Training dynamically balanced excitatory-inhibitory networks

Abstract

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.

12 pages, 7 figures

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Cited by in corpus (9)

• Synaptic polarity and sign-balance prediction using gene expression data in the Caenorhabditis elegans chemical synapse neuronal connectome network
• Thalamo-cortical spiking model of incremental learning combining perception, context and NREM-sleep-mediated noise-resilience
• Input correlations impede suppression of chaos and learning in balanced rate networks
• Target spiking patterns enable efficient and biologically plausible learning for complex temporal tasks
• Error-based or target-based? A unifying framework for learning in recurrent spiking networks
• MAP-SNN: Mapping Spike Activities with Multiplicity, Adaptability, and Plasticity into Bio-Plausible Spiking Neural Networks
• Optimal Learning with Excitatory and Inhibitory synapses
• Different eigenvalue distributions encode the same temporal tasks in recurrent neural networks
• Effect in the spectra of eigenvalues and dynamics of RNNs trained with Excitatory-Inhibitory constraint