Biological Dynamics Enabling Training of Binary Recurrent Networks

Neuromorphic computing systems have been used for the processing of spatiotemporal video-like data, requiring the use of recurrent networks, while attempting to minimize power consumption by utilizing binary activation functions. However, previous work on binary activation networks has primarily focused on training of feed-forward networks due to difficulties in training recurrent binary networks. Spiking neural networks however have been successfully trained in recurrent networks, despite the fact that they operate with binary communication. Intrigued by this discrepancy, we design a generalized leaky-integrate and fire neuron which can be deconstructed to a binary activation unit, allowing us to investigate the minimal dynamics from a spiking network that are required to allow binary activation networks to be trained. We find that a subthreshold integrative membrane potential is the only requirement to allow an otherwise standard binary activation unit to be trained in a recurrent network. Investigating further the trained networks, we find that these stateful binary networks learn a soft reset mechanism by recurrent weights, allowing them to approximate the explicit reset of spiking networks.