Garnier Artiñano Tomás, Andalibi Vafa, Atula Iiris, Maestri Matteo, Vanni Simo
Helsinki University Hospital (HUS) Neurocenter, Neurology, Helsinki University Hospital, Helsinki, Finland.
Department of Neurosciences, Clinicum, University of Helsinki, Helsinki, Finland.
Front Comput Neurosci. 2023 Jan 25;17:1011814. doi: 10.3389/fncom.2023.1011814. eCollection 2023.
Information transmission and representation in both natural and artificial networks is dependent on connectivity between units. Biological neurons, in addition, modulate synaptic dynamics and post-synaptic membrane properties, but how these relate to information transmission in a population of neurons is still poorly understood. A recent study investigated local learning rules and showed how a spiking neural network can learn to represent continuous signals. Our study builds on their model to explore how basic membrane properties and synaptic delays affect information transfer.
The system consisted of three input and output units and a hidden layer of 300 excitatory and 75 inhibitory leaky integrate-and-fire (LIF) or adaptive integrate-and-fire (AdEx) units. After optimizing the connectivity to accurately replicate the input patterns in the output units, we transformed the model to more biologically accurate units and included synaptic delay and concurrent action potential generation in distinct neurons. We examined three different parameter regimes which comprised either identical physiological values for both excitatory and inhibitory units (Comrade), more biologically accurate values (Bacon), or the Comrade regime whose output units were optimized for low reconstruction error (HiFi). We evaluated information transmission and classification accuracy of the network with four distinct metrics: coherence, Granger causality, transfer entropy, and reconstruction error.
Biophysical parameters showed a major impact on information transfer metrics. The classification was surprisingly robust, surviving very low firing and information rates, whereas information transmission overall and particularly low reconstruction error were more dependent on higher firing rates in LIF units. In AdEx units, the firing rates were lower and less information was transferred, but interestingly the highest information transmission rates were no longer overlapping with the highest firing rates.
Our findings can be reflected on the predictive coding theory of the cerebral cortex and may suggest information transfer qualities as a phenomenological quality of biological cells.
自然网络和人工网络中的信息传输与表示都依赖于单元之间的连接性。此外，生物神经元会调节突触动力学和突触后膜特性，但这些与神经元群体中的信息传输之间的关系仍知之甚少。最近的一项研究调查了局部学习规则，并展示了脉冲神经网络如何学习表示连续信号。我们的研究基于他们的模型，以探索基本膜特性和突触延迟如何影响信息传递。
该系统由三个输入和输出单元以及一个包含300个兴奋性和75个抑制性泄漏积分发放（LIF）或自适应积分发放（AdEx）单元的隐藏层组成。在优化连接性以准确复制输出单元中的输入模式后，我们将模型转换为更符合生物学实际的单元，并纳入了突触延迟以及不同神经元中同时发生的动作电位生成。我们研究了三种不同的参数模式，其中包括兴奋性和抑制性单元具有相同生理值的模式（同志模式）、更符合生物学实际值的模式（培根模式），或者其输出单元针对低重建误差进行了优化的同志模式（高保真模式）。我们使用四种不同的指标评估了网络的信息传输和分类准确性：相干性、格兰杰因果关系、转移熵和重建误差。
生物物理参数对信息传输指标有重大影响。分类结果出人意料地稳健，在非常低的发放率和信息率下仍能保持，而整体信息传输，尤其是低重建误差则更依赖于LIF单元中较高的发放率。在AdEx单元中，发放率较低，传递的信息较少，但有趣的是，最高信息传输率不再与最高发放率重叠。
我们的发现可以在大脑皮层的预测编码理论中得到体现，并且可能表明信息传递质量是生物细胞的一种现象学性质。
