• Corpus ID: 253734605

Learning biological neuronal networks with artificial neural networks: neural oscillations

@inproceedings{Zhang2022LearningBN,
  title={Learning biological neuronal networks with artificial neural networks: neural oscillations},
  author={Ruilin Zhang and Zhongyi Wang and Tianyi Wu and Yuhang Cai and Louis Tao and Zhuocheng Xiao and Yao Li},
  year={2022}
}
First-principles-based modelings have been extremely successful in providing crucial insights and predictions for complex biological functions and phenomena. However, they can be hard to build and expensive to simulate for complex living systems. On the other hand, modern data-driven methods thrive at modeling many types of high-dimensional and noisy data. Still, the training and interpretation of these data-driven models remain challenging. Here, we combine the two types of methods to model… 

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References

SHOWING 1-10 OF 66 REFERENCES

Spiking Neural Networks

A state-of-the-art review of the development of spiking neurons and SNNs is presented, and insight into their evolution as the third generation neural networks is provided.

Introduction to spiking neural networks: Information processing, learning and applications.

This paper summarizes basic properties of spiking neurons and spiking networks, and focuses, specifically, on models of spike-based information coding, synaptic plasticity and learning.

Model Reduction Captures Stochastic Gamma Oscillations on Low-Dimensional Manifolds

A suite of Markovian model reduction methods with varying levels of complexity are proposed and applied to spiking network models exhibiting heterogeneous dynamical regimes, ranging from homogeneous firing to strong synchrony in the gamma band, suggesting that the statistical features of gamma oscillations strongly depend on the subthreshold neuronal distributions.

Field-theoretic approach to fluctuation effects in neural networks.

  • M. BuiceJ. Cowan
  • Physics
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2007
The effective spike model is constructed, which describes both neural fluctuations and response and is argued that neural activity governed by this model exhibits a dynamical phase transition which is in the universality class of directed percolation.

Multi-band oscillations emerge from a simple spiking network

This work demonstrates the emergence of multi-band oscillations in a simple network consisting of a single excitatory and a single inhibitory neuronal population driven by constant input and points to unexplored regimes of stochastic competition between excitation and inhibition behind the generation of dynamic, patterned neuronal activities.

DNN-assisted statistical analysis of a model of local cortical circuits

A data-driven approach assisted by deep neural networks (DNN) to first discover certain input-output relations, and then to leverage this information and the superior computation speeds of the well-trained DNN to guide parameter searches and to deduce theoretical understanding.

Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms

This Perspective describes an emerging ‘differentiable biology’ in which phenomena ranging from the small and specific to the broad and complex can be modeled effectively and efficiently, often by exploiting knowledge about basic natural phenomena to overcome the limitations of sparse, incomplete and noisy data.

Fast Global Oscillations in Networks of Integrate-and-Fire Neurons with Low Firing Rates

We study analytically the dynamics of a network of sparsely connected inhibitory integrate-and-fire neurons in a regime where individual neurons emit spikes irregularly and at a low rate. In the

Neuronal Dynamics: From Single Neurons To Networks And Models Of Cognition

This textbook for advanced undergraduate and beginning graduate students provides a thorough and up-to-date introduction to the fields of computational and theoretical neuroscience.
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