Biology subjects

Shinomoto, S.

Publications and source records attributed to Shinomoto, S..

5 recordsLinked to original sources

bioRxiv2025

Relating Monosynaptic and Functional Connectivity - Complementary Perspectives on Neural Computation -

Information processing in the brain is thought to result from the coordination of large-scale neu-ronal activity, but this occurs on a fine neural circuit. Here, we investigate their relationship. First, we estimate monosynaptic connectivity by applying an advanced analysis method to spike trains recorded with high-density microelectrodes and confirm that the estimated neuronal wiring is largely consistent with neuroanatomical and neurophysiological evidence. Second, we simulate calcium imaging signals from the same dataset and confirm that the estimated functional connectivity is influenced by shared inputs and population synchronization on slower timescales. Notably, even with unrealistically fast calcium dynamics, the functional connectivity is only partially consistent with the monosynaptic connectivity. These findings suggest the complementary roles for monosynaptic and functional connectivity: the former provides circuit-level specificity, while the latter reflects emergent system-wide patterns of activity. We propose that an integrative approach combining both perspectives is essential for understanding circuit-level computation in the brain.

neuroscience↗

bioRxiv2023

Non-differentiable activity in the brain

Spike rasters of multiple neurons show vertical stripes, indicating that neurons exhibit synchronous activity in the brain. We seek to determine whether these coherent dynamics are caused by smooth brainwave activity or by something else. By analyzing biological data, we find that their cross-correlograms exhibit not only slow undulation but also a cusp at the origin, in addition to possible signs of monosynaptic connectivity. Here we show that undulation emerges if neurons are subject to smooth brainwave oscillations while a cusp results from non-differentiable fluctuations. While modern analysis methods have achieved good connectivity estimation by adapting the models to slow undulation, they still make false inferences due to the cusp. We devise a new analysis method that may solve both problems. We also demonstrate that oscillations and non-differentiable fluctuations may emerge in simulations of large-scale neural networks.

neuroscience↗

bioRxiv2020

CoNNECT: Convolutional Neural Network for Estimating synaptic Connectivity from spike Trains

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 cross-correlograms. Although the algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another method using a Convolutional Neural Network for Estimating synaptic Connectivity from spike trains (CoNNECT). After adaptation to huge amounts of simulated data, this method robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new method, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys.

neuroscience↗

bioRxiv2019

Precise hyperacuity estimation of spike timing from calcium imaging

Two-photon imaging is a major recording technique in neuroscience, but it suffers from several limitations, including a low sampling rate, the nonlinearity of calcium responses, the slow dynamics of calcium dyes and a low signal-to-noise ratio, all of which impose a severe limitation on the application of two-photon imaging in elucidating neuronal dynamics with high temporal resolution. Here, we developed a hyperacuity algorithm (HA_time) based on an approach combining a generative model and machine learning to improve spike detection and the precision of spike time inference. First, Bayesian inference estimates the calcium spike model by assuming the constancy of the spike shape and size. A support vector machine employs this information and detects spikes with higher temporal precision than the sampling rate. Compared with conventional thresholding, HA_time improved the precision of spike time estimation up to 20-fold for simulated calcium data. Furthermore, the benchmark analysis of experimental data from different brain regions and simulation of a broader range of experimental conditions showed that our algorithm was among the best in a class of hyperacuity algorithms. We encourage experimenters to use the proposed algorithm to precisely estimate hyperacuity spike times from two-photon imaging.

neuroscience↗

bioRxiv2018

Reconstructing Neuronal Circuitry from Parallel Spike Trains

State-of-the-art techniques allow researchers to record large numbers of spike trains parallel for many hours. With enough such data, we should be able to infer the connectivity among neurons. Here we develop a computationally realizable method for reconstructing neuronal circuitry by applying a generalized linear model (GLM) to spike crosscorrelations. Our method estimates interneuronal connections in units of postsynaptic potentials and the amount of spike recording needed for verifying connections. The performance of inference is optimized by counting the estimation errors using synthetic data from a network of Hodgkin-Huxley type neurons. By applying our method to rat hippocampal data, we show that the numbers and types of connections estimated from our calculations match the results inferred from other physiological cues. Our method provides the means to build a circuit diagram from recorded spike trains, thereby providing a basis for elucidating the differences in information processing in different brain regions.

neuroscience↗