Dynamic Communications Between GABAA Switch, Local Connectivity, and Synapses During Cortical Development: A Computational Study

Cerebral (“brain”) organoids are high-fidelity in vitro cellular models of the developing brain, which makes them one of the go-to methods to study isolated processes of tissue organization and its electrophysiological properties, allowing to collect invaluable data for in silico modeling neurodevelopmental processes. Complex computer models of biological systems supplement in vivo and in vitro experimentation and allow researchers to look at things that no laboratory study has access to, due to either technological or ethical limitations. In this paper, we present the Biological Cellular Neural Network Modeling (BCNNM) framework designed for building dynamic spatial models of neural tissue organization and basic stimulus dynamics. The BCNNM uses a convenient predicate description of sequences of biochemical reactions and can be used to run complex models of multi-layer neural network formation from a single initial stem cell. It involves processes such as proliferation of precursor cells and their differentiation into mature cell types, cell migration, axon and dendritic tree formation, axon pathfinding and synaptogenesis. The experiment described in this article demonstrates a creation of an in silico cerebral organoid-like structure, constituted of up to 1 million cells, which differentiate and self-organize into an interconnected system with four layers, where the spatial arrangement of layers and cells are consistent with the values of analogous parameters obtained from research on living tissues. Our in silico organoid contains axons and millions of synapses within and between the layers, and it comprises neurons with high density of connections (more than 10). In sum, the BCNNM is an easy-to-use and powerful framework for simulations of neural tissue development that provides a convenient way to design a variety of tractable in silico experiments.

Section: Discussionmentioning

confidence: 99%

BCNNM: A Framework for in silico Neural Tissue Development Modeling

Bozhko¹,

Galumov²,

Polovian³

et al. 2021

“…LIF neuron model and STDP learning rule . Compared to conventional artificial neural networks, spiking neural network (SNN) captures more essential characteristics of the biological brain (Ghosh-Dastidar and Adeli, 2009 ; Khalil et al, 2017 , 2018a ; Zhao et al, 2018 ). In recent years, SNN has been successfully applied in many aspects of cognitive function modeling, such as decision making and creative processes (Héricé et al, 2016 ; Khalil and Moustafa, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Brain-Inspired Affective Empathy Computational Model and Its Application on Altruistic Rescue Task

Feng

Zeng

2022

Affective empathy is an indispensable ability for humans and other species' harmonious social lives, motivating altruistic behavior, such as consolation and aid-giving. How to build an affective empathy computational model has attracted extensive attention in recent years. Most affective empathy models focus on the recognition and simulation of facial expressions or emotional speech of humans, namely Affective Computing. However, these studies lack the guidance of neural mechanisms of affective empathy. From a neuroscience perspective, affective empathy is formed gradually during the individual development process: experiencing own emotion—forming the corresponding Mirror Neuron System (MNS)—understanding the emotions of others through the mirror mechanism. Inspired by this neural mechanism, we constructed a brain-inspired affective empathy computational model, this model contains two submodels: (1) We designed an Artificial Pain Model inspired by the Free Energy Principle (FEP) to the simulate pain generation process in living organisms. (2) We build an affective empathy spiking neural network (AE-SNN) that simulates the mirror mechanism of MNS and has self-other differentiation ability. We apply the brain-inspired affective empathy computational model to the pain empathy and altruistic rescue task to achieve the rescue of companions by intelligent agents. To the best of our knowledge, our study is the first one to reproduce the emergence process of mirror neurons and anti-mirror neurons in the SNN field. Compared with traditional affective empathy computational models, our model is more biologically plausible, and it provides a new perspective for achieving artificial affective empathy, which has special potential for the social robots field in the future.

“…This phenomenon has lead to the research of biologically plausible Spiking Neural Networks (SNNs). SNNs have received extensive research in recent years, and have a wide range of applications in various domains, such as brain function modeling (Durstewitz et al, 2000 ; Levina et al, 2007 ; Izhikevich and Edelman, 2008 ; Potjans and Diesmann, 2014 ; Zenke et al, 2015 ; Breakspear, 2017 ; Khalil et al, 2017a , b , 2018 ), image classification (Zhang et al, 2018a ; Gu et al, 2019 ), decision making (Héricé et al, 2016 ; Zhao et al, 2018 ), object detection (Kim et al, 2019 ), and visual tracking (Luo et al, 2020 ). The discrete spike activation and high dimension information representation in SNNs make it more biologically plausible and energy-efficient.…”

Section: Introductionmentioning

confidence: 99%

GLSNN: A Multi-Layer Spiking Neural Network Based on Global Feedback Alignment and Local STDP Plasticity

Zhao

Zeng

Zhang

et al. 2020

Spiking Neural Networks (SNNs) are considered as the third generation of artificial neural networks, which are more closely with information processing in biological brains. However, it is still a challenge for how to train the non-differential SNN efficiently and robustly with the form of spikes. Here we give an alternative method to train SNNs by biologically-plausible structural and functional inspirations from the brain. Firstly, inspired by the significant top-down structural connections, a global random feedback alignment is designed to help the SNN propagate the error target from the output layer directly to the previous few layers. Then inspired by the local plasticity of the biological system in which the synapses are more tuned by the neighborhood neurons, a differential STDP is used to optimize local plasticity. Extensive experimental results on the benchmark MNIST (98.62%) and Fashion MNIST (89.05%) have shown that the proposed algorithm performs favorably against several state-of-the-art SNNs trained with backpropagation.