Introduction Maintaining a working memory of sensory inputs is a fundamental neural computation, widely thought to rely on synaptic plasticity or dedicated attractor dynamics [1,2]. A complementary substrate is provided by dendritic subunits equipped with NMDA receptors, whose slowly decaying, voltage-gated conductances allow information to persist within individual neurons beyond the membrane timescale. To study how NMDA-driven dendritic dynamics contribute to working memory in a recurrent network, we introduce a dendritic extension of the classical Brunel network [3], yielding a minimal system with realistic NMDA kinetics that preserves the well-characterized asynchronous irregular dynamics of the original model.
Methods Excitatory neurons are modeled with one somatic and five dendritic compartments incorporating AMPA/NMDA receptor kinetics. In this neuron model, NMDA receptors provide voltage-gated, slowly decaying conductances. Excitatory neurons are organized into clusters with tunable inter- and intra-cluster connection probabilities; inhibitory neurons provide global inhibition targeting random dendritic compartments. MNIST digit images are presented by converting pixel intensities to Poisson spike train firing rates. Network activity serves as a reservoir, and a linear readout trained on spike rates classifies digit identity. Decoding accuracy is evaluated across post-stimulus time windows to quantify the temporal persistence of stimulus information.
Results Simulations are ongoing; early observations suggest that excitatory clustering modulates the emergence of NMDA-driven activity in the dendritic compartments and extends the temporal persistence of stimulus-specific population dynamics beyond the timescales seen in the standard Brunel network. Preliminary decoding results indicate that stimulus information is retained over longer post-stimulus intervals, with above-chance classification accuracy persisting at delays where the standard network falls to chance.
Discussion
References [1] Mongillo, G., Barak, O., & Tsodyks, M. (2008). Synaptic theory of working memory. Science. https://www.science.org/doi/10.1126/science.1150769 [2] Amit, D. J., & Brunel, N. (1997). Model of Global Spontaneous Activity and Local Structured Activity During Delay Periods in the Cerebral Cortex. Cerebral Cortex. https://doi.org/10.1093/cercor/7.3.237 [3] Brunel, N. (2000). Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons. Journal of Computational Neuroscience. https://doi.org/10.1023/A:1008925309027
Acknowledgement NeuroSys as part of the initiative “Clusters4Future” is funded by the Federal Ministry of Education and Research BMBF (03ZU2106CB).
