Topic: chaotic activity

We study the dynamics of (inhibitory) balanced networks at varying (i) the level of symmetry in the synaptic connectivity; and (ii) the ariance of the synaptic efficacies (synaptic gain). We find three regimes of activity. For suitably low synaptic gain, regardless of the level of symmetry, there exists a unique stable fixed point. Using a cavity-like approach, we develop a quantitative theory that describes the statistics of the activity in this unique fixed point, and the conditions for its stability. Increasing the synaptic gain, the unique fixed point destabilizes, and the network exhibits

Increasing the strength of synaptic connections (synaptic upscaling) within networks of randomly connected neurons transitions the internal dynamics from stationary to a chaotic regime [1, 2]. Synaptic upscaling in neural networks improves learning speed, accuracy, and robustness [3] and enhances the dimensionality of internal dynamics in response to external stimuli [2], by boosting chaotic activity. Nonetheless, the direct link between synaptic upscaling, the dimensionality of the network activity, and and enhanced computational capacity of neural networks for learning tasks remains unclear.