Training dynamically balanced excitatory-inhibitory networks
This work addresses the problem of building functional neural circuit models for neuroscience researchers, but it appears incremental as it builds on existing target-based methods.
The authors tackled the challenge of constructing biologically plausible neural networks with separate excitatory and inhibitory neurons by developing a target-based approach combined with online constrained optimization, resulting in networks that can produce complex temporal patterns and solve tasks while maintaining biological features like Dale's law and response variability.
The construction of biologically plausible models of neural circuits is crucial for understanding the computational properties of the nervous system. Constructing functional networks composed of separate excitatory and inhibitory neurons obeying Dale's law presents a number of challenges. We show how a target-based approach, when combined with a fast online constrained optimization technique, is capable of building functional models of rate and spiking recurrent neural networks in which excitation and inhibition are balanced. Balanced networks can be trained to produce complicated temporal patterns and to solve input-output tasks while retaining biologically desirable features such as Dale's law and response variability.