AI Insight
This study develops a mathematical model of working memory that incorporates two distinct types of inhibitory neurons (parvalbumin-expressing and somatostatin-expressing) rather than treating inhibition as uniform. The researchers found that the spatial extent of somatostatin neuron connectivity is particularly important for stabilizing persistent activity patterns that encode remembered information, and that broader somatostatin connectivity reduces the rate at which memories degrade due to neural noise. The model provides specific predictions about how different inhibitory cell types contribute to both the stability and precision of continuous working memory representations.
Why it matters
Understanding how different inhibitory neuron subtypes contribute to working memory could help explain individual differences in memory capacity and guide potential therapeutic interventions for cognitive disorders involving working memory deficits. The findings suggest that somatostatin-expressing interneurons may be particularly important targets for understanding and potentially treating memory impairments.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Abstract: The maintenance of continuous variable information in working memory is thought to rely on persistent patterns of cortical activity. In delayed-estimation tasks, neural activity can form localized activity peaks, or “bumps,” whose positions track the remembered variable. Such activity is well described by continuous-attractor neural field models, but most existing models collapse cortical inhibition into a single homogeneous population. Here, we introduce a stochastic neural field model with distinct excitatory, parvalbumin-expressing (PV), and somatostatin-expressing (SST) populations to examine how inhibitory subtype structure shapes persistent activity. Using a Heaviside firing-rate approximation, we derive stationary bump solutions and reduce their linear stability to separate shifting and scaling modes. We show that population thresholds and inhibitory timescales determine both bump stability and the mechanism by which stability is lost, while inhibitory connection strengths and spatial scales substantially reshape the stable parameter region. In particular, broader SST connectivity promotes stable bump states. Finally, we derive an effective diffusion coefficient for noise-driven bump wandering and show that increasing the SST spatial footprint reduces the rate of memory diffusion. Together, these results demonstrate how inhibitory subtype structure can shape both the deterministic stability and stochastic precision of continuous-attractor memories.
Source: Stability and Wandering of Bumps in Neural Fields with Interneuron Subtypes