Imagine you’re a detective trying to solve a complex crime. You have a vast number of suspects and only a few key clues that will lead you to the culprit. To crack the case, you need to explore different paths, strategies, and analyze each suspect’s behavior. This is similar to what scientists face when studying the brain using computational neuroscience models. These models have countless parameters, and only certain combinations unlock the secrets of brain dynamics. In their quest for knowledge, researchers have turned to an ingenious tool – Learning to Learn (L2L).

L2L is like having a brilliant assistant who helps you navigate through the vast landscape of parameter and hyper-parameter space. Just as a skilled investigator can sift through evidence quickly, L2L employs high-performance computers (HPC) to speed up these explorations. It utilizes a two-loop optimization process, enabling researchers to fine-tune their models for maximum performance.

In this study, scientists harness the power of L2L in Python to conduct parameter and hyper-parameter space exploration. They showcase its versatility by optimizing various neuroscience models, from single-cell simulations to full-blown simulations of the entire brain. The examples span a wide array of tasks – from replicating experimental results to learning how to tackle dynamic environments. By leveraging L2L’s open-source software and built-in optimizer algorithms, they achieve adaptive and efficient exploration of parameter spaces.

The findings highlight the importance of efficient tools and strategies in advancing our understanding of the brain. If you’re curious about unraveling the mysteries of neuroscience models and exploring cutting-edge research, make sure to dive into this fascinating study!

Neuroscience models commonly have a high number of degrees of freedom and only specific regions within the parameter space are able to produce dynamics of interest. This makes the development of tools and strategies to efficiently find these regions of high importance to advance brain research. Exploring the high dimensional parameter space using numerical simulations has been a frequently used technique in the last years in many areas of computational neuroscience. Today, high performance computing (HPC) can provide a powerful infrastructure to speed up explorations and increase our general understanding of the behavior of the model in reasonable times. Learning to learn (L2L) is a well-known concept in machine learning (ML) and a specific method for acquiring constraints to improve learning performance. This concept can be decomposed into a two loop optimization process where the target of optimization can consist of any program such as an artificial neural network, a spiking network, a single cell model, or a whole brain simulation. In this work, we present L2L as an easy to use and flexible framework to perform parameter and hyper-parameter space exploration of neuroscience models on HPC infrastructure. Learning to learn is an implementation of the L2L concept written in Python. This open-source software allows several instances of an optimization target to be executed with different parameters in an embarrassingly parallel fashion on HPC. L2L provides a set of built-in optimizer algorithms, which make adaptive and efficient exploration of parameter spaces possible. Different from other optimization toolboxes, L2L provides maximum flexibility for the way the optimization target can be executed. In this paper, we show a variety of examples of neuroscience models being optimized within the L2L framework to execute different types of tasks. The tasks used to illustrate the concept go from reproducing empirical data to learning how to solve a problem in a dynamic environment. We particularly focus on simulations with models ranging from the single cell to the whole brain and using a variety of simulation engines like NEST, Arbor, TVB, OpenAIGym, and NetLogo.

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