Variations in cellular and network parameters are crucial in shaping brain dynamics, which are the foundation of human cognition and behavior. This project addressed the problem of inferring properties of neural circuits using computational modeling, machine learning (ML), and brain recordings. We approached this problem as an “inverse modeling” task: We first developed biologically realistic computer simulators of neural circuits. By finding model parameters that produce simulations like experimental data, we can obtain a “digital copy” of the circuit, enabling us to dissect the model in greater detail than possible in in-vivo systems. To link recordings of neural population dynamics with their underlying circuit parameters, we used spiking neural networks (SNNs). However, with biologically realistic SNNs, it is challenging to identify a single model configuration that can reproduce experimental data, let alone many data-consistent models and their uncertainties. Therefore, we developed probabilistic ML methods to address the problem of mechanistic model identification given experimental data in neuroscience.
The ability to infer neural circuit properties from brain recordings has significant implications for society: Understanding the link between neural circuit properties, dynamics, and computations is an ongoing challenge in brain research. This applies to both healthy brains and those affected by neurological and psychiatric disorders. Being able to create and analyze data-consistent computational models of neural circuits, instead of relying solely on in-vivo counterparts, is a significant step towards this goal, potentially accelerating neuroscience research, and reducing costs and the need for invasive experiments on model organisms. Moreover, computer models allow for parallel testing of numerous interventions to study their effects on pathological brain states, increasing efficiency while minimizing risks. Finally, ML methods for inverse modeling are broadly applicable in many areas of science where simulators are used, such as astrophysics, geology, biochemistry, and more.
Here, we aimed to develop a "family" of SNN models with biologically realistic parameters. These models should exhibit a range of realistic network dynamics observed in-vivo depending on parameter values, e.g. oscillations, bursting, and asynchronous activity. Second, we developed (Bayesian) ML algorithms for inverse modeling, particularly within the framework of simulation-based inference (SBI), to enhance the accuracy and efficiency of model parameter inference. Finally, we applied these techniques to infer cellular and network properties from experimental data collected from human brain organoids and animal cortex. This enables us to infer changes across time and brain areas, advancing our understanding of early brain development and how different neural circuit properties contribute to neural dynamics and computation.
