
다양한 시간적 비대칭성을 가진 학습 규칙을 가진 뉴런들로 구성된 네트워크에서 외부 입력을 조절하여 순차적 활성화 속도를 유연하게 제어할 수 있다.


coremsg

Temporal rescaling of sequential neural activity has been observed in multiple brain areas during behaviors involving time estimation and motor execution at variable speeds. Temporally asymmetric Hebbian rules have been used in network models to learn and retrieve sequential activity, with characteristics that are qualitatively consistent with experimental observations. However, in these models sequential activity is retrieved at a fixed speed. Here, we investigate the effects of a heterogeneity of plasticity rules on network dynamics. In a model in which neurons differ by the degree of temporal symmetry of their plasticity rule, we find that retrieval speed can be controlled by varying external inputs to the network. Neurons with temporally symmetric plasticity rules act as brakes and tend to slow down the dynamics, while neurons with temporally asymmetric rules act as accelerators of the dynamics. We also find that such networks can naturally generate separate ‘preparatory’ and ‘execution’ activity patterns with appropriate external inputs.

### Competing Interest Statement

The authors have declared no competing interest.

Dynamic control of sequential retrieval speed in networks with heterogeneous learning rules

다양한-학습-규칙을-가진-네트워크에서-순차적-검색-속도의-동적-제어


다양한 학습 규칙을 가진 네트워크에서 순차적 검색 속도의 동적 제어
