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Interplay of intrinsic and network heterogeneity in strongly recurrent spiking networks

Interplay of intrinsic and network heterogeneity in strongly recurrent spiking networks Ly BMC Neuroscience 2015, 16(Suppl 1):P150 http://www.biomedcentral.com/1471-2202/16/S1/P150 POSTER PRESENTATION Open Access Interplay of intrinsic and network heterogeneity in strongly recurrent spiking networks Cheng Ly From 24th Annual Computational Neuroscience Meeting: CNS*2015 Prague, Czech Republic. 18-23 July 2015 Heterogeneity has recently gained a lot of attention and augmented mean-field theory based partially on methods it is becoming more apparent that it is a crucial feature in [12-15], we provide analytic explanations to account in neural processing [1-5]. Despite its importance, this for the observed phenomena. Our work gives insight for realistic physiological feature has traditionally been how these two forms of heterogeneity interact in a gen- neglected in theoretical studies of cortical neural net- eric recurrent spiking network that may be applicable to works. A common reason is that mean-field descriptions many areas of the cortex. of noisy cortical networks are high dimensional and generally intractable. Although heterogeneous spiking Published: 18 December 2015 neural networks have recently been studied theoretically [5-8], there is still a lot unknown. In particular, combin- References ing network heterogeneity [9] and intrinsic heterogene- 1. Shamir M, Sompolinsky H: Implications of neuronal diversity on population coding. Neural Computation 2006, 18:1951-1986. ity [10] have yet to be considered simultaneously despite 2. Chelaru MI, Dragoi V: Efficient coding in heterogeneous neuronal the fact that both are known to exist and likely have sig- populations. Proceedings of the National Academy of Sciences 2008, nificant roles in neural network dynamics. 105:16344-16349. 3. Padmanabhan K, Urban NN: Intrinsic biophysical diversity decorrelates To this end, we study a recurrently coupled spiking neuronal firing while increasing information content. Nature neuroscience network of leaky integrate-and-fire (LIF) neurons consist- 2010, 13:1276-1282. ing of excitatory and inhibitory neurons. The intrinsic 4. Tripathy SJ, Padmanabhan K, Gerkin RC, Urban NN: Intermediate intrinsic diversity enhances neural population coding. Proceedings of the National heterogeneity is modeled by varying the voltage threshold Academy of Sciences 2013, 110:8248-8253. for spiking [5], and the network heterogeneity is modeled 5. Mejias JF, Longtin A: Optimal heterogeneity for coding in spiking neural by different conductance strengths (partially motivated networks. Physical Review Letters 2012, 108:228102. 6. Ly C: Dynamics of Coupled Noisy Neural Oscillators with Heterogeneous by recent results [11], both excitatory and inhibitory con- Phase Resetting Curves. SIAM Journal on Applied Dynamical Systems 2014, ductances are scaled so each neuron has a different level 14:1733-1755. of balanced input). Unsurprisingly, we find that when 7. Nicola W, Campbell SA: Mean-field models for heterogeneous networks of two-dimensional integrate and fire neurons. Frontiers in computational either intrinsic or network heterogeneity is increased, the neuroscience 2013, 7:184. response heterogeneity also increases (i.e., the range of 8. Zhou P, Burton SD, Urban NN, Ermentrout GB: Impact of neuronal the average firing rate of the excitatory neurons also heterogeneity on correlated colored noise-induced synchronization. Frontiers in computational neuroscience 2013, 7:113. increases). However, for a fixed level of both forms of 9. Perin R, Berger TK, Markram H: A synaptic organizing principle for cortical heterogeneity, the network robustly exhibits a wide range neuronal groups. Proceedings of the National Academy of Sciences 2011, of response heterogeneity that strongly depends on the 108:5419-5424. 10. Marder E: Variability, compensation, and modulation in neurons and relationship between intrinsic and network heterogeneity. circuits. Proceedings of the National Academy of Sciences 2011, This coupled network is difficult to analyze because it is 108:15542-15548. stochastic, heterogeneous, and high dimensional with 11. Xue M, Atallah BV, Scanziani M: Equalizing excitation-inhibition ratios across visual cortical neurons. Nature 2014, 511:596-600. alpha function synapses and colored external noisy input. 12. Moreno-Bote R, Parga N: Auto- and crosscorrelograms for the spike With combination of Monte Carlo simulations and response of leaky integrate-and-fire neurons with slow synapses. Physical Review Letters 2006, 96:028101. 13. Nesse WH, Borisyuk A, Bressloff PC: Fluctuation-driven rhythmogenesis in Correspondence: CLy@vcu.edu an excitatory neuronal network with slow adaptation. Journal of Department of Statistical Sciences and Operations Research, Virginia Computational Neuroscience 2008, 25:317-333. Commonwealth University, Richmond, VA 23284, USA © 2015 Ly 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. Ly BMC Neuroscience 2015, 16(Suppl 1):P150 Page 2 of 2 http://www.biomedcentral.com/1471-2202/16/S1/P150 14. Ly C: A Principled Dimension-Reduction Method for the Population Density Approach to Modeling Networks of Neurons with Synaptic Dynamics. Neural Computation 2013, 25:2682-2708. 15. Nicola W, Ly C, Campbell SA: One-dimensional Population Density Approaches to Recurrently Coupled Networks of Neurons with Noise [http:// arxiv.org/abs/1411.2273], Submitted. doi:10.1186/1471-2202-16-S1-P150 Cite this article as: Ly: Interplay of intrinsic and network heterogeneity in strongly recurrent spiking networks. BMC Neuroscience 2015 16(Suppl 1):P150. 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

Interplay of intrinsic and network heterogeneity in strongly recurrent spiking networks

BMC Neuroscience , Volume 16 (1) – Dec 18, 2015

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References (14)

Publisher
Springer Journals
Copyright
Copyright © 2015 by Ly et al.
Subject
Biomedicine; Neurosciences; Neurobiology; Animal Models
eISSN
1471-2202
DOI
10.1186/1471-2202-16-S1-P150
Publisher site
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Abstract

Ly BMC Neuroscience 2015, 16(Suppl 1):P150 http://www.biomedcentral.com/1471-2202/16/S1/P150 POSTER PRESENTATION Open Access Interplay of intrinsic and network heterogeneity in strongly recurrent spiking networks Cheng Ly From 24th Annual Computational Neuroscience Meeting: CNS*2015 Prague, Czech Republic. 18-23 July 2015 Heterogeneity has recently gained a lot of attention and augmented mean-field theory based partially on methods it is becoming more apparent that it is a crucial feature in [12-15], we provide analytic explanations to account in neural processing [1-5]. Despite its importance, this for the observed phenomena. Our work gives insight for realistic physiological feature has traditionally been how these two forms of heterogeneity interact in a gen- neglected in theoretical studies of cortical neural net- eric recurrent spiking network that may be applicable to works. A common reason is that mean-field descriptions many areas of the cortex. of noisy cortical networks are high dimensional and generally intractable. Although heterogeneous spiking Published: 18 December 2015 neural networks have recently been studied theoretically [5-8], there is still a lot unknown. In particular, combin- References ing network heterogeneity [9] and intrinsic heterogene- 1. Shamir M, Sompolinsky H: Implications of neuronal diversity on population coding. Neural Computation 2006, 18:1951-1986. ity [10] have yet to be considered simultaneously despite 2. Chelaru MI, Dragoi V: Efficient coding in heterogeneous neuronal the fact that both are known to exist and likely have sig- populations. Proceedings of the National Academy of Sciences 2008, nificant roles in neural network dynamics. 105:16344-16349. 3. Padmanabhan K, Urban NN: Intrinsic biophysical diversity decorrelates To this end, we study a recurrently coupled spiking neuronal firing while increasing information content. Nature neuroscience network of leaky integrate-and-fire (LIF) neurons consist- 2010, 13:1276-1282. ing of excitatory and inhibitory neurons. The intrinsic 4. Tripathy SJ, Padmanabhan K, Gerkin RC, Urban NN: Intermediate intrinsic diversity enhances neural population coding. Proceedings of the National heterogeneity is modeled by varying the voltage threshold Academy of Sciences 2013, 110:8248-8253. for spiking [5], and the network heterogeneity is modeled 5. Mejias JF, Longtin A: Optimal heterogeneity for coding in spiking neural by different conductance strengths (partially motivated networks. Physical Review Letters 2012, 108:228102. 6. Ly C: Dynamics of Coupled Noisy Neural Oscillators with Heterogeneous by recent results [11], both excitatory and inhibitory con- Phase Resetting Curves. SIAM Journal on Applied Dynamical Systems 2014, ductances are scaled so each neuron has a different level 14:1733-1755. of balanced input). Unsurprisingly, we find that when 7. Nicola W, Campbell SA: Mean-field models for heterogeneous networks of two-dimensional integrate and fire neurons. Frontiers in computational either intrinsic or network heterogeneity is increased, the neuroscience 2013, 7:184. response heterogeneity also increases (i.e., the range of 8. Zhou P, Burton SD, Urban NN, Ermentrout GB: Impact of neuronal the average firing rate of the excitatory neurons also heterogeneity on correlated colored noise-induced synchronization. Frontiers in computational neuroscience 2013, 7:113. increases). However, for a fixed level of both forms of 9. Perin R, Berger TK, Markram H: A synaptic organizing principle for cortical heterogeneity, the network robustly exhibits a wide range neuronal groups. Proceedings of the National Academy of Sciences 2011, of response heterogeneity that strongly depends on the 108:5419-5424. 10. Marder E: Variability, compensation, and modulation in neurons and relationship between intrinsic and network heterogeneity. circuits. Proceedings of the National Academy of Sciences 2011, This coupled network is difficult to analyze because it is 108:15542-15548. stochastic, heterogeneous, and high dimensional with 11. Xue M, Atallah BV, Scanziani M: Equalizing excitation-inhibition ratios across visual cortical neurons. Nature 2014, 511:596-600. alpha function synapses and colored external noisy input. 12. Moreno-Bote R, Parga N: Auto- and crosscorrelograms for the spike With combination of Monte Carlo simulations and response of leaky integrate-and-fire neurons with slow synapses. Physical Review Letters 2006, 96:028101. 13. Nesse WH, Borisyuk A, Bressloff PC: Fluctuation-driven rhythmogenesis in Correspondence: CLy@vcu.edu an excitatory neuronal network with slow adaptation. Journal of Department of Statistical Sciences and Operations Research, Virginia Computational Neuroscience 2008, 25:317-333. Commonwealth University, Richmond, VA 23284, USA © 2015 Ly 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. Ly BMC Neuroscience 2015, 16(Suppl 1):P150 Page 2 of 2 http://www.biomedcentral.com/1471-2202/16/S1/P150 14. Ly C: A Principled Dimension-Reduction Method for the Population Density Approach to Modeling Networks of Neurons with Synaptic Dynamics. Neural Computation 2013, 25:2682-2708. 15. Nicola W, Ly C, Campbell SA: One-dimensional Population Density Approaches to Recurrently Coupled Networks of Neurons with Noise [http:// arxiv.org/abs/1411.2273], Submitted. doi:10.1186/1471-2202-16-S1-P150 Cite this article as: Ly: Interplay of intrinsic and network heterogeneity in strongly recurrent spiking networks. BMC Neuroscience 2015 16(Suppl 1):P150. 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

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BMC NeuroscienceSpringer Journals

Published: Dec 18, 2015

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