Structural and functional specializations of human fast spiking neurons support fast cortical signaling

Abstract

In rodent cortical networks, fast spiking interneurons (FSINs) provide fast inhibition that synchronizes neuronal activity and is critical for cognitive function. Fast synchronization frequencies are evolutionary conserved in the expanded human neocortex, despite larger neuron-to-neuron distances that challenge fast input-output transfer functions of FSINs. Here, we test which mechanistic specializations of large human FSINs explain their fast-signaling properties in human cortex. With morphological reconstructions, multi-patch recordings, and biophysical modeling we find that despite three-fold longer dendritic path lengths, human FSINs maintain fast inhibition between connected pyramidal neurons through several mechanisms: stronger synapse strength of excitatory inputs, larger dendrite diameter with reduced complexity, faster AP initiation, and faster and larger inhibitory output, while Na+ current activation /inactivation properties are similar. These adaptations underlie short input-output delays in fast inhibition of human pyramidal neurons through FSINs, explaining how cortical synchronization frequencies are conserved despite expanded and sparse network topology of human cortex.

Of mice and men: topologically complex dendrites assemble uniquely human networks

“…With the discovery of a larger proportion of interneurons in the human brain than in mouse, there has been speculation that they may have unforeseen functional significance (21). Our findings suggest that the simpler dendritic shapes of interneurons (32) may necessitate a higher abundance of these cells in order to maintain the balance between excitation and inhibition within the network. This suggestion is reinforced by experimental findings of preserved balance between inhibition and excitation as synaptic inputs of excitatory cells (21).…”

“…For this analysis, we focused on basket cells as they are the best characterized cell type of interneurons in the human brain. This analysis will be generalized to the highly diverse cortical interneuron types, once sufficient data become available (32). However, the higher density of branches around the somata of basket cells is significantly less prominent than the respective difference in branching density of pyramidal cells.…”

“…Despite the greater inter-neuron distances resulting from lower neuron density in human pyramidal cells (Fig 5A-B), the distinct topology of their dendritic arbors (Fig 5C-D) emerges as a mechanism to establish higher-order interactions, as evidenced by the larger number of higher-dimensional simplices in the network (Fig 5E-F). In contrast, while interneurons exhibit higher densities in human, their simpler structural characteristics (32) limit their capacity to enhance network complexity to the same extent. These observations are supported by a balance in inhibition and excitation (21) in the innervation of pyramidal cells in the human cortex.…”

Shared and divergent principles of synaptic transmission between cortical excitatory neurons in rodent and human brain

“…Experimental or analytical conditions such as developmental stage (i.e., juvenile, adolescent, adult), external Ca 2+ (1.3, 1.8, 2.0, 2.5 or 3.0 mM), temperature or reported mean/median could certainly influence the reported uEPSP amplitude, but it is also likely that the synaptic strength is dependent on the connection established between different L2/3 pyramidal cell types ( Deitcher et al, 2017 ; Hodge et al, 2019 ; Berg et al, 2021 ; Hunt et al, 2023 ). Pyramidal-to-interneuron or interneuron-to-pyramidal cell connections are typically stronger ( Molnar et al, 2016 ; Wilbers et al, 2023 ), but here we focus on synaptic transmission between pairs of excitatory neurons. In this context, it is important to emphasize that amplitude distributions are typically skewed with the majority of connections showing small amplitudes and a long tail of stronger connections ( Feldmeyer et al, 1999 ; Holmgren et al, 2003 ; Song et al, 2005 ; Cossell et al, 2015 ; Seeman et al, 2018 ; Hunt et al, 2023 ).…”