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The role of mechanosensory T cells for stimulus encoding in the local bend network of the leech

The role of mechanosensory T cells for stimulus encoding in the local bend network of the leech Pirschel et al. BMC Neuroscience 2015, 16(Suppl 1):P132 http://www.biomedcentral.com/1471-2202/16/S1/P132 POSTER PRESENTATION Open Access The role of mechanosensory T cells for stimulus encoding in the local bend network of the leech Friederice Pirschel , Oliver Kuehn, Jutta Kretzberg From 24th Annual Computational Neuroscience Meeting: CNS*2015 Prague, Czech Republic. 18-23 July 2015 Accurate behavioral responses to sensory stimuli require most probable of all possible stimuli. It provides a mea- reliable encoding and processing of stimulus properties in sure, how well a certain response feature (e.g. spike the underlying neuronal network. The relatively simple count, latency, EPSP amplitude) encodes a specific sti- nervous system of the leech is able to react with surprising mulus property (location, intensity, duration). The pair- precision when the skin is touched: With their local bend wise discrimination approach compares responses to response [1], leeches discriminate behaviorally between two stimuli and quantifies minimal stimulus differences, which could be detected based on the neuronal response touch location differences of 9° [2], corresponding to a dis- tance of less than 1 mm. The underlying network consists features. These methods revealed that the relative of one layer of sensory neurons, approximately 20 inter- latency of two T cells leads to the best estimation of sti- neurons and a layer of motor neurons [1]. Studies investi- mulus locations (for tested intensities up to 50 mN). For gating the local bend mainly focused on one of the three interneurons, graded response features (e.g. EPSP inte- mechanosensory cell types, the pressure (“P”) cells [1,2]. gral and amplitude) allow good stimulus discrimination But decoding experiments revealed a discrepancy between performance, even for small distances of touch locations. P cell activity and the behavioral performance [2]. Additionally, we started building a computational Therefore, we investigated how the mechanosensory model of the local bend network. We tried to fit the touch (“T”) cells, respond to touch stimulus properties parameters of the Izhikevich model [4] to intracellularly and influence responses of local bend interneurons. recorded T cell responses. However, the depolarized Using a semi-intact preparation, we elicited the local resting potential (compared to cortical neurons) of this bend response with mechanical stimulation of the skin. invertebrate cell required modification of the model. Simultaneously, we performed intracellular double Moreover, we were not able to find a parameter range recordings from T cells and interneurons 157 or 159, reproducing the characteristic T-cell bursts in response which are involved in the local bend network [3]. We to stimulus onset. None of the tested parameter sets foundthatT cellsrespondwith characteristic bursts to yielded stimulus-dependent response latencies, which are the onset of touch stimulation. The first T cell spikes essential for stimulus encoding. Hence, the Izhikevich are generated with an extremely high temporal precision model is not well suited for modeling the response prop- and short response latency for a broad range of touch erties of an invertebrate sensor cell, which are relevant stimulus intensities. Local bend interneurons 157 and for the encoding of sensory stimuli. 159 get input from T cells and produce characteristic EPSPs with short response latencies in response to tac- Acknowledgements tile stimulation. In particular EPSPs of cell 159 follow Supported by the PhD program “Neurosenses” of the State of Lower Saxony. T cell bursts. Published: 18 December 2015 For quantitative data analysis, we used two maximum likelihood approaches of stimulus estimation: The classi- References fication approach assigned each response trial to the 1. Kristan WB, Calabrese RL, Friesen WO: Neuronal control of leech behavior. J Progress in Neurobiol 2005, 76:279-327. 2. Thomson EE, Kristan WB: Encoding and decoding touch location in the * Correspondence: friederice.pirschel@uni-oldenburg.de leech CNS. J Neurosci 2006, 26:8009-8016. Computational Neuroscience, University of Oldenburg, D-26111 Oldenburg, Germany © 2015 Pirschel et al. 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. Pirschel et al. BMC Neuroscience 2015, 16(Suppl 1):P132 Page 2 of 2 http://www.biomedcentral.com/1471-2202/16/S1/P132 3. Lockery SR, Kristan WB: Distributed processing of sensory information in the leech. II. Identification of interneurons contributing to the local bending reflex. J Neurosci 1990, 10:1816-1819. 4. Izhikevich EM: Simple model of spiking neurons. IEEE Trans Neural Networks 2003, 14:1569-1572. doi:10.1186/1471-2202-16-S1-P132 Cite this article as: Pirschel et al.: The role of mechanosensory T cells for stimulus encoding in the local bend network of the leech. BMC Neuroscience 2015 16(Suppl 1):P132. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png BMC Neuroscience Springer Journals

The role of mechanosensory T cells for stimulus encoding in the local bend network of the leech

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Springer Journals
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Copyright © 2015 by Pirschel et al.
Subject
Biomedicine; Neurosciences; Neurobiology; Animal Models
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1471-2202
DOI
10.1186/1471-2202-16-S1-P132
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Abstract

Pirschel et al. BMC Neuroscience 2015, 16(Suppl 1):P132 http://www.biomedcentral.com/1471-2202/16/S1/P132 POSTER PRESENTATION Open Access The role of mechanosensory T cells for stimulus encoding in the local bend network of the leech Friederice Pirschel , Oliver Kuehn, Jutta Kretzberg From 24th Annual Computational Neuroscience Meeting: CNS*2015 Prague, Czech Republic. 18-23 July 2015 Accurate behavioral responses to sensory stimuli require most probable of all possible stimuli. It provides a mea- reliable encoding and processing of stimulus properties in sure, how well a certain response feature (e.g. spike the underlying neuronal network. The relatively simple count, latency, EPSP amplitude) encodes a specific sti- nervous system of the leech is able to react with surprising mulus property (location, intensity, duration). The pair- precision when the skin is touched: With their local bend wise discrimination approach compares responses to response [1], leeches discriminate behaviorally between two stimuli and quantifies minimal stimulus differences, which could be detected based on the neuronal response touch location differences of 9° [2], corresponding to a dis- tance of less than 1 mm. The underlying network consists features. These methods revealed that the relative of one layer of sensory neurons, approximately 20 inter- latency of two T cells leads to the best estimation of sti- neurons and a layer of motor neurons [1]. Studies investi- mulus locations (for tested intensities up to 50 mN). For gating the local bend mainly focused on one of the three interneurons, graded response features (e.g. EPSP inte- mechanosensory cell types, the pressure (“P”) cells [1,2]. gral and amplitude) allow good stimulus discrimination But decoding experiments revealed a discrepancy between performance, even for small distances of touch locations. P cell activity and the behavioral performance [2]. Additionally, we started building a computational Therefore, we investigated how the mechanosensory model of the local bend network. We tried to fit the touch (“T”) cells, respond to touch stimulus properties parameters of the Izhikevich model [4] to intracellularly and influence responses of local bend interneurons. recorded T cell responses. However, the depolarized Using a semi-intact preparation, we elicited the local resting potential (compared to cortical neurons) of this bend response with mechanical stimulation of the skin. invertebrate cell required modification of the model. Simultaneously, we performed intracellular double Moreover, we were not able to find a parameter range recordings from T cells and interneurons 157 or 159, reproducing the characteristic T-cell bursts in response which are involved in the local bend network [3]. We to stimulus onset. None of the tested parameter sets foundthatT cellsrespondwith characteristic bursts to yielded stimulus-dependent response latencies, which are the onset of touch stimulation. The first T cell spikes essential for stimulus encoding. Hence, the Izhikevich are generated with an extremely high temporal precision model is not well suited for modeling the response prop- and short response latency for a broad range of touch erties of an invertebrate sensor cell, which are relevant stimulus intensities. Local bend interneurons 157 and for the encoding of sensory stimuli. 159 get input from T cells and produce characteristic EPSPs with short response latencies in response to tac- Acknowledgements tile stimulation. In particular EPSPs of cell 159 follow Supported by the PhD program “Neurosenses” of the State of Lower Saxony. T cell bursts. Published: 18 December 2015 For quantitative data analysis, we used two maximum likelihood approaches of stimulus estimation: The classi- References fication approach assigned each response trial to the 1. Kristan WB, Calabrese RL, Friesen WO: Neuronal control of leech behavior. J Progress in Neurobiol 2005, 76:279-327. 2. Thomson EE, Kristan WB: Encoding and decoding touch location in the * Correspondence: friederice.pirschel@uni-oldenburg.de leech CNS. J Neurosci 2006, 26:8009-8016. Computational Neuroscience, University of Oldenburg, D-26111 Oldenburg, Germany © 2015 Pirschel et al. 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. Pirschel et al. BMC Neuroscience 2015, 16(Suppl 1):P132 Page 2 of 2 http://www.biomedcentral.com/1471-2202/16/S1/P132 3. Lockery SR, Kristan WB: Distributed processing of sensory information in the leech. II. Identification of interneurons contributing to the local bending reflex. J Neurosci 1990, 10:1816-1819. 4. Izhikevich EM: Simple model of spiking neurons. IEEE Trans Neural Networks 2003, 14:1569-1572. doi:10.1186/1471-2202-16-S1-P132 Cite this article as: Pirschel et al.: The role of mechanosensory T cells for stimulus encoding in the local bend network of the leech. BMC Neuroscience 2015 16(Suppl 1):P132. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit

Journal

BMC Neuroscience – Springer Journals

Published: Dec 18, 2015

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