
정밀하게 균형잡힌 기억 네트워크는 이산적 끌개 상태 대신 연속적인 표현 다양체를 생성하여 학습된 입력을 분류할 수 있다.


coremsg

Biological memory networks are thought to store information 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 created 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 internal representations enable fast pattern classification, can support continual learning, and may provide a basis for higher-order learning and cognitive computations.

### Competing Interest Statement

The authors have declared no competing interest.

Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex

정밀하게-균형잡힌-기억-네트워크의-표현-기하학과-역학-후각-피질과-관련된-내용


정밀하게 균형잡힌 기억 네트워크의 표현 기하학과 역학: 후각 피질과 관련된 내용
