Meissner-Bernard Claire, Zenke Friedemann, Friedrich Rainer W
Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
University of Basel, Basel, Switzerland.
Elife. 2025 Jan 13;13:RP96303. doi: 10.7554/eLife.96303.
Biological memory networks are thought to store information by experience-dependent changes in the synaptic connectivity between assemblies of neurons. Recent models suggest that these assemblies contain both excitatory and inhibitory neurons (E/I assemblies), resulting in co-tuning and precise balance of excitation and inhibition. To understand computational consequences of E/I assemblies under biologically realistic constraints we built a spiking network model based on experimental data from telencephalic area Dp of adult zebrafish, a precisely balanced recurrent network homologous to piriform cortex. We found that E/I assemblies stabilized firing rate distributions compared to networks with excitatory assemblies and global inhibition. Unlike classical memory models, networks with E/I assemblies did not show discrete attractor dynamics. Rather, responses to learned inputs were locally constrained onto manifolds that 'focused' activity into neuronal subspaces. The covariance structure of these manifolds supported pattern classification when information was retrieved from selected neuronal subsets. Networks with E/I assemblies therefore transformed the geometry of neuronal coding space, resulting in continuous representations that reflected both relatedness of inputs and an individual's experience. Such continuous representations enable fast pattern classification, can support continual learning, and may provide a basis for higher-order learning and cognitive computations.
生物记忆网络被认为是通过神经元集合之间突触连接的经验依赖性变化来存储信息的。最近的模型表明，这些集合包含兴奋性和抑制性神经元（E/I集合），从而导致兴奋和抑制的共同调节以及精确平衡。为了在生物学现实约束下理解E/I集合的计算结果，我们基于成年斑马鱼端脑Dp区域的实验数据构建了一个脉冲网络模型，该区域是一个与梨状皮质同源的精确平衡的递归网络。我们发现，与具有兴奋性集合和全局抑制的网络相比，E/I集合稳定了放电率分布。与经典记忆模型不同，具有E/I集合的网络没有显示出离散吸引子动力学。相反，对学习输入的反应在局部被限制在将活动“聚焦”到神经元子空间的流形上。当从选定的神经元子集中检索信息时，这些流形的协方差结构支持模式分类。因此，具有E/I集合的网络改变了神经元编码空间的几何形状，产生了反映输入相关性和个体经验的连续表示。这种连续表示能够实现快速模式分类，可以支持持续学习，并可能为高阶学习和认知计算提供基础。
