Simulating Small Neural Circuits with a Discrete Computational Model
Simulating Small Neural Circuits with a Discrete Computational Model
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DOI：
10.1007/s00422-020-00826-w
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发表时间：
2020-06-01
影响因子：
1.9
通讯作者：
Kuznetsov, Oleg P.
中科院分区：
文献类型：
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作者：
Bazenkov, Nikolay, I;Boldyshev, Boris A.;Kuznetsov, Oleg P.
神经活动的模拟通常基于微分方程式。我们解决了用简化的离散模型可以实现什么的问题。提出的模型类似于人工神经网络，丰富了额外的生物启发特征。神经元有几种状态，状态转换遵循内源性模式，大致对应于生物神经元中观察到的放电行为：振荡、紧张性、平台性等。神经相互作用由两部分组成：突触连接和神经递质的突触外发射。动力学是异步的和基于事件的；事件与神经元活动的变化相对应。该模型的创新之处在于引入了离散框架来模拟神经递质相互作用，神经递质相互作用在神经调节中起着重要作用。我们模拟像中枢模式产生器(CPG)这样的小型神经集成的节律活动。建模的例子包括：抑制后反弹的半中心机制产生的双相节律(如水蚤心跳CPG)、三相节律(如池塘蜗牛喂CPG)和多个神经元系统中的模式转换(如喂食CPG的海兔在摄食和排出之间的切换)。异步动力学允许获得多相节律，其相位持续时间接近其生物原型。结论部分对离散建模在生物学研究中的应用前景进行了展望。
Simulations of neural activity are commonly based on differential equations. We address the question what can be achieved with a simplified discrete model. The proposed model resembles artificial neural networks enriched with additional biologically inspired features. A neuron has several states, and the state transitions follow endogenous patterns which roughly correspond to firing behavior observed in biological neurons: oscillatory, tonic, plateauing, etc. Neural interactions consist of two components: synaptic connections and extrasynaptic emission of neurotransmitters. The dynamics is asynchronous and event-based; the events correspond to the changes in neurons activity. This model is innovative in introducing discrete framework for modeling neurotransmitter interactions which play the important role in neuromodulation. We simulate rhythmic activity of small neural ensembles like central pattern generators (CPG). The modeled examples include: the biphasic rhythm generated by the half-center mechanism with the post-inhibitory rebound (like the leech heartbeat CPG), the triphasic rhythm (like in pond snail feeding CPG) and the pattern switch in the system of several neurons (like the switch between ingestion and egestion in Aplysia feeding CPG). The asynchronous dynamics allows to obtain multi-phasic rhythms with phase durations close to their biological prototypes. The perspectives of discrete modeling in biological research are discussed in the conclusion.