On the Use of Dynamic Bayesian Networks in Reconstructing Functional Neuronal Networks from Spike Train Ensembles

Eldawlatly, Seif; Zhou, Yang; Jin, Rong; Oweiss, Karim G.

doi:10.1162/neco.2009.11-08-900

Cited by 61 publications

(59 citation statements)

References 74 publications

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“…In addition, recent theoretical studies show that an informationgeometric measure could be more directly related to synaptic interactions (Tatsuno and Okada, 2004;Nie and Tatsuno, 2012) and that it can be applied to nonstationary data (Shimazaki et al, 2012). Other promising methods would include a Bayesian approach (Brown et al, 2004;Eldawlatly et al, 2010) and a regularized logistic approach (Zhao et al, 2012). These measures may perform well in identifying causal, nonlinear relationships between neurons.…”

Section: Discussionmentioning

confidence: 99%

Long-Term Recordings Improve the Detection of Weak Excitatory–Excitatory Connections in Rat Prefrontal Cortex

et al. 2014

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Characterization of synaptic connectivity is essential to understanding neural circuit dynamics. For extracellularly recorded spike trains, indirect evidence for connectivity can be inferred from short-latency peaks in the correlogram between two neurons. Despite their predominance in cortex, however, significant interactions between excitatory neurons (E) have been hard to detect because of their intrinsic weakness. By taking advantage of long duration recordings, up to 25 h, from rat prefrontal cortex, we found that 7.6% of the recorded pyramidal neurons are connected. This corresponds to ϳ70% of the local E-E connection probability that has been reported by paired intracellular recordings (11.6%). This value is significantly higher than previous reports from extracellular recordings, but still a substantial underestimate. Our analysis showed that long recording times and strict significance thresholds are necessary to detect weak connections while avoiding false-positive results, but will likely still leave many excitatory connections undetected. In addition, we found that hyper-reciprocity of connections in prefrontal cortex that was shown previously by paired intracellular recordings was only present in short-distance, but not in long distance (ϳ300 micrometers or more) interactions. As hyper-reciprocity is restricted to local clusters, it might be a minicolumnar effect. Given the current surge of interest in very high-density neural spike recording (e.g., NIH BRAIN Project) it is of paramount importance that we have statistically reliable methods for estimating connectivity from cross-correlation analysis available. We provide an important step in this direction.

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Section: Discussionmentioning

confidence: 99%

Long-Term Recordings Improve the Detection of Weak Excitatory–Excitatory Connections in Rat Prefrontal Cortex

et al. 2014

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“…There are techniques that can help identify the functional connectivity of the neural circuitry using available observations. One of the promising approaches is to use Dynamic Bayesian Networks (DBN) to infer the effective connectivity from observed spike trains collected using highdensity microelectrode arrays [30].…”

Section: Stimulation Site Selection and Nominal Stabilitymentioning

confidence: 99%

Feedback control of the spatiotemporal firing patterns of neural microcircuits

Oweiss

Khalil

2010

49th IEEE Conference on Decision and Control (CDC)

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One of the fundamental objectives in systems neuroscience is to precisely control the spatiotemporal firing of cortical neurons to elicit a desired pattern of activity. In this work, we study the effects of intracortical micro-stimulation on the dynamics of a basal ganglia microcircuit model, and explore the feasibility of controlling the spatiotemporal firing patterns of the population in the presence of unobserved inputs. Results from the simulation study suggest that properly designed Multiple-Input-Multiple-Output (MIMO) feedback controller can force a subpopulation of output neurons to follow a prescribed spatiotemporal firing pattern despite the presence of unobserved inputs. The accuracy of the spike timing of the controlled neural firing with respect to the reference spike trains is in the order of tens of milliseconds. Even a simplified circuit model of Hammerstein-Wiener type can help in prescreening potential stimulation sites and analyzing the nominal stability of the closed-loop system.

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“…The past decades have seen the arrival of many methods that can characterize spike timing networks (Abeles and Gerstein, 1988; Chapin and Nicolelis, 1999; Nádasdy et al, 1999; Tetko and Villa, 2001; Grün et al, 2002; Lee and Wilson, 2002; Schnitzer and Meister, 2003; Ikegaya et al, 2004; Okatan et al, 2005; Schneider et al, 2006; Nikolíc, 2007; Pipa et al, 2008; Schrader et al, 2008; Berger et al, 2010; Eldawlatly et al, 2010; Louis et al, 2010; Peyrache et al, 2010; Humphries, 2011; Lopes-dos-Santos et al, 2011; Gansel and Singer, 2012; Torre et al, 2016). Their application has led to important insights, yet they have several limitations, especially when it comes to their application on large scale neuronal recordings (Buzsáki, 2004).…”

Section: Introductionmentioning

confidence: 99%

“…Namely, either: (1) the complexity of the identified networks is limited due to combinatorial explosion with increasing network size (e.g., template searching); (2) the networks are described only by the association of their member neurons without describing spike sequences; (3) between-spike time delays >0 are either discarded or not recovered; (4) temporal binning of spike times leads to reduced temporal precision; (5) networks with overlapping member neurons are not separated; or (6) a combination of the above. Although not important for every investigation of interactions in spiking networks (e.g., for higher order interactions see, Nakahara and Amari, 2002; Yu et al, 2009; Eldawlatly et al, 2010; Staude et al, 2010; Balaguer-Ballester et al, 2011; Shimazaki et al, 2012), they are essential for the exact identification of neurons and their spike sequences, and investigating their occurrence as a function of experimental variables.…”

Section: Introductionmentioning

confidence: 99%

Uncovering Neuronal Networks Defined by Consistent Between-Neuron Spike Timing from Neuronal Spike Recordings

Meij

Voytek

2018

eNeuro

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It is widely assumed that distributed neuronal networks are fundamental to the functioning of the brain. Consistent spike timing between neurons is thought to be one of the key principles for the formation of these networks. This can involve synchronous spiking or spiking with time delays, forming spike sequences when the order of spiking is consistent. Finding networks defined by their sequence of time-shifted spikes, denoted here as spike timing networks, is a tremendous challenge. As neurons can participate in multiple spike sequences at multiple between-spike time delays, the possible complexity of networks is prohibitively large. We present a novel approach that is capable of (1) extracting spike timing networks regardless of their sequence complexity, and (2) that describes their spiking sequences with high temporal precision. We achieve this by decomposing frequency-transformed neuronal spiking into separate networks, characterizing each network’s spike sequence by a time delay per neuron, forming a spike sequence timeline. These networks provide a detailed template for an investigation of the experimental relevance of their spike sequences. Using simulated spike timing networks, we show network extraction is robust to spiking noise, spike timing jitter, and partial occurrences of the involved spike sequences. Using rat multineuron recordings, we demonstrate the approach is capable of revealing real spike timing networks with sub-millisecond temporal precision. By uncovering spike timing networks, the prevalence, structure, and function of complex spike sequences can be investigated in greater detail, allowing us to gain a better understanding of their role in neuronal functioning.

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On the Use of Dynamic Bayesian Networks in Reconstructing Functional Neuronal Networks from Spike Train Ensembles

Cited by 61 publications

References 74 publications

Long-Term Recordings Improve the Detection of Weak Excitatory–Excitatory Connections in Rat Prefrontal Cortex

Long-Term Recordings Improve the Detection of Weak Excitatory–Excitatory Connections in Rat Prefrontal Cortex

Feedback control of the spatiotemporal firing patterns of neural microcircuits

Uncovering Neuronal Networks Defined by Consistent Between-Neuron Spike Timing from Neuronal Spike Recordings

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