Clustered connectivity

…Hawkes networks, with clustered connectivity, in an inhibition-stabilized regime. This mapping allows us to leverage powerful analytical results for CTLN networks

Neural population dynamics are shaped by many cellular, synaptic, and network properties. Not only is it important to understand how coordinated changes in circuit parameters alter neural activity, but also when dynamics are unaffected by---or invariant to---such changes. Computational modeling has revealed invariances in single neurons and small circuits that are thought to reflect their robustness against variability and perturbations. However, generalizing these insights to larger circuits in cortex and other brain areas remains challenging. A key bottleneck lies in inverse modeling of neur

…collective activity to the network's connectivity structure is a key goal of computational neuroscience. We approach this question for clustered networks, which can form via biologically realistic learning rules and allow for the re-activation of previously evoked patterns. Previous studies of clustered networks have focused on metastabilty between fixed points corresponding to individual clusters, leaving open the question of whether clustered spiking networks can display more rich dyna

Variations in cellular and network parameters shape neural dynamics and computation. Mechanistic models, such as spiking neural networks (SNNs), are instrumental for linking recordings of neural population dynamics to their underlying circuit parameters. However, SNNs lack analytical expressions and accessible gradients for parameter inference (i.e., model identification), making it difficult to systematically identify a single configuration that can reproduce experimental data, let alone the full space of data-consistent models and their uncertainty. As a result, SNNs are often reduced a prio