Primate neuronal connections are sparse as compared to mouse

Abstract

SummaryThe mouse and macaque primary visual cortices are foundational models of cortical functioning, particularly at the level of single neurons. Therefore, detailing differences in how individual neurons connect across these species would inform models of cortical functioning and of how brains evolve. However, existing comparisons are limited, measuring synapse density without regard to where synapses are made or on what types of neurons. We use large volume electron microscopy to address this gap, reconstructing a total of 7735 synapses across 160 total neurons (146 excitatory, 14 inhibitory) from adult Rhesus macaque and mouse Layer 2/3 of primary visual cortex (V1). We find that primate connections are broadly sparse: primate excitatory and inhibitory neurons received 3-5 times fewer spine and somatic synapses with lower ratios of excitatory to inhibitory synapses than mouse equivalents. However, despite reductions in absolute synapse number, patterns of axonal innervation were preserved: inhibitory axons sparsely innervated neighboring excitatory neurons in macaque and mouse at similar rates and proportions. On the output side, most excitatory axons in mice myelinated close to the soma (81%) while most primate axons (68%) did not. Interestingly, primate axons, but not mouse axons, that myelinated had 3.3 fold more axon initial segment synapses than axons that did not myelinate, suggesting differential inhibitory control of long distance output in primate brains. Finally, we discover that when artificial recurrent neural networks (RNNs) are constrained by the metabolic cost of creating and maintaining synapses, increasing the number of nodes (e.g. neurons) as networks optimize for a cognitive task, reduces the number of connections per node, similar to primate neurons as compared to mice.One Sentence SummaryUsing large volume serial electron microscopy, we show that primate cortical neural networks are sparser than mouse and using recursive neural nets, we show that energetic costs of synaptic maintenance could underlie this difference.