Reconstructing Neuronal Circuitry from Parallel Spike Trains

Kobayashi, Ryota; Kurita, Shuhei; Kitano, Katsunori; Mizuseki, Kenji; Richmond, Barry J.; Shinomoto, Shigeru

doi:10.1101/334078

Cited by 22 publications

(40 citation statements)

References 64 publications

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“…The proposed model considers three types of features: the userdependent propensity to continue any topic ("Prop"), the preference to topics ("Pref "), and the topic trend due to news and social events ("Trend"). The prediction performance was evaluated by three metrics: the accuracy ("ACC"), the F1 score ("F1"), and the Matthews correlation coefficient ("MCC") [36,51], defined as…”

Section: Appendix Appendix A: Up/down-votes and Likes As Feedbackmentioning

confidence: 99%

Estimating community feedback effect on topic choice in social media with predictive modeling

et al. 2020

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Social media users post content on various topics. A defining feature of social media is that other users can provide feedback-called community feedback-to their content in the form of comments, replies, and retweets. We hypothesize that the amount of received feedback influences the choice of topics on which a social media user posts. However, it is challenging to test this hypothesis as user heterogeneity and external confounders complicate measuring the feedback effect. Here, we investigate this hypothesis with a predictive approach based on an interpretable model of an author's decision to continue the topic of their previous post. We explore the confounding factors, including author's topic preferences and unobserved external factors such as news and social events, by optimizing the predictive accuracy. This approach enables us to identify which users are susceptible to community feedback. Overall, we find that 33% and 14% of active users in Reddit and Twitter, respectively, are influenced by community feedback. The model suggests that this feedback alters the probability of topic continuation up to 14%, depending on the user and the amount of feedback.

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Section: Appendix Appendix A: Up/down-votes and Likes As Feedbackmentioning

confidence: 99%

Estimating community feedback effect on topic choice in social media with predictive modeling

et al. 2020

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“…The accumulation of knowledge on brain electrophysiological properties at multiple scales hints towards the conjunct role of the dynamical properties of elementary elements, and the diversity of neuronal connections (Wang, 2010). The development of techniques giving access to the simultaneous recording of multiple single cells (Buzsáki, 2004;Le Van Quyen & Bragin, 2007;Kobayashi et al, 2019;Jun et al, 2017;Mitz et al, 2017) will probably lead to a breakthrough in our understanding of the emergence of network dynamics (LOIC is this change ok?) from the properties of individual neurons.…”

Section: Oscillatory Neuronal Activity and Network Architecture Allowmentioning

confidence: 99%

Why do we move to the beat? A multi-scale approach, from physical principles to brain dynamics

Damm

Varoqui

Cock

et al. 2020

Neuroscience & Biobehavioral Reviews

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Humans' ability to synchronize movement with auditory rhythms relies on motor networks, such as cortical areas, basal ganglia and the cerebellum, which also participate in rhythm perception and movement production. Current research has provided insights into the dependence of this action-perception coupling upon the entrainment of neuronal activity by external rhythms. At a physical level, advances on wearable robotics have enriched our understanding of the dynamical properties of the locomotor system showing evidences of mechanical entrainment. Here we defend the view that modelling brain and locomotor oscillatory activities as dynamical systems, at both neural and physical levels, provides a unified theoretical framework for the understanding of externally driven rhythmic entrainment of biological systems. To better understand the underlying mechanisms of this multi-level entrainment during locomotion, we review in a common framework the core questions related to the dynamic properties of biological oscillators and the neural bases of auditory-motor synchronization. Illustrations of our approach, using personalized auditory stimulation, to gait rehabilitation in Parkinson disease and to manipulation of runners' kinematics are presented.

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“…A well-known example in neuroscience is the generalized linear model (GLM), which statistically describes spike trains as an inhomogeneous Poisson point processes whose time-varying intensity (also known as Poisson rate) results from a non-linear function of filters, each processing a different variable influencing the neuron’s activity, such as the stimulus, the spike train own past activity and the spike trains from other neurons [21] . The use of GLMs has been widely applied to study neural interactions in a number of simultaneous studies [23] , [24] , [25] , [26] . For instance, it was showed how retinal cell interactions were more prominent between neighboring cells and how these interactions improved the decoding of visual stimuli [23] .…”

Section: From Cross-correlations To Model-based Approachesmentioning

confidence: 99%

Inferring neural information flow from spiking data

Campo

2020

Computational and Structural Biotechnology Journal

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The brain can be regarded as an information processing system in which neurons store and propagate information about external stimuli and internal processes. Therefore, estimating interactions between neural activity at the cellular scale has significant implications in understanding how neuronal circuits encode and communicate information across brain areas to generate behavior. While the number of simultaneously recorded neurons is growing exponentially, current methods relying only on pairwise statistical dependencies still suffer from a number of conceptual and technical challenges that preclude experimental breakthroughs describing neural information flows. In this review, we examine the evolution of the field over the years, starting from descriptive statistics to model-based and model-free approaches. Then, we discuss in detail the Granger Causality framework, which includes many popular state-of-the-art methods and we highlight some of its limitations from a conceptual and practical estimation perspective. Finally, we discuss directions for future research, including the development of theoretical information flow models and the use of dimensionality reduction techniques to extract relevant interactions from large-scale recording datasets.

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Reconstructing Neuronal Circuitry from Parallel Spike Trains

Cited by 22 publications

References 64 publications

Estimating community feedback effect on topic choice in social media with predictive modeling

Estimating community feedback effect on topic choice in social media with predictive modeling

Why do we move to the beat? A multi-scale approach, from physical principles to brain dynamics

Inferring neural information flow from spiking data

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