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25Th Annual Computational Neuroscience Meeting: CNS‑2016 Seogwipo City, Jeju-Do, South Korea

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25Th Annual Computational Neuroscience Meeting: CNS‑2016 Seogwipo City, Jeju-Do, South Korea BMC Neurosci 2016, 17(Suppl 1):54 DOI 10.1186/s12868-016-0283-6 BMC Neuroscience MEETING ABSTRACTS Open Access 25th Annual Computational Neuroscience Meeting: CNS‑2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 2016 Published: 18 August 2016 A1 adaptive exponential (AdEx) model, up to Hodgkin–Huxley (HH) type Functional advantages of cell‑type heterogeneity in neural models, all with conductance-based inputs. circuits Using these transfer functions we have built “realistic” mean-field mod- Tatyana O. Sharpee1 els for networks with two populations of neurons, the regular-spiking 1Computational Neurobiology Laboratory, The Salk Institute for Biological (RS) excitatory neurons, showing spike frequency adaptation, and the Studies, San Diego, CA, USA fast-spiking (FS) inhibitory neurons. This mean-field model can repro- Correspondence: Tatyana O. Sharpee ‑ [email protected] duce the propagating waves in V1, due to horizontal interactions, as BMC Neuroscience 2016, 17(Suppl 1):A1 shown previously using IF networks. This mean-field model also repro- duced the suppressive interactions between propagating waves. The Neural circuits are notorious for the complexity of their organization. mechanism of suppression was based on the preferential recruitment Part of this complexity is related to the number of different cell types of inhibitory cells over excitatory cells by afferent activity, which acted that work together to encode stimuli. I will discuss theoretical results through the conductance-based shunting effect of the two waves that point to functional advantages of splitting neural populations onto one another. The suppression was negligible in networks with into subtypes, both in feedforward and recurrent networks. These identical models for excitatory and inhibitory cells (such as IF net- results outline a framework for categorizing neuronal types based on works). This suggests that the suppressive effect is a general phenom- their functional properties. Such classification scheme could augment enon due to the higher excitability of inhibitory neurons in cortex, in classification schemes based on molecular, anatomical, and electro- line with previous models (Ozeki et al., Neuron 2009). physiological properties. Work done in collaboration with Yann Zerlaut (UNIC) for modeling, Sandrine Chemla and Frederic Chavane (CNRS, Marseille) for in vivo A2 experiments. Supported by CNRS and the European Commission Mesoscopic modeling of propagating waves in visual cortex (Human Brain Project). Alain Destexhe1,2 1UNIC, CNRS, Gif sur Yvette, France; 2The European Institute for Theoretical Neuroscience (EITN), Paris, France A3 Correspondence: Alain Destexhe ‑ [email protected]‑gif.fr Dynamics and biomarkers of mental disorders BMC Neuroscience 2016, 17(Suppl 1):A2 Mitsuo Kawato1 1 Propagating waves are large-scale phenomena widely seen in the ATR Computational Neuroscience Laboratories, 2‑2 Hikaridai, Seika‑cho, nervous system, in both anesthetized and awake or sleeping states. Soraku‑gun, Kyoto 619‑0288, Japan Recently, the presence of propagating waves at the scale of microns– Correspondence: Mitsuo Kawato ‑ [email protected] millimeters was demonstrated in the primary visual cortex (V1) of BMC Neuroscience 2016, 17(Suppl 1):A3 macaque monkey. Using a combination of voltage-sensitive dye (VSD) Current diagnoses of mental disorders are made in a categorical way, imaging in awake monkey V1 and model-based analysis, we showed as exemplified by DSM-5, but many difficulties have been encountered that virtually every visual input is followed by a propagating wave in such categorical regimes: the high percentage of comorbidities, (Muller et al., Nat Comm 2014). The wave was confined within V1, and usage of the same drug for multiple disorders, the lack of any vali- was consistent and repeatable for a given input. Interestingly, two dated animal model, and the situation where no epoch-making drug propagating waves always interact in a suppressive fashion, and sum has been developed in the past 30 years. NIMH started RDoC (research sublinearly. This is in agreement with the general suppressive effect domain criterion) to overcome these problems [1], and some success- seen in other circumstances in V1 (Bair et al., J Neurosci 2003; Reynaud ful results have been obtained, including common genetic risk loci [2] et al., J Neurosci 2012). and common neuroanatomical changes for multiple disorders [3] as To investigate possible mechanisms for this suppression we have well as psychosis biotypes [4]. designed mean-field models to directly integrate the VSD experi- In contrast to the currently dominant molecular biology approach, ments. Because the VSD signal is primarily caused by the summed which basically assumes one-to-one mapping between genes and voltage of all membranes, it represents an ideal case for mean-field disorders, I postulate the following dynamics-based view of psychiatric models. However, usual mean-field models are based on neuronal disorders. Our brain is a nonlinear dynamical system that can generate transfer functions such as the well-known sigmoid function, or func- spontaneous spatiotemporal activities. The dynamical system is char- tions estimated from very simple models. Any error in the transfer acterized by multiple stable attractors, only one of which corresponds function may result in wrong predictions by the corresponding mean- to a healthy or typically developed state. The others are pathological field model. To palliate this caveat, we have obtained semi-analytic states. forms of the transfer function of more realistic neuron models. We The most promising research approach within the above dynamical found that the same mathematical template can capture the trans- view is to combine resting-state functional magnetic resonance imag- fer function for models such as the integrate-and-fire (IF) model, the ing, machine learning, big data, and sophisticated neurofeedback. © 2016 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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. BMC Neurosci 2016, 17(Suppl 1):54 Page 2 of 112 Yahata et al. developed an ASD biomarker using only 16/9730 func- inhibitory synaptic process at various frequencies (2–30 Hz). We found tional connections, and it did not generalize to MDD or ADHD but that models with dendritic inputs expressed enhanced resonant fir- moderately to schizophrenia [5]. Yamashita’s regression model of ing at theta frequencies compared to models with somatic inputs. We working memory ability from functional connections [6] generalized then performed detailed analyses on the outputs of the models with to schizophrenia and reproduced the severity of working-memory dendritic inputs to further elucidate these results with respect to Ih dis- deficits of four psychiatric disorders (in preparation). tributions. The ability of the models to be recruited at the modulated With the further development of machine learning algorithms and input frequencies was quantified using the rotation number, or aver- accumulation of reliable datasets, we hope to obtain a comprehensive age number of spikes across all input cycles. Models with somatoden- landscape of many psychiatric and neurodevelopmental disorders. dritic Ih were recruited at >50 % of the input cycles for a wider range of Guided by this full-spectrum structure, a tailor-made neurofeedback theta frequencies (3–9 Hz) compared to models with somatic Ih only therapy should be optimized for each patient [7]. (3–4 Hz). Models with somatodendritic Ih also exhibited a wider range of theta frequencies for which phase-locked output (vector strength References >0.75) was observed (4–12 Hz), compared to models with somatic Ih 1. Insel T, Cuthbert B, Garvey M., et al. Research domain criteria (RDoC): (3–5 Hz). Finally, the phase of firing of models with somatodendritic Ih toward a new classification framework for research on mental disorders. given 8–10 Hz modulated input was delayed 180–230° relative to the Am J Psychiatry. 2010;167:748–51. time of release from inhibitory synaptic input. 2. Cross-disorder group of the psychiatric genomics consortium: identifica- O-LM cells receive phasic inhibitory inputs at theta frequencies from tion of risk loci with shared effects on five major psychiatric disorders: a a subpopulation of parvalbumin-positive GABAergic interneurons in genome-wide analysis. Lancet. 2013;381:1371–9. the medial septum (MS) timed to the peak of hippocampal theta, as 3. Goodkind M, et al. Identification of a common neurobiological substrate measured in the stratum pyramidale layer [3]. Furthermore, O-LM cells for mental illness. JAMA Psychiatry. 2015;72:305–15. fire at the trough of hippocampal pyramidal layer theta in vivo [4], an 4. Clementz BA, et al. Identification of distinct psychosis biotypes using approximate 180˚ phase delay from the MS inputs, corresponding to brain-based biomarkers. Am J Psychiatry. 2016;173:373–84. the phase delay in our models with somatodendritic Ih. Our results 5. Yahata N, Morimoto J, Hashimoto R, Lisi G, Shibata
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