Biology subjects

Shimazaki, H.

Publications and source records attributed to Shimazaki, H..

7 recordsLinked to original sources

bioRxiv2026

Inferring state-dependent functional circuit motifs using higher-order interactions analysis

Analysing higher-order interactions among simultaneously recorded neurons can provide crucial insights into neural network dynamics. Recent technological advances have enabled large-scale, long-term neuronal recordings, but analysis of such datasets often relies on simpler statistics due to computational and statistical challenges in assessing higher-order interactions. Here, we developed CHOIR, an efficient and reliable method for calculating higher-order interactions from large-scale neuronal recordings. We then used the inferred HOIs to uncover the underlying functional connectivity, differentiating between connectivity motifs in the space of pairwise and triplet-wise interactions. We found that this approach could successfully distinguish stationary and running states, sleep and awake states, and neuronal ensembles with distinct activity patterns in mice. Furthermore, we identified potential circuit architectures underlying different higher-order interactions, which we confirmed through simulations of large-scale spiking networks with specific subnetwork connectivity. Applying CHOIR to a causal manipulation dataset further confirmed the role of lateral inhibition, a key inhibitory motif, in generating specific HOI patterns. Our work provides a systematic analysis of higher-order interactions in diverse datasets and suggests that HOIs can reveal circuit motifs underlying neural dynamics across brain areas and brain states.

neuroscience↗

bioRxiv2025

Predicting individual learning trajectories in zebrafish via the free-energy principle

The free-energy principle has been proposed as a unified theory of brain function, and recent evidence from in vitro experiments supports its validity. However, its empirical application to in vivo neuronal dynamics during active decision-making remains limited. This work reverse-engineered generative models--cast as canonical neural networks--from large-scale calcium imaging data of zebrafish performing a visually guided go/no-go task in a virtual-reality environment. Leveraging the formal equivalence between neural network dynamics and variational Bayesian inference, we constructed biologically plausible synthetic agents capable of active inference. These agents recapitulated individual variability in zebrafish neuronal dynamics and behaviour by identifying subject-specific prior and posterior beliefs. Additionally, they enabled quantitative predictions of long-term changes in neural activity, effective synaptic connectivity, and behavioural performance, including task accuracy after training. Our results demonstrate a powerful framework of active inference for modelling in vivo neuronal self-organisation and highlight the predictive validity of the free-energy principle in behaving animals.

neuroscience↗

bioRxiv2024

Population coding under the scale-invariance of high-dimensional noise

High-dimensional scale-invariant neural activity is ubiquitous across brain regions and species, but its implications for information coding remain unclear. Here, we ask how stimulus information in the high-dimensional activity of mouse V1 scales with neuron number: does it saturate due to noise correlations, or increase without bound as subpopulations grow? Contrary to previous reports, we find that leading noise components that scale linearly with population size, and thus can limit information, are not sufficiently aligned with the signal to impose a bound. This conclusion follows from two scale-invariant power-law properties of neuronal responses in mouse V1: the noise eigenspectrum and alignment of noise components with the signal. We show that population subsampling links the observed power-law exponents to information boundedness, and that information scaling depends on the full eigenspectrum rather than its leading modes. Finally, we prove that, under subsampling, information-limiting correlations, if present, are differential correlations. Our findings clarify how information scales in high-dimensional neuronal activity under scale-invariant noise. TeaserScale-invariant noise in mouse V1 does not limit stimulus information as population size increases.

neuroscience↗

bioRxiv2020

Canonical neural networks perform active inference

This work considers a class of canonical neural networks comprising rate coding models, wherein neural activity and plasticity minimise a common cost function--and plasticity is modulated with a certain delay. We show that such neural networks implicitly perform active inference and learning to minimise the risk associated with future outcomes. Mathematical analyses demonstrate that this biological optimisation can be cast as maximisation of model evidence, or equivalently minimisation of variational free energy, under the well-known form of a partially observed Markov decision process model. This equivalence indicates that the delayed modulation of Hebbian plasticity--accompanied with adaptation of firing thresholds--is a sufficient neuronal substrate to attain Bayes optimal control. We corroborated this proposition using numerical analyses of maze tasks. This theory offers a universal characterisation of canonical neural networks in terms of Bayesian belief updating and provides insight into the neuronal mechanisms underlying planning and adaptive behavioural control.

neuroscience↗

bioRxiv2019

Future state prediction errors guide active avoidance behavior by adult zebrafish.

Human predicts future. To ask if fish also has this capacity, we established the virtual reality training system with live imaging of the telencephalic neurons of adult zebrafish in the active avoidance and found that, at the onset of the trial, learned fish conceives two future conditions as the favorable status on its way to the safe goal, i.e. one with the backwardly moving landscape and the other with the color of the safe goal. And the two different neural ensembles monitor the discrepancy between these predictions and the perceived real external status. Once fish reaches the goal, another ensemble is set to work to monitor whether fish keeps staying in the safe goal. The manipulation to artificially enhance these prediction errors elevated the activities of these ensembles and induced fish to behave to correct errors, revealing that fish sets behavioral strategy to actively realize these predictions.

neuroscience↗

bioRxiv2018

Uncovering Network Architecture Using an Exact Statistical Input-Output Relation of a Neuron Model

Using observed neuronal activity, we try to unveil hidden microcircuits. A key requirement is the knowledge of statistical input-output relation of single neurons in vivo. We use a recent exact solution of spike-timing for leaky integrate-and-fire neurons under noisy inputs balanced near threshold, and construct a framework that links synaptic type/strength, and spiking nonlinearity, with statistics of neuronal activity. The framework explains structured higher-order interactions of neurons receiving common inputs under different architectures. Comparing models prediction with an empirical dataset of monkey V1 neurons, we find that excitatory inputs to pairs explain the observed sparse activity characterized by negative triple-wise interactions, ruling out the intuitive shared inhibition. We show that the strong interactions are in general the signature of excitatory rather than inhibitory inputs whenever spontaneous activity is low. Finally, we present a guide map that can be used to reveal the hidden motifs underlying observed interactions found in empirical data.

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bioRxiv2017

Learning Complex Representations from Spatial Phase Statistics of Natural Scenes

Natural scenes contain higher-order statistical structures that can be encoded in their spatial phase information. Nevertheless, little progress has been made in modeling phase information of images, and understanding efficient representation of the image phases in the brain. In order to capture spatial phase structure under the efficient coding hypothesis, here we introduce a generative model of natural scenes by assuming independent source signals in a complex domain and non-uniform phase priors for the complex signals. Parameters of the proposed model are then estimated under the maximum-likelihood principle. This approach extends existing methods of independent component analysis for complex-valued signals to the one that utilizes phase information. Using simulated data, we demonstrate that the proposed model outperforms conventional models with a uniform phase prior in blind source separation of complex-valued signals. We then apply the proposed model to natural scenes in the Fourier domain. Real and imaginary parts of the learned complex features exhibit a pair of Gabor-like filters in quadratic phase structure with a similar shape. The proposed model significantly improved the goodness-of-the-fit from the model with a uniform phase prior, indicating that the structured spatial phases are important for removing redundancy in natural scenes. These results predict the presence of phase sensitive complex cells in the visual cortex.

neuroscience↗