Literature DB >> 23274313

Quantitative examination of stimulus-response relations in cortical networks in vitro.

Oliver Weihberger1, Samora Okujeni, Jarno E Mikkonen, Ulrich Egert.   

Abstract

Variable responses of neuronal networks to repeated sensory or electrical stimuli reflect the interaction of the stimulus' response with ongoing activity in the brain and its modulation by adaptive mechanisms, such as cognitive context, network state, or cellular excitability and synaptic transmission capability. Here, we focus on reliability, length, delays, and variability of evoked responses with respect to their spatial distribution, interaction with spontaneous activity in the networks, and the contribution of GABAergic inhibition. We identified network-intrinsic principles that underlie the formation and modulation of spontaneous activity and stimulus-response relations with the use of state-dependent stimulation in generic neuronal networks in vitro. The duration of spontaneously recurring network-wide bursts of spikes was best predicted by the length of the preceding interval. Length, delay, and structure of responses to identical stimuli systematically depended on stimulus timing and distance to the stimulation site, which were described by a set of simple functions of spontaneous activity. Response length at proximal recording sites increased with the duration of prestimulus inactivity and was best described by a saturation function y(t) = A(1 - e(-αt)). Concomitantly, the delays of polysynaptic late responses at distant sites followed an exponential decay y(t) = Be(-βt) + C. In addition, the speed of propagation was determined by the overall state of the network at the moment of stimulation. Disinhibition increased the number of spikes/network burst and interburst interval length at unchanged gross firing rate, whereas the response modulation by the duration of prestimulus inactivity was preserved. Our data suggest a process of network depression during bursts and subsequent recovery that limit evoked responses following distinct rules. We discuss short-term synaptic depression due to depletion of neurotransmitter vesicles as an underlying mechanism. The seemingly unreliable patterns of spontaneous activity and stimulus-response relations thus follow a predictable structure determined by the interdependencies of network structures and activity states.

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Year:  2012        PMID: 23274313     DOI: 10.1152/jn.00481.2012

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  18 in total

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Authors:  Samora Okujeni; Steffen Kandler; Ulrich Egert
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2.  DOC2B and Munc13-1 differentially regulate neuronal network activity.

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Journal:  Cereb Cortex       Date:  2013-03-28       Impact factor: 5.357

3.  Comparative microelectrode array data of the functional development of hPSC-derived and rat neuronal networks.

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Journal:  Sci Data       Date:  2022-03-30       Impact factor: 6.444

4.  Parameters for burst detection.

Authors:  Douglas J Bakkum; Milos Radivojevic; Urs Frey; Felix Franke; Andreas Hierlemann; Hirokazu Takahashi
Journal:  Front Comput Neurosci       Date:  2014-01-13       Impact factor: 2.380

5.  Slow dynamics in features of synchronized neural network responses.

Authors:  Netta Haroush; Shimon Marom
Journal:  Front Comput Neurosci       Date:  2015-04-14       Impact factor: 2.380

6.  Shaping Neuronal Network Activity by Presynaptic Mechanisms.

Authors:  Ayal Lavi; Omri Perez; Uri Ashery
Journal:  PLoS Comput Biol       Date:  2015-09-15       Impact factor: 4.475

7.  Interaction of electrically evoked activity with intrinsic dynamics of cultured cortical networks with and without functional fast GABAergic synaptic transmission.

Authors:  Thomas Baltz; Thomas Voigt
Journal:  Front Cell Neurosci       Date:  2015-07-17       Impact factor: 5.505

8.  Synaptic dynamics contribute to long-term single neuron response fluctuations.

Authors:  Sebastian Reinartz; Istvan Biro; Asaf Gal; Michele Giugliano; Shimon Marom
Journal:  Front Neural Circuits       Date:  2014-07-01       Impact factor: 3.492

9.  Controlling neural network responsiveness: tradeoffs and constraints.

Authors:  Hanna Keren; Shimon Marom
Journal:  Front Neuroeng       Date:  2014-04-29

10.  A comparison of computational methods for detecting bursts in neuronal spike trains and their application to human stem cell-derived neuronal networks.

Authors:  Ellese Cotterill; Paul Charlesworth; Christopher W Thomas; Ole Paulsen; Stephen J Eglen
Journal:  J Neurophysiol       Date:  2016-04-20       Impact factor: 2.714

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