Koren Veronika, Malerba Simone Blanco, Schwalger Tilo, Panzeri Stefano
Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany.
Institute of Mathematics, Technische Universität Berlin, 10623 Berlin, Germany.
bioRxiv. 2025 Jan 17:2024.04.24.590955. doi: 10.1101/2024.04.24.590955.
The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we derive the structural, coding, and biophysical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. We assumed that the network encodes a number of independent stimulus features varying with a time scale equal to the membrane time constant of excitatory and inhibitory neurons. The optimal network has biologically-plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-specific excitatory external input. The excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implements feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal ratio of excitatory vs inhibitory neurons and the ratio of mean inhibitory-to-inhibitory vs excitatory-to-inhibitory connectivity are comparable to those of cortical sensory networks. The efficient network solution exhibits an instantaneous balance between excitation and inhibition. The network can perform efficient coding even when external stimuli vary over multiple time scales. Together, these results suggest that key properties of biological neural networks may be accounted for by efficient coding.
高效编码原理假定，感觉皮层网络旨在以最小的代谢成本编码最大的感觉信息。尽管高效编码在神经科学中具有重大影响，但神经网络活动的基本经验属性是否能仅基于这一规范性原理来解释，仍不明确。在此，我们推导了脉冲神经元兴奋性 - 抑制性递归网络的结构、编码和生物物理属性，这些属性直接源于施加网络使瞬时损失函数和体现高效编码的时间平均性能指标最小化。我们假设网络编码一些随时间尺度变化的独立刺激特征，该时间尺度等于兴奋性和抑制性神经元的膜时间常数。最优网络具有生物学上合理的生物物理特征，包括现实的积分发放脉冲动力学、脉冲触发适应性以及非特异性兴奋性外部输入。具有相似刺激调谐的神经元之间的兴奋性 - 抑制性递归连接实现了特征特异性竞争，类似于最近在视觉皮层中发现的情况。具有非结构化连接的网络无法达到可比的编码效率水平。兴奋性与抑制性神经元的最优比例以及平均抑制性与抑制性连接与兴奋性与抑制性连接的比例与皮层感觉网络的比例相当。高效网络解决方案在兴奋和抑制之间呈现瞬时平衡。即使外部刺激在多个时间尺度上变化，该网络也能执行高效编码。总之，这些结果表明生物神经网络的关键属性可能由高效编码来解释。
