicate that brief (< 25 ms) bursts of high-frequency firing have special importance in brain function. Recent work shows that many central synapses are surprisingly unreliable at signaling the arrival of single presynaptic action potentials to the postsynaptic neuron. However, bursts are reliably signaled because transmitter release is facilitated. Thus, these synapses can be viewed as filters that transmit bursts, but filter out single spikes. Bursts appear to have a special role in synaptic plasticity and information processing. In the hippocampus, a single burst can produce long-term synaptic modifications. In brain structures whose computational role is known, action potentials that arrive in bursts provide more-precise information than action potentials that arrive singly. These results, and the requirement for multiple inputs to fire a cell suggest that the best stimulus for exciting a cell (that is, a neural code) is coincident bursts.
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            Simulation of networks of spiking neurons: a review of tools and strategies.

            We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.
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              Function of the thalamic reticular complex: the searchlight hypothesis.

              F Crick (1984)
              It is suggested that in the brain the internal attentional searchlight, proposed by Treisman and others, is controlled by the reticular complex of the thalamus (including the closely related perigeniculate nucleus) and that the expression of the searchlight is the production of rapid bursts of firing in a subset of thalamic neurons. It is also suggested that the conjunctions produced by the attentional searchlight are mediated by rapidly modifiable synapses--here called Malsburg synapses--and especially by rapid bursts acting on them. The activation of Malsburg synapses is envisaged as producing transient cell assemblies, including "vertical" ones that temporarily unite neurons at different levels in the neural hierarchy.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: Data curation
                Role: ConceptualizationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                February 2018
                12 February 2018
                : 14
                : 2
                : e1005960
                Affiliations
                [1 ] Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
                [2 ] Cellular and Systems Neurobiology, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands
                University College London, UNITED KINGDOM
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-9084-9520
                Article
                PCOMPBIOL-D-17-00568
                10.1371/journal.pcbi.1005960
                5834212
                29432418
                6ac15dbe-df1a-48c2-8262-8b792b511a9c
                © 2018 Zeldenrust 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 credited.

                History
                : 6 April 2017
                : 8 January 2018
                Page count
                Figures: 13, Tables: 0, Pages: 36
                Funding
                Funded by: Radboud Christine Mohrmann Fellowship
                Award Recipient :
                This work was partically funded by a Radboud Christine Mohrmann Fellowship received by FZ. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
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                Membrane Potential
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                Custom metadata
                vor-update-to-uncorrected-proof
                2018-03-02
                The model is published in the ModelDB (accession number 232876). The data is in the Repository of the Donders Institute ( https://data.donders.ru.nl/collections/shared/di.dcn.DSC_626840_0002_144/versions/1?2)

                Quantitative & Systems biology
                Quantitative & Systems biology

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