Computational Model Results on NMDA Receptor Kinetics and Neuronal Firing

A 2026 computational study by Mehdi Borjkhani analyzes over 2.9 million inter-spike intervals from a Hodgkin-Huxley-type model. The model incorporates NMDA, AMPA, and GABA receptor kinetics to investigate how NMDA receptor closing rates and glutamatergic stimulation control neuronal dynamics. The analysis characterizes deterministic chaos and links kinetic regimes to downstream plasticity signaling via CaMKII phosphorylation.

Use Cases

• Modeling pathways to deterministic chaos in neuronal firing based on NMDA receptor kinetics described in the study.
• Analyzing the impact of GABAergic inhibition on stabilizing parameter space, which the results indicate expanded stability by 34.2%.
• Investigating the relationship between prolonged NMDA activation and pathological long-term potentiation based on CaMKII phosphorylation analysis.
• Studying optimal kinetic windows for information transfer, identified at a specific NMDA deactivation rate (β_NMDA = 0.042 ms⁻¹).

Strengths

• Analysis is based on a systematic sweep of over 2.9 million inter-spike intervals.
• Model incorporates specific, biologically detailed components including NMDA, AMPA, and GABA receptor kinetics.
• Results quantify specific outcomes, such as a 34.2% expansion of stable parameter space with GABAergic inhibition and an optimal information transfer of 0.275 bits.

Limitations

• The underlying simulation data (rows, columns) is not directly accessible or described; the primary file is a PDF summary.
• Row count and column-level documentation are unknown, which limits suitability assessment for direct data analysis.
• Data reflects outputs from a specific computational model, not empirical measurements.

Provenance

Source

Mehdi Borjkhani via figshare.

Collection Method

Generated from a computational Hodgkin–Huxley-type model with parameter sweeps.

Freshness

Last updated 2026-06-03.