Encoding scheme-dependent effects of input pattern pairs on pattern separation in the dentate gyrus neuronal network

Huimin Bai; Kai Yang; Huimin Bai; Kai Yang

doi:10.3934/era.2025285

Electronic Research Archive

2025, Volume 33, Issue 10: 6476-6492. doi: 10.3934/era.2025285

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Encoding scheme-dependent effects of input pattern pairs on pattern separation in the dentate gyrus neuronal network

Huimin Bai ¹,
Kai Yang ^{2
,
,}

1.
Department of Cybersecurity Defense, Shanxi Police College, Taiyuan 030401, China
2.
School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received: 09 August 2025 Revised: 07 October 2025 Accepted: 21 October 2025 Published: 29 October 2025

The ability to discriminate highly similar memories depends critically on pattern separation mediated by the dentate gyrus (DG). Although intrinsic neural mechanisms have been extensively studied, the influence of external input patterns on this process is often overlooked. Here, we established a biologically plausible DG model, subjected it to input pattern pairs with varying degrees of overlap, and examined the firing properties of granule cells (GCs) as output patterns. Based on the analysis of population coding, we observed that as the overlap between input pairs decreased, both the separation distance and orthogonalization level increased, indicating enhanced separation performance. Subsequently, from the perspective of rate coding, we computed the mean firing rate of individual GC to paired inputs and statistically analyzed the distributions of mean firing rates and their pairwise differences across GCs. Our results showed that these firing rate metrics unaffected by variations in input overlap. However, the Pearson correlation coefficient and mutual information between GC ensembles were significantly influenced, with both measures decreasing as input overlap is reduced. We also found that high-overlap inputs ($ > 70\% $) induced substantial firing rate variability in individual GC, which predominantly manifested as rate remapping. Furthermore, clustering analysis based on time-windowed firing rates demonstrated that pattern separation does not alter the structural characteristics of GCs, with similar cluster configurations emerging across different input overlap conditions. Together, these findings provided a detailed characterization of GC firing properties during pattern separation.
- pattern separation,
- input pattern pairs,
- population coding,
- rate coding,
- neuronal network
Citation: Huimin Bai, Kai Yang. Encoding scheme-dependent effects of input pattern pairs on pattern separation in the dentate gyrus neuronal network[J]. Electronic Research Archive, 2025, 33(10): 6476-6492. doi: 10.3934/era.2025285

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Abstract

The ability to discriminate highly similar memories depends critically on pattern separation mediated by the dentate gyrus (DG). Although intrinsic neural mechanisms have been extensively studied, the influence of external input patterns on this process is often overlooked. Here, we established a biologically plausible DG model, subjected it to input pattern pairs with varying degrees of overlap, and examined the firing properties of granule cells (GCs) as output patterns. Based on the analysis of population coding, we observed that as the overlap between input pairs decreased, both the separation distance and orthogonalization level increased, indicating enhanced separation performance. Subsequently, from the perspective of rate coding, we computed the mean firing rate of individual GC to paired inputs and statistically analyzed the distributions of mean firing rates and their pairwise differences across GCs. Our results showed that these firing rate metrics unaffected by variations in input overlap. However, the Pearson correlation coefficient and mutual information between GC ensembles were significantly influenced, with both measures decreasing as input overlap is reduced. We also found that high-overlap inputs ($ > 70\% $) induced substantial firing rate variability in individual GC, which predominantly manifested as rate remapping. Furthermore, clustering analysis based on time-windowed firing rates demonstrated that pattern separation does not alter the structural characteristics of GCs, with similar cluster configurations emerging across different input overlap conditions. Together, these findings provided a detailed characterization of GC firing properties during pattern separation.

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