Spike-Centered Jitter Can Mistake Temporal Structure

Platkiewicz, Jonathan; Stark, Eran; Amarasingham, Asohan

doi:10.1162/neco_a_00927

Cited by 15 publications

(14 citation statements)

References 21 publications

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“…Both methods revealed ~80% true positive rates and <5% false positive rates. Existing statistical methods for synapse detection provide a probability of observing excess synchrony beyond some null distribution (Platkiewicz et al, 2017). With our labeled data, we can convert these likelihoods into the statistic of interest, the probability of synaptic connectivity.…”

Section: Discussionmentioning

confidence: 99%

Pyramidal Cell-Interneuron Circuit Architecture and Dynamics in Hippocampal Networks

English

McKenzie

Evans

et al. 2017

Neuron

222

304

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Summary Excitatory control of inhibitory neurons is poorly understood due to the difficulty of studying synaptic connectivity in vivo. We inferred such connectivity through analysis of spike timing and validated this inference using juxtacellular and optogenetic control of presynaptic spikes in behaving mice. We observed that neighboring CA1 neurons had stronger connections, and that superficial pyramidal cells projected more to deep interneurons. Connection probability and strength were skewed, with a minority of highly connected hubs. Divergent presynaptic connections led to synchrony between interneurons. Synchrony of convergent presynaptic inputs boosted postsynaptic drive. Presynaptic firing frequency was read out by postsynaptic neurons through short-term depression and facilitation, with individual pyramidal cells and interneurons displaying a diversity of spike transmission filters. Additionally, spike transmission was strongly modulated by prior spike timing of the postsynaptic cell. These results bridge anatomical structure with physiological function.

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Section: Discussionmentioning

confidence: 99%

Pyramidal Cell-Interneuron Circuit Architecture and Dynamics in Hippocampal Networks

English

McKenzie

Evans

et al. 2017

Neuron

222

304

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“…To test whether the spike–triggered synaptic potentials where exceeding chance level, we compared the temporal structure in with a surrogate data set, where the spike times had been jittered. The location of the spike in time were locally jittered to abolish temporal structure using the interval jitter method 68 of size 100 ms. The same analysis was then performed to establish a distribution of when there is no causal structure.…”

Section: Methodsmentioning

confidence: 99%

Decoupling of timescales reveals sparse convergent CPG network in the adult spinal cord

et al. 2019

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During the generation of rhythmic movements, most spinal neurons receive an oscillatory synaptic drive. The neuronal architecture underlying this drive is unknown, and the corresponding network size and sparseness have not yet been addressed. If the input originates from a small central pattern generator (CPG) with dense divergent connectivity, it will induce correlated input to all receiving neurons, while sparse convergent wiring will induce a weak correlation, if any. Here, we use pairwise recordings of spinal neurons to measure synaptic correlations and thus infer the wiring architecture qualitatively. A strong correlation on a slow timescale implies functional relatedness and a common source, which will also cause correlation on fast timescale due to shared synaptic connections. However, we consistently find marginal coupling between slow and fast correlations regardless of neuronal identity. This suggests either sparse convergent connectivity or a CPG network with recurrent inhibition that actively decorrelates common input.

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“…A simple case, called dithering or jittering , modifies the precise time of each spike by some random amount within a small interval, thereby preserving all coarse temporal structure and removing all fine temporal structure. Many variations on this theme have been explored (Grün 2009; Harrison et al 2013; Platkiewicz et al 2017), and connections have been made with the well-established statistical notion of conditional inference (Harrison et al 2015).…”

Section: Networkmentioning

confidence: 99%

Computational Neuroscience: Mathematical and Statistical Perspectives

Kass

Amari

Arai

et al. 2018

Annu. Rev. Stat. Appl.

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Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.

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Spike-Centered Jitter Can Mistake Temporal Structure

Cited by 15 publications

References 21 publications

Pyramidal Cell-Interneuron Circuit Architecture and Dynamics in Hippocampal Networks

Pyramidal Cell-Interneuron Circuit Architecture and Dynamics in Hippocampal Networks

Decoupling of timescales reveals sparse convergent CPG network in the adult spinal cord

Computational Neuroscience: Mathematical and Statistical Perspectives

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