Adult-born dentate granule cells promote hippocampal population sparsity

Abstract

The dentate gyrus (DG) gates neocortical information flow to the hippocampus. Intriguingly, the DG also produces adult-born dentate granule cells (abDGCs) throughout the lifespan, but their contribution to downstream firing dynamics remains unclear. Here, we show that abDGCs promote sparser hippocampal population spiking during mnemonic processing of novel stimuli. By combining triple-(DG-CA3-CA1) ensemble recordings and optogenetic interventions in behaving mice, we show that abDGCs constitute a subset of high-firing-rate neurons with enhanced activity responses to novelty and strong modulation by theta oscillations. Selectively activating abDGCs in their 4–7-week post-birth period increases sparsity of hippocampal population patterns, whereas suppressing abDGCs reduces this sparsity, increases principal cell firing rates and impairs novel object recognition with reduced dimensionality of the network firing structure, without affecting single-neuron spatial representations. We propose that adult-born granule cells transiently support sparser hippocampal population activity structure for higher-dimensional responses relevant to effective mnemonic information processing.

Chemogenetic Stimulation of Adult Neurogenesis, and Not Neonatal Neurogenesis, Is Sufficient to Improve Long-Term Memory Accuracy

“…We have shown that 3 month-old adult-born neurons are recruited by memory retrieval (Tronel et al, 2015a) and that inhibiting 6 month-old adult-born neurons impairs short-term memory retention, indicating that adult born cells do not need to be young to participate in memory retention (Masachs et al, 2021). Interestingly, 9-12 week-old adult-born neurons lose their ability to promote population sparsity, but become more spatially tuned and can entrain DG principal cells (McHugh et al, 2022), properties that could also support memory accuracy.…”

“…All data shown are mean ± s.e.m. For statistical details, see Table S1 McHugh et al, 2022) which may be related to increased excitability, reduced perisomatic inhibition and enhanced synaptic plasticity (long-term depression and excitation) (Lodge and Bischofberger, 2019;Massa et al, 2011). Recently, it has been shown that neurons aged 4-7-weeks support sparser hippocampal population activity, suggesting that sparse coding may allow for higher dimensional network activity in support of memory accuracy (McHugh et al, 2022).…”

“…For statistical details, see Table S1 McHugh et al, 2022) which may be related to increased excitability, reduced perisomatic inhibition and enhanced synaptic plasticity (long-term depression and excitation) (Lodge and Bischofberger, 2019;Massa et al, 2011). Recently, it has been shown that neurons aged 4-7-weeks support sparser hippocampal population activity, suggesting that sparse coding may allow for higher dimensional network activity in support of memory accuracy (McHugh et al, 2022). In addition, they control network oscillations (Lacefield et al, 2012;McHugh et al, 2022) most probably via inhibitory interneurons (Drew et al, 2016;Lodge and Bischofberger, 2019;Park et al, 2015;Temprana et al, 2015).…”

“…Recently, it has been shown that neurons aged 4-7-weeks support sparser hippocampal population activity, suggesting that sparse coding may allow for higher dimensional network activity in support of memory accuracy (McHugh et al, 2022). In addition, they control network oscillations (Lacefield et al, 2012;McHugh et al, 2022) most probably via inhibitory interneurons (Drew et al, 2016;Lodge and Bischofberger, 2019;Park et al, 2015;Temprana et al, 2015). By modulating the temporal dynamics of hippocampal network activity they could exert their influence beyond hippocampal activity (Mateus-Pinheiro et al, 2021).…”

Robust and consistent measures of pattern separation based on information theory and demonstrated in the dentate gyrus

“…Classical measures of pattern separation, such as orthogonalisation, decorrelation, or spike train distance, are effective at analysing the measured behaviour of a neuronal system and quantify the degree of difference between the inputs and outputs. In the dentate gyrus, outputs are typically encoded by sparser activity than the inputs and sparsity is a major contributor to pattern separation (Rolls and Treves, 1994; Battaglia and Treves, 1998; McHugh et al, 2022). Increasing sparsity in a neural code, however, may reduce the information it carries about the afferent signal (Billings et al, 2014).…”