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      Ensemble stacking mitigates biases in inference of synaptic connectivity

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          Abstract

          A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches.

          Author Summary

          Mapping the routing of spikes through local circuitry is crucial for understanding neocortical computation. Under appropriate experimental conditions, these maps can be used to infer likely patterns of synaptic recruitment, linking activity to underlying anatomical connections. Such inferences help to reveal the synaptic implementation of population dynamics and computation. We compare a number of standard functional measures to infer underlying connectivity. We find that regularization impacts measures heterogeneously, and that individual algorithms have unique biases that impact their interpretation. These biases are nonoverlapping, and thus have the potential to mitigate one another. Combining individual algorithms into a single ensemble method results in a stronger inference algorithm than the best individual component measure. Ensemble-based inference can yield higher sensitivity to underlying connections and an improved estimate of the true statistics of synaptic recruitment.

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          Interneurons of the neocortical inhibitory system.

          Mammals adapt to a rapidly changing world because of the sophisticated cognitive functions that are supported by the neocortex. The neocortex, which forms almost 80% of the human brain, seems to have arisen from repeated duplication of a stereotypical microcircuit template with subtle specializations for different brain regions and species. The quest to unravel the blueprint of this template started more than a century ago and has revealed an immensely intricate design. The largest obstacle is the daunting variety of inhibitory interneurons that are found in the circuit. This review focuses on the organizing principles that govern the diversity of inhibitory interneurons and their circuits.
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            Ensemble Methods in Machine Learning

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              State-dependent computations: spatiotemporal processing in cortical networks.

              A conspicuous ability of the brain is to seamlessly assimilate and process spatial and temporal features of sensory stimuli. This ability is indispensable for the recognition of natural stimuli. Yet, a general computational framework for processing spatiotemporal stimuli remains elusive. Recent theoretical and experimental work suggests that spatiotemporal processing emerges from the interaction between incoming stimuli and the internal dynamic state of neural networks, including not only their ongoing spiking activity but also their 'hidden' neuronal states, such as short-term synaptic plasticity.
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                Author and article information

                Journal
                Netw Neurosci
                Netw Neurosci
                netn
                Network Neuroscience (Cambridge, Mass.)
                MIT Press (One Rogers Street, Cambridge, MA 02142-1209USAjournals-info@mit.edu )
                2472-1751
                01 March 2018
                Spring 2018
                : 2
                : 1
                : 60-85
                Affiliations
                [1 ]Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
                [2 ]Department of Neurobiology, University of Chicago, Chicago, IL, USA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                [* ]Corresponding author: jmaclean@ 123456uchicago.edu .

                Handling Editor: Olaf Sporns

                Author information
                http://orcid.org/0000-0001-9138-6452
                Article
                NETN_a_00032
                10.1162/NETN_a_00032
                5989998
                29911678
                0000bb21-1203-4449-9923-d4dcd2797f57
                © 2017 Massachusetts Institute of Technology

                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 work is properly cited.

                History
                : 23 May 2017
                : 11 October 2017
                Page count
                Figures: 9, Tables: 4, Equations: 22, References: 58, Pages: 26
                Funding
                Funded by: National Science Foundation (US);
                Award ID: CAREER Award No. 095286
                Funded by: Mary-Rita Angelo Fellowship;
                Categories
                Research
                Custom metadata
                Chambers, B., Levy, M., Dechery, J. B., & MacLean, J. N. (2018). Ensemble stacking mitigates biases in inference of synaptic connectivity. Network Neuroscience, 2(1), 60–85. https://doi.org/10.1162/netn_a_00032

                network analysis,network motifs,simulation and modeling,synaptic connectivity,information theory,ensemble learning

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