Monosynaptic inference via finely-timed spikes

Platkiewicz, Jonathan; Saccomano, Zachary; McKenzie, Sam; English, Daniel F.; Amarasingham, Asohan

doi:10.1007/s10827-020-00770-5

Cited by 9 publications

(12 citation statements)

References 75 publications

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“…Cell type classification Spike waveform (width and asymmetry), autocorrelation properties, and mean firing rate (mean inter-spike interval) were used to classify neurons into excitatory cells and interneurons. The autocorrelation was parameterized with a double exponential model (Platkiewicz et al, 2021):…”

Section: Declaration Of Interestsmentioning

confidence: 99%

Preexisting hippocampal network dynamics constrain optogenetically induced place fields

McKenzie

Huszár

English

et al. 2021

Neuron

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Section: Declaration Of Interestsmentioning

confidence: 99%

Preexisting hippocampal network dynamics constrain optogenetically induced place fields

McKenzie

Huszár

English

et al. 2021

Neuron

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“…It is instructional to assess whether the temporal cellular-level interactions uncovered by DyNetCP are bringing new latent dimensions to the analysis of neural code. Dimensionality reduction methods 18,34,35 are often used to estimate the number of independent dimensions of the code that can better explain the variability of spike trains. When principal component (PC) dimensionality reduction 30 is applied to the dynamic weights W dyn of all valid pairs (i.e., passed through all 3 thresholds, Fig.S8) in a single animal (65 units, 105 pairs, session 831882777), up to 33 components are required to account for 95% of the variance (Fig.5C, black).…”

Section: Discussionmentioning

confidence: 99%

Dynamic organization of visual cortical networks inferred from massive spiking datasets

Graber

Vlasov

Schwing

2023

Preprint

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We introduce a probabilistic model to discover the dynamic connectivity structure in a network of spiking cortical neurons. The model is evaluated on ground truth synthetic data and compared to alternative methods to ensure quality and quantification of model predictions. When applied to large-scale recordings of spiking activity in a mammalian visual cortex, the model reveals the latent dynamic functional interactions in cortical networks that is typically omitted from analysis.

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“…There were many possible sources for the lack of reliability and specificity, such as large fluctuations produced by external signals or higher-order interactions among neurons. Over the years, there have been many attempts to minimize the presence of such spurious connections, by shuffling spike trains [2], by jittering spike times [3][4][5][6], or by taking fluctuating inputs into account [7][8][9][10][11][12][13]. These, in general, helped eliminate the FPs, but they then tended to be conservative, giving rise to false negatives (FNs), i.e., missing existing connections.…”

Section: Introductionmentioning

confidence: 99%

A convolutional neural network for estimating synaptic connectivity from spike trains

Endo

Kobayashi

Bartolo

et al. 2020

Preprint

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The recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians, because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from neuronal spike trains by applying the generalized linear model to crosscorrelograms, GLMCC. Although the GLMCC algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another algorithm using a convolutional neural network for estimating synaptic connectivity from spike trains, CoNNECT. After adaptation to very large amounts of simulated data, this algorithm robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new algorithm, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys. * shinomoto.shigeru.6e@kyoto-u.ac.jpRecently, we developed an estimation method that works well in balancing the conflicting demands of reducing FPs and reducing FNs [11]. The estimation method we call GLMCC (GLM cross-correlation) nonetheless has a shortcoming: the estimation results are sensitive to the model parameters, and therefore the parameters need to be tuned for the spiking data. Here, we develop another algorithm: Convolutional Neural Network for Estimating synaptic Connectivity from spike Trains (CoNNECT). The two premises are that a convolutional neural network is good at capturing the features important for distinguishing among images (in this case, cross-correlograms) and that the cross-correlogram image will contain sufficient information from which to infer the presence of monosynaptic connectivity. This new algorithm is easy to use, and it works robustly with data arising from different cortical regions in non-human primates. The convolutional neural network algorithm has tens of thousands of internal parameters [12][13][14][15]. The parameters are adjusted using hundreds of thousands of pairs of spike trains generated with a large-scale simulation of the circuitry of realistic model neurons. To reproduce large fluctuations in real spike trains, we added external fluctuations to the model neurons in the simulation.CoNNECT provides reasonable inference. It does not, however, give a rationale for why the result was derived, whereas our previous algorithm GLMCC does because it fits an interaction kernel to the cross-correlogram. These methods, therefore, have different strengths and weaknesses

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Monosynaptic inference via finely-timed spikes

Cited by 9 publications

References 75 publications

Preexisting hippocampal network dynamics constrain optogenetically induced place fields

Preexisting hippocampal network dynamics constrain optogenetically induced place fields

Dynamic organization of visual cortical networks inferred from massive spiking datasets

A convolutional neural network for estimating synaptic connectivity from spike trains

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