Short-term postsynaptic plasticity facilitates predictive tracking in continuous attractors

Huilin Zhao; Sungchil Yang; Chi Chung Alan Fung

doi:10.3389/fncom.2023.1231924

Short-term postsynaptic plasticity facilitates predictive tracking in continuous attractors

Front Comput Neurosci. 2023 Nov 2:17:1231924. doi: 10.3389/fncom.2023.1231924. eCollection 2023.

Authors

Huilin Zhao¹, Sungchil Yang¹, Chi Chung Alan Fung¹

Affiliation

¹ Department of Neuroscience, City University of Hong Kong, Kowloon, Hong Kong SAR, China.

Abstract

Introduction: The N-methyl-D-aspartate receptor (NMDAR) plays a critical role in synaptic transmission and is associated with various neurological and psychiatric disorders. Recently, a novel form of postsynaptic plasticity known as NMDAR-based short-term postsynaptic plasticity (STPP) has been identified. It has been suggested that long-lasting glutamate binding to NMDAR allows for the retention of input information in brain slices up to 500 ms, leading to response facilitation. However, the impact of STPP on the dynamics of neuronal populations remains unexplored.

Methods: In this study, we incorporated STPP into a continuous attractor neural network (CANN) model to investigate its effects on neural information encoding in populations of neurons. Unlike short-term facilitation, a form of presynaptic plasticity, the temporally enhanced synaptic efficacy resulting from STPP destabilizes the network state of the CANN by increasing its mobility.

Results: Our findings demonstrate that the inclusion of STPP in the CANN model enables the network state to predictively respond to a moving stimulus. This nontrivial dynamical effect facilitates the tracking of the anticipated stimulus, as the enhanced synaptic efficacy induced by STPP enhances the system's mobility.

Discussion: The discovered STPP-based mechanism for sensory prediction provides valuable insights into the potential development of brain-inspired computational algorithms for prediction. By elucidating the role of STPP in neural population dynamics, this study expands our understanding of the functional implications of NMDAR-related plasticity in information processing within the brain.

Conclusion: The incorporation of STPP into a CANN model highlights its influence on the mobility and predictive capabilities of neural networks. These findings contribute to our knowledge of STPP-based mechanisms and their potential applications in developing computational algorithms for sensory prediction.

Keywords: NMDA receptors; attractor models; computational models; predictive coding; synaptic plasticity.

Grants and funding

This research was supported by a grants from the Research Grants Council of Hong Kong (11102120 and 11101922) for SY and startup grant (9610591) from City University of Hong Kong to CF.