发表机构
School of Computer Science and Electronic Engineering, University of Essex（埃塞克斯大学计算机科学与电子工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究通过in silico实验探究芯片上神经元网络的架构与计算关系，提出IC$^3$表征方法，发现低IC$^3$的特定架构在分类任务上表现最佳，并引入神经拓扑形态计算新方向。
AI 中文摘要
活体神经元网络通过循环的细胞和群体动力学转换输入，然而目前尚不清楚哪种网络架构支持哪种计算。芯片上的神经元将这一问题转化为设计问题，因为微通道引导轴突生长并设定网络架构。我们引入了IC$^3$，一种通信驱动计算的集成表征方法，它通过神经元动力学、功能通信和结构支持来表征网络状态。我们在in silico中实现了九种架构，作为基于电导的脉冲网络，并测试了它们在频率解码、时间顺序判别和衰减记忆上的表现。主要的前馈电路解码效果最佳。顺序链和微通道二极管具有最低的IC$^3$，并招募了可达神经元的三分之一，但在两个分类任务上取得了最高的两个分数。跨架构来看，较高的IC$^3$对应较低的分类分数。我们将这一新方向称为“神经拓扑形态计算”，其中神经元连接的物理组织被设计为计算基底的一部分。
英文摘要
Living neuronal networks transform inputs through recurrent cellular and population dynamics, yet it is unknown which network architecture supports which computation. Neurons-on-a-chip turn this question into a design problem because microchannels guide axonal growth and set the network architecture. We introduce IC$^3$, an Integrated Characterisation of Communication-Driven Computation, which characterizes network state through neuronal dynamics, functional communication, and structural support. We implemented nine architectures \textit{in silico} as conductance-based spiking networks and tested each on frequency decoding, temporal-order discrimination, and fading memory. Predominantly feedforward circuits decoded best. Sequential Chain and Microchannel Diode had the lowest IC$^3$ and recruited a third of reachable neurons, yet achieved the two highest scores on both classification tasks. Across architectures, higher IC$^3$ went with lower classification scores. We term this new direction \emph{neurotopomorphic computing}, in which the physical organisation of neuronal connectivity is engineered as part of the computing substrate.
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