| Abstrakt: |
Recent developments in experimental techniques have enabled simultaneous recordings from thousands of neurons, enabling the study of functional cell assemblies. However, determining the patterns of synaptic connectivity giving rise to these assemblies remains challenging. To address this, we developed a complementary, simulation-based approach, using a detailed, large-scale cortical network model. Using a combination of established methods we detected functional cell assemblies from the stimulus-evoked spiking activity of 186,665 neurons. We studied how the structure of synaptic connectivity underlies assembly composition, quantifying the effects of thalamic innervation, recurrent connectivity, and the spatial arrangement of synapses on dendrites. We determined that these features reduce up to 30%, 22%, and 10% of the uncertainty of a neuron belonging to an assembly. The detected assemblies were activated in a stimulus-specific sequence and were grouped based on their position in the sequence. We found that the different groups were affected to different degrees by the structural features we considered. Additionally, connectivity was more predictive of assembly membership if its direction aligned with the temporal order of assembly activation, if it originated from strongly interconnected populations, and if synapses clustered on dendritic branches. In summary, reversing Hebb's postulate, we showed how cells that are wired together, fire together, quantifying how connectivity patterns interact to shape the emergence of assemblies. This includes a qualitative aspect of connectivity: not just the amount, but also the local structure matters; from the subcellular level in the form of dendritic clustering to the presence of specific network motifs. Author summary: It is widely known in the neuroscience field that cells that fire together wire together, forming so called "cell assemblies". However, on a quantitative level the picture is more complicated, because neurons can participate in several spatially distributed assemblies and receive synaptic inputs from many local and long-range sources. The effects of different connections on different assemblies are difficult to analyze because simultaneous recordings of neuronal activity and their connections are only feasible for small sets of neurons in a given region. Thus, we turned to a complimentary, simulation-based approach using a large-scale cortical microcircuit model with non-random, biorealistic connectivity. We found different types of assemblies that differed in how much they were determined by connections from local or long-range sources. Additionally, we characterized ways in which connectivity can tie a neuron into an assembly more efficiently, for example through dendritic clustering of synapses. [ABSTRACT FROM AUTHOR] |
