Periodic Reporting for period 1 - AutoMIND (Automated Model Inference from Neural Dynamics for a Mechanistic Understanding of Cognition)
Okres sprawozdawczy: 2021-05-01 do 2023-04-30
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.
Second, we developed a new SBI algorithm that incorporates the recently developed generalized Bayesian inference formalism. The proposed method allows the user to use arbitrary cost functions to evaluate the “goodness” of a parameter configuration in reproducing data, and is robust under the setting of model misspecification, i.e. the model cannot produce the data exactly. It similarly uses deep neural networks and is easily plugged into the existing SBI framework, where the inference result is a distribution of model parameters that can reproduce aspects of the data, but better fitting models are more frequently sampled. We evaluate the method on a variety of benchmark tasks, as well as use it to find single-neuron model parameters that can reproduce experimental recordings, and find superior performance than existing algorithms especially in cases of misspecified models.
Finally, we apply SBI to infer SNN model parameters that can produce simulations matching real data. We first validate our approach by performing inference on simulated network activity. SBI identifies many models that can reproduce both observed and unobserved features of network dynamics, revealing covariance structure and degeneracy between parameters. Applied to a dataset of brain organoid electrophysiological recordings, we automatically identify models that exhibit network bursts, while elucidating the co-evolution of cellular and network parameters over 40 weeks of development. Together, these results demonstrate how SBI can advance our understanding of the dynamical regimes of flexibly parameterized SNNs, while providing mechanistic explanations and generating hypotheses about hidden circuit properties that underlie changes in brain network dynamics.
The novel SBI algorithm is described in an arXiv preprint (Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation) and currently under review. Results on discovering SNN models from neural recordings using SBI have been presented at several neuroscience conferences, including SfN, Bernstein, and COSYNE meetings, and the manuscript is under preparation. Related works on inferring cognitive models and circuit wiring models from experimental data, as well as probabilistic ML methods for modeling neurophysiological recordings resulted from collaborations within the group, contributing to the overall Project goal of building ML tools to understand how neural circuits shape neural dynamics and computation.