P117 Layer- and Area-Specific Dynamics and Function in Spiking Cortical Neuronal Networks
M. Sharif Hussainyar1*, Dong Li1, Claus C. Hilgetag1
1Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf(UKE), 20251, Hamburg, Germany
*Email: m.hussainyar@uke.de
Introduction The cerebral cortex exhibits significant regional diversity, with areas varying in neuron density and the morphology of specific layers [1]. A spectrum of cortical types ranges from agranular, lacking layer 4, to granular, with a well-differentiated layer 4 [2,3]. These structural differences are relevant to cortical connectivity [4,5] and information flow. Correspondingly, different cortical areas and layers exhibit distinct dynamics and functions [6] underlying their computational roles, with faster neuronal timescales supporting sensory processing and slower dynamics in association areas [7,8]. However, how structural variations across cortical types shape these properties remains unclear.
Methods We developed a series of spiking network models to simulate different cortical types. Each model consists of leaky integrate-and-fire neurons organized into layers preserving critical structural features, such as the excitatory-inhibitory ratio, layer-specific neuron distributions, and interlaminar connections. To compare evolutionary cortical variations, we parameterized models for three distinct exemplars: rodents, non-human primates and humans, accounting for species-specific differences in cortical organization, neuronal density, and laminar structure patterns [9]. This approach allows us to examine how structural variations shape timescales and baseline activity across cortical types and species.
Results Fundamental dynamical properties such as timescale and baseline activity differ systematically between cortical types and layers. Granular types, exemplified by microcolumns in the visual system, exhibit shorter timescales than agranular types characteristic of association areas. These differential timescales imply functional specialization, where shorter timescales support rapid sensory processing, while longer timescales in agranular regions facilitate integrative functions requiring extended temporal windows. These findings align with experimental evidence and previous theoretical findings [6,8], and reinforce the hypothesis that structural variations shape cortical dynamics. Discussion Our findings confirm that structural variations shape cortical dynamics and function. The observed timescales differences between cortical types align with experimental data and support computational theories of functional specialization [8]. The cortical-type-based connectivities, along with the integrate-and-fire nature of cortical neurons, establish the foundation for area- and layer-specific cortical timescales and baseline activity. These, in turn, define the fundamental functional units by shaping how different cortical areas and layers process and integrate information from external inputs.
Acknowledgements This work was in part founded by the, SH: Landesforschungsförderung Hamburg(LFF)-FV76. DL:TRR169-A2. CCH: Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), SFB 936, Project-ID 178316478-A1/Z3; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project-ID 434434223 SFB 1461; DFG TRR-169 (A2).
