N-methyl-D-aspartate receptors (NMDARs) are excitatory glutamate-gated ion channels that are expressed throughout the central nervous system. NMDARs mediate calcium entry into cells, and are involved in a host of neurological functions. The GluN2A subunit, encoded by the GRIN2A gene, is expressed by both excitatory and inhibitory neurons, with well described roles in pyramidal cells. By using Grin2a knockout mice, we show that the loss of GluN2A signaling impacts parvalbumin-positive (PV) GABAergic interneuron function in hippocampus. Grin2a knockout mice have 33% more PV cells in CA1 compared to wild type but similar cholecystokinin-positive cell density. Immunohistochemistry and electrophysiological recordings show that excess PV cells do eventually incorporate into the hippocampal network and participate in phasic inhibition. Although the morphology of Grin2a knockout PV cells is unaffected, excitability and action-potential firing properties show age-dependent alterations. Preadolescent (P20-25) PV cells have an increased input resistance, longer membrane time constant, longer action-potential half-width, a lower current threshold for depolarization-induced block of action-potential firing, and a decrease in peak action-potential firing rate. Each of these measures are corrected in adulthood, reaching wild type levels, suggesting a potential delay of electrophysiological maturation. The circuit and behavioral implications of this age-dependent PV interneuron malfunction are unknown. However, neonatal Grin2a knockout mice are more susceptible to lipopolysaccharide and febrile-induced seizures, consistent with a critical role for early GluN2A signaling in development and maintenance of excitatory-inhibitory balance. These results could provide insights into how loss-of-function GRIN2A human variants generate an epileptic phenotypes.

Inhibitory neurons take on many forms and functions. How this diversity contributes to memory function is not completely known. Previous formal studies indicate inhibition differentiated by local and global connectivity in associative memory networks functions to rescale the level of retrieval of excitatory assemblies. However, such studies lack biological details such as a distinction between types of neurons (excitatory and inhibitory), unrealistic connection schemas, and non-sparse assemblies. In this study, we present a rate-based cortical model where neurons are distinguished (as excitatory, local inhibitory, or global inhibitory), connected more realistically, and where memory items correspond to sparse excitatory assemblies. We use this model to study how local-global inhibition balance can alter memory retrieval in associative memory structures, including naturalistic and artificial structures. Experimental studies have reported inhibitory neurons and their sub-types uniquely respond to specific stimuli and can form sophisticated, joint excitatory-inhibitory assemblies. Our model suggests such joint assemblies, as well as a distribution and rebalancing of overall inhibition between two inhibitory sub-populations – one connected to excitatory assemblies locally and the other connected globally – can quadruple the range of retrieval across related memories. We identify a possible functional role for local-global inhibitory balance to, in the context of choice or preference of relationships, permit and maintain a broader range of memory items when local inhibition is dominant and conversely consolidate and strengthen a smaller range of memory items when global inhibition is dominant. This model therefore highlights a biologically-plausible and behaviourally-useful function of inhibitory diversity in memory.

How can neural networks learn to efficiently represent complex and high-dimensional inputs via local plasticity mechanisms? Classical models of representation learning assume that input weights are learned via pairwise Hebbian-like plasticity. Here, we show that pairwise Hebbian-like plasticity only works under specific requirements on neural dynamics and input statistics. To overcome these limitations, we derive from first principles a learning scheme based on voltage-dependent synaptic plasticity rules. Here, inhibition learns to locally balance excitatory input in individual dendritic compartments, and thereby can modulate excitatory synaptic plasticity to learn efficient representations. We demonstrate in simulations that this learning scheme works robustly even for complex, high-dimensional and correlated inputs. It also works in the presence of inhibitory transmission delays, where Hebbian-like plasticity typically fails. Our results draw a direct connection between dendritic excitatory-inhibitory balance and voltage-dependent synaptic plasticity as observed in vivo, and suggest that both are crucial for representation learning.