Abstract
It is generally appreciated that storing memories of specific events in the mammalian brain, and associating features of the environment with behavioral outcomes requires fine-tuning of the strengths of connections between neurons through synaptic plasticity. It is less understood whether the organization of neuronal circuits comprised of multiple distinct neuronal cell types provides an architectural prior that facilitates learning and memory by generating unique patterns of neuronal activity in response to different stimuli in the environment, even before plasticity and learning occur. Here we simulated a neuronal network responding to sensory stimuli, and systematically determined the effects of specific neuronal cell types and connections on three key metrics of neuronal sensory representations: sparsity, selectivity, and discriminability. We found that when the total amount of input varied considerably across stimuli, standard feedforward and feedback inhibitory circuit motifs failed to discriminate all stimuli without sacrificing sparsity or selectivity. Interestingly, networks that included dedicated excitatory feedback interneurons based on the mossy cells of the hippocampal dentate gyrus exhibited improved pattern separation, a result that depended on the indirect recruitment of feedback inhibition. These results elucidate the roles of cellular diversity and neural circuit architecture on generating neuronal representations with properties advantageous for memory storage and recall.
Highlights
A prerequisite for highly similar experiences to be stored in the brain as distinct memories that can be independently recalled is for different combinations of sensory inputs to produce distinct patterns of neuronal activity
We found that incorporating into the network a dedicated recurrent excitatory interneuron modeled after the mossy cells of the dentate gyrus resulted in output patterns that were highly sparse and discriminable from each other
These specialized excitatory feedback interneurons received a copy of the sparse output of the circuit, increased their own activity via recurrent excitatory connections with each other, and provided dense excitation to feedback inhibitory interneurons that in turn enforced a low fraction of active output neurons
Summary
A prerequisite for highly similar experiences to be stored in the brain as distinct memories that can be independently recalled is for different combinations of sensory inputs to produce distinct patterns of neuronal activity. This important function of neuronal circuits is termed “pattern separation,” and it is thought that a brain region in mammals called the hippocampus subserves this function as part of a larger role in the storage and recall of spatial and episodic memories (Burgess et al, 2002; Leutgeb et al, 2007; Yassa and Stark, 2011). In this study we use computational modeling to investigate the neural circuit mechanisms that support this transformation from dense and overlapping combinatorial patterns of activity in cortex into ultrasparse, unique patterns of activity in the hippocampus
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