Fast signaling and focal connectivity of PV⁺ interneurons ensure efficient pattern separation by lateral inhibition in a full-scale dentate gyrus network model

Abstract

Pattern separation is a fundamental brain computation that converts small differences in synaptic input patterns into large differences in action potential (AP) output patterns. Pattern separation plays a key role in the dentate gyrus, enabling the efficient storage and recall of memories in downstream hippocampal CA3 networks. Several mechanisms for pattern separation have been proposed, including expansion of coding space, sparsification of neuronal activity, and simple thresholding mechanisms. Alternatively, a winner-takes-all mechanism, in which the most excited cells inhibit all less-excited cells by lateral inhibition, might be involved. Although such a mechanism is computationally powerful, it remains unclear whether it operates in biological networks. Here, we develop a full-scale network model of the dentate gyrus, comprised of granule cells (GCs) and parvalbumin + (PV + ) inhibitory interneurons, based on experimentally determined biophysical cellular properties and synaptic connectivity rules. Our results demonstrate that a biologically realistic principal neuron-interneuron (PN-IN) network model is a highly efficient pattern separator. Mechanistic dissection in the model revealed that a winner-takes-all mechanism by lateral inhibition plays a crucial role in pattern separation. Furthermore, both fast signaling properties of PV + interneurons and focal GC-interneuron connectivity are essential for efficient pattern separation. Thus, PV + interneurons are not only involved in basic microcircuit functions, but also contribute to higher-order computations in neuronal networks, such as pattern separation.

Differential Relation between Neuronal and Behavioral Discrimination during Hippocampal Memory Encoding

“…In summary, our data reveal that the DG produces highly orthogonalized output by activating sparse non-overlapping subpopulations of neurons in response to small or large changes between environments. This high selectivity is observed in the population firing patterns of active cells as well as in the subthreshold membrane potential dynamics of silent neurons, consistent with a scenario where pattern separation is implemented by expansion coding in concert with inhibition (Cayco-Gajic and Silver, 2019;Espinoza et al, 2018;Guzman et al, 2019;Marr, 1969;McNaughton and Nadel, 1990;Rolls and Treves, 1998). Such a model does not require any specifically targeted connectivity…”

Impact of Perineuronal Nets on Electrophysiology of Parvalbumin Interneurons, Principal Neurons, and Brain Oscillations: A Review

“…Suppression of PV interneurons increased activity, but it also increased the cell overlap for two stimuli that originally had a more separate representation in the brain (Agetsuma et al, 2018 ). PV interneuron-mediated winner-take-all lateral inhibition also appears to be supported as a mechanism for pattern separation in the dentate gyrus (Espinoza et al, 2018 ; Guzman et al, 2019 ; Braganza et al, 2020 ). Importantly, Braganza et al ( 2020 ) suggested that a mechanism of pattern separation occurred in the dentate gyrus similar to that found in the cortex.…”

Dentate gyrus somatostatin cells are required for contextual discrimination during episodic memory encoding

“…Furthermore, the regulation of orthogonalization of granule cells relies heavily on the perisomatic lateral inhibition provided by parvalbumin cells (83, 84). In turn, lateral inhibition might be controlled by SOM through direct dendritic inhibition of granule cells and perisomatic inhibition of parvalbumin cells (30).…”