Inferring Network Connectivity from Event Timing Patterns

Casadiego, Jose; Maoutsa, Dimitra; Timme, Marc

doi:10.1103/physrevlett.121.054101

Cited by 28 publications

(12 citation statements)

References 42 publications

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“…This is an intended focus of future work. Indeed, the inference of the connectivity of spiking neural networks from their activity is an active area of research [77,78] which includes recently proposed continuous-time approaches [79,80]. However, any conditional independence test will suffer from the curse of dimensionality.…”

Section: Discussionmentioning

confidence: 99%

Estimating Transfer Entropy in Continuous Time Between Neural Spike Trains or Other Event-Based Data

2021

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Transfer entropy (TE) is a widely used measure of directed information flows in a number of domains including neuroscience. Many real-world time series for which we are interested in information flows come in the form of (near) instantaneous events occurring over time. Examples include the spiking of biological neurons, trades on stock markets and posts to social media, amongst myriad other systems involving events in continuous time throughout the natural and social sciences. However, there exist severe limitations to the current approach to TE estimation on such event-based data via discretising the time series into time bins: it is not consistent, has high bias, converges slowly and cannot simultaneously capture relationships that occur with very fine time precision as well as those that occur over long time intervals. Building on recent work which derived a theoretical framework for TE in continuous time, we present an estimation framework for TE on event-based data and develop a k-nearest-neighbours estimator within this framework. This estimator is provably consistent, has favourable bias properties and converges orders of magnitude more quickly than the current state-of-the-art in discrete-time estimation on synthetic examples. We demonstrate failures of the traditionally-used source-time-shift method for null surrogate generation. In order to overcome these failures, we develop a local permutation scheme for generating surrogate time series conforming to the appropriate null hypothesis in order to test for the statistical significance of the TE and, as such, test for the conditional independence between the history of one point process and the updates of another. Our approach is shown to be capable of correctly rejecting or accepting the null hypothesis of conditional independence even in the presence of strong pairwise time-directed correlations. This capacity to accurately test for conditional independence is further demonstrated on models of a spiking neural circuit inspired by the pyloric circuit of the crustacean stomatogastric ganglion, succeeding where previous related estimators have failed.

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Section: Discussionmentioning

confidence: 99%

Estimating Transfer Entropy in Continuous Time Between Neural Spike Trains or Other Event-Based Data

2021

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“…Although the methods presented here are likely to be useful for large-scale detection of putative synaptic connections, modeling the cross-correlogram directly does not necessarily provide unambiguous evidence for or against the presence of a synapse. Other detection methods have used other spike statistics (Casadiego et al 2018;Chen et al 2011;Ito et al 2011;Kadirvelu et al 2017;Ladenbauer et al 2019;Monasson and Cocco 2011;Song et al 2013), and the shape of the cross-correlogram can be influenced by many other factors, such as the dynamics of the presynaptic neuron (Perkel et al 1967b) and common input from unobserved neurons (Gerstein et al 1989;Stevenson et al 2008). Here we exclude the neuron pairs when there is a peak or trough in the cross-correlogram at t=0 to remove the potentially problematic connections, and expect our slow basis functions can act to model the common inputs if they occur on slow timescales (~10 ms).…”

Section: Discussionmentioning

confidence: 99%

Model-based detection of putative synaptic connections from spike recordings with latency and type constraints

Ren

Ito

Hafizi

et al. 2020

Preprint

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8Detecting synaptic connections using large-scale extracellular spike recordings presents a statistical challenge. 9While previous methods often treat the detection of each putative connection as a separate hypothesis test, here 10 we develop a modeling approach that infers synaptic connections while incorporating circuit properties learned 11 from the whole network. We use an extension of the Generalized Linear Model framework to describe the cross-12 correlograms between pairs of neurons and separate correlograms into two parts: a slowly varying effect due to 13 background fluctuations and a fast, transient effect due to the synapse. We then use the observations from all 14 putative connections in the recording to estimate two network properties: the presynaptic neuron type (excitatory 15 or inhibitory) and the relationship between synaptic latency and distance between neurons. Constraining the 16 presynaptic neuron's type, synaptic latencies, and time constants improves synapse detection. In data from 17 simulated networks, this model outperforms two previously developed synapse detection methods, especially on 18 the weak connections. We also apply our model to in vitro multielectrode array recordings from mouse 19 somatosensory cortex. Here our model automatically recovers plausible connections from hundreds of neurons, 20 and the properties of the putative connections are largely consistent with previous research. 21 22 Using in vivo or in vitro multielectrode arrays, the extracellular spiking of hundreds of neurons can be recorded 24 simultaneously. These recordings are allowing new, large-scale studies of neuronal networks (Hahn et al. 2019; 25 Harris et al. 2003; Levenstein et al. 2019; Okun et al. 2015; Tingley and Buzsáki 2018), and the number of 26 neurons that can be simultaneously recorded is increasing approximately exponentially (Stevenson and Kording 27 2011). Depending on the species, brain area, and electrode configuration, these simultaneously recorded 28 neurons can have tens of thousands of potential synapses between them. Detecting and characterizing these 29 synapses represents a major challenge for neural data analysis. Here, we develop a model-based method 30 incorporating network-level constraints on 1) the presynaptic neuron type and 2) the synaptic latencies between 31 pre-and postsynaptic neurons. We examine whether these constraints can improve synapse detection using 32 simulated data and large-scale in vitro multielectrode array recordings. 33 34Detecting synaptic connections from extracellular spike observations is a difficult statistical problem. Since both 35 spiking and synapses themselves are sparse, it is often difficult to distinguish between changes in spike 36 probability that are due to a specific synaptic input, changes that are due other (typically unobserved) inputs, or 37 due to chance. Using extracellular spike data, researchers often identify putative monosynaptic connections by 38 examining cross-correlograms between the spiking of two neurons. If two neu...

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“…Alternative approaches to infer connectivity from spike trains, other than those addressed above, have employed models of sparsely and linearly interacting point processes 83 , or have been designed in a model-free manner 51,84,85 , for example, using CCGs 51,84 similarly to our comparisons. A general challenge in subsampled networks arises from pairwise spike train correlations at small time lags generated by shared connections from unobserved neurons, regardless of whether a direct connection is present.…”

Section: Discussionmentioning

confidence: 99%

Inferring and validating mechanistic models of neural microcircuits based on spike-train data

et al. 2019

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The interpretation of neuronal spike train recordings often relies on abstract statistical models that allow for principled parameter estimation and model selection but provide only limited insights into underlying microcircuits. In contrast, mechanistic models are useful to interpret microcircuit dynamics, but are rarely quantitatively matched to experimental data due to methodological challenges. Here we present analytical methods to efficiently fit spiking circuit models to single-trial spike trains. Using derived likelihood functions, we statistically infer the mean and variance of hidden inputs, neuronal adaptation properties and connectivity for coupled integrate-and-fire neurons. Comprehensive evaluations on synthetic data, validations using ground truth in-vitro and in-vivo recordings, and comparisons with existing techniques demonstrate that parameter estimation is very accurate and efficient, even for highly subsampled networks. Our methods bridge statistical, data-driven and theoretical, model-based neurosciences at the level of spiking circuits, for the purpose of a quantitative, mechanistic interpretation of recorded neuronal population activity.

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Inferring Network Connectivity from Event Timing Patterns

Cited by 28 publications

References 42 publications

Estimating Transfer Entropy in Continuous Time Between Neural Spike Trains or Other Event-Based Data

Estimating Transfer Entropy in Continuous Time Between Neural Spike Trains or Other Event-Based Data

Model-based detection of putative synaptic connections from spike recordings with latency and type constraints

Inferring and validating mechanistic models of neural microcircuits based on spike-train data

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