Neuromorphic Dynamical Synapses With Reconfigurable Voltage-Gated Kinetics
Neuromorphic Dynamical Synapses With Reconfigurable Voltage-Gated Kinetics
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DOI：
10.1109/tbme.2019.2948809
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发表时间：
2020-07
影响因子：
4.6
通讯作者：
Jun Wang;G. Cauwenberghs;F. Broccard
中科院分区：
文献类型：
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作者：
Jun Wang;G. Cauwenberghs;F. Broccard
目标：虽然生物突触在神经元膜中表达多种受体，但目前神经形态突触的硬件实现通常依赖于简单的模型，忽略了突触传递的异质性。我们的目标是模拟不同类型的突触具有不同的属性。研究方法：基于电导的化学和电突触之间的硅神经元上实现了完全可编程和可重构的，生物物理现实的神经形态的VLSI芯片。不同的突触特性是通过配置芯片上的数字参数的电导，反转电位，和电压依赖性的通道动力学。将人工突触的I-V特性与生物学数据进行了比较。结果如下：我们复制了五种不同类型的化学突触的反应特性，包括兴奋性（$AMPA$，$NMDA$）和抑制性（$GABA_A$，$GABA_C$，$甘氨酸$）亲离子受体。此外，电突触在四个硅神经元的小网络中实现。结论：我们的工作扩展了硅神经元之间的突触类型，为在神经形态芯片上设计和实现生物学上真实的神经网络提供了更大的灵活性。重要性：神经形态芯片中较高的突触异质性与节能群体代码的硬件实现以及神经模型在神经形态VLSI硬件中实现的动态箝位应用相关。
Objective: Although biological synapses express a large variety of receptors in neuronal membranes, the current hardware implementation of neuromorphic synapses often rely on simple models ignoring the heterogeneity of synaptic transmission. Our objective is to emulate different types of synapses with distinct properties. Methods: Conductance-based chemical and electrical synapses were implemented between silicon neurons on a fully programmable and reconfigurable, biophysically realistic neuromorphic VLSI chip. Different synaptic properties were achieved by configuring on-chip digital parameters for the conductances, reversal potentials, and voltage dependence of the channel kinetics. The measured I-V characteristics of the artificial synapses were compared with biological data. Results: We reproduced the response properties of five different types of chemical synapses, including both excitatory ($AMPA$, $NMDA$) and inhibitory ($GABA_A$, $GABA_C$, $glycine$) ionotropic receptors. In addition, electrical synapses were implemented in a small network of four silicon neurons. Conclusion: Our work extends the repertoire of synapse types between silicon neurons, providing greater flexibility for the design and implementation of biologically realistic neural networks on neuromorphic chips. Significance: A higher synaptic heterogeneity in neuromorphic chips is relevant for the hardware implementation of energy-efficient population codes as well as for dynamic clamp applications where neural models are implemented in neuromorphic VLSI hardware.