The role of NMDA receptors in memory and prediction in cultured neural networks

Martina Lamberti*, Michel J.A.M. van Putten, Sarah Marzen, Joost le Feber*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Objective: Memory has been extensively studied at the behavioural as well as the cellular level. Spike timing dependent plasticity is widely considered essential for long-term memory and is associated with activation of N-methyl-D-aspartate (NMDA) receptors. This suggests that NMDA receptor activation plays a crucial role in enabling long-term memory. However, experimental evidence remains sparse, probably due to the complex combination of cellular and functional readouts required.

Approach: Recent work showed that in-vitro cortical networks memorize and predict inputs. The initial dependency of prediction on short-term memory decreased during the formation of long-term memory traces. Here, we stimulated networks of dissociated cortical neurons that were grown on multi electrode arrays to investigate memory and prediction under control conditions, or under NMDA block.

Main results: The NMDA antagonist 2-amino-5-phosphonovaleric acid (APV) at the used concentration impeded long-term memory trace formation, but did not significantly reduce network excitability. In APV-treated cultures short-term memory of stimuli persisted and they were still able to predict. In contrast to control cultures, prediction remained fully dependent on short-term memory. Significance. This confirms that NMDA receptor activation is essential for the formation of long-term memory traces and supports the notion that, as control cultures learn to memorize the stimulus, long-term memory starts to contribute to their predictive capability.

Original languageEnglish
Article number016053
JournalJournal of neural engineering
Volume22
Issue number1
DOIs
Publication statusPublished - 1 Feb 2025

Keywords

  • UT-Hybrid-D
  • Memory and prediction
  • Multi electrodes array
  • NMDA receptors
  • Synaptic plasticity
  • In-vitro neural networks

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