Jannik Lubeoinski @ INRC – Brian2Lava: connecting the Brian 2 simulator to neuromorphic hardware

Abstract:
Neuromorphic hardware allows for fast and energy-efficient simulation of spiking neural networks. However, the usage of such devices is still a challenge, as it requires detailed knowledge about the neuromorphic hardware as well as the used software interface, e.g., the Lava framework for neuromorphic computing spearheaded by Intel. This stands in contrast to the relative ease of simulating spiking neural networks on conventional CPU or GPU architectures, for which user-friendly simulation environments exist. The Brian 2 simulator, for instance, allows to readily define a spiking neural network with a set of equations, handling all subsequent hardware interactions.

To link the best of both worlds, we are developing Brain2Lava. Brian2Lava combines the intuitive user interface of Brian 2 with the functionality of Lava. By means of a so-called device for Brian 2, Brian2Lava seamlessly generates and executes the desired simulations in Lava without the need for users to write additional code. At the current stage, Brian2Lava supports most Brian 2 features when executing Lava on CPU, and a selection of essential features for the execution on Intel’s Loihi 2 chip. We are constantly working to expand the number of features supported with the chip, aiming to enable to flexibly execute simulations on different hardware platforms.

In summary, by bridging the gap between user-friendly model definition and neuromorphic implementation, Brian2Lava empowers engineers and neuroscientists alike to leverage the potential of neuromorphic hardware with greater ease and efficiency.

Bio: Jannik Luboeinski is currently a postdoctoral researcher at University of Göttingen. He received his B.Sc. and M.Sc. degrees in Physics from Technical University of Darmstadt and Goethe University Frankfurt, respectively. From 2017 to 2021, he did his Ph.D. with Christian Tetzlaff at University of Göttingen, investigating the role of two-phase synaptic plasticity in recurrent spiking neural networks, which resulted in the publication of several journal papers. In 2021, Dr. Luboeinski continued to work in the group of Professor Tetzlaff (now Computational Synaptic Physiology Group) as a postdoctoral researcher. A major aim of his research is to identify properties that enable efficient memory processes in biological and artificial neural systems. His work currently focuses on neuromorphic computing and the development of simulation software for recurrent spiking neural networks.

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