Biologically Relevant Dynamical Behaviors Realized in an Ultra-Compact Neuron Model
Biologically Relevant Dynamical Behaviors Realized in an Ultra-Compact Neuron Model
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DOI：
10.3389/fnins.2020.00421
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发表时间：
2020-05-12
影响因子：
4.3
通讯作者：
Rozenberg, Marcelo J.
中科院分区：
文献类型：
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作者：
Stoliar, Pablo;Schneegans, Olivier;Rozenberg, Marcelo J.
我们展示了各种生物相关的动力学行为，建立在最近推出的超紧凑神经元（UCN）模型。我们提供了详细的电路，所有共享一个共同的基本块，实现泄漏积分和火灾（LIF）尖峰行为。所有电路都有少量的有源元件，基本块只有三个，两个晶体管和一个可控硅整流器（SCR）。我们还表明，数值模拟可以忠实地代表各种尖峰行为，并可用于进一步探索的动力学行为。以Izhikevich的一组生物相关行为为参考，我们的工作表明，LIF神经元模型的电路可以用作实现各种相关尖峰模式的基础。这些行为可能有助于构建可以捕获复杂大脑动力学的神经网络，或者也可能有助于人工智能应用。因此，我们的UCN模型可以被认为是Izhikevich（2003）数学神经元模型的电子电路对应物，具有两个看似矛盾的特征：极其简单和丰富的动力学行为。
We demonstrate a variety of biologically relevant dynamical behaviors building on a recently introduced ultra-compact neuron (UCN) model. We provide the detailed circuits which all share a common basic block that realizes the leaky-integrate-and-fire (LIF) spiking behavior. All circuits have a small number of active components and the basic block has only three, two transistors and a silicon controlled rectifier (SCR). We also demonstrate that numerical simulations can faithfully represent the variety of spiking behavior and can be used for further exploration of dynamical behaviors. Taking Izhikevich's set of biologically relevant behaviors as a reference, our work demonstrates that a circuit of a LIF neuron model can be used as a basis to implement a large variety of relevant spiking patterns. These behaviors may be useful to construct neural networks that can capture complex brain dynamics or may also be useful for artificial intelligence applications. Our UCN model can therefore be considered the electronic circuit counterpart of Izhikevich's (2003) mathematical neuron model, sharing its two seemingly contradicting features, extreme simplicity and rich dynamical behavior.