ZOOM LINK:
https://uni-frankfurt.zoom.us/j/96438263549?pwd=Zm8rVWZ3YjZRWjVuUzNKWTRJaUFMUT09
Password: 196137
Fabian Schubert,
Claudius GrosRecurrent cortical network dynamics plays a crucial role for sequential information processing in the brain. While the theoretical framework of reservoir computing provides a conceptual basis for the understanding of recurrent neural computation, it often requires manual adjustments of global network parameters, in particular of the spectral radius of the recurrent synaptic weight matrix. Being a mathematical and relatively complex quantity, the spectral radius is not readily accessible to biological neural networks, which are based on the principle that information about the network state should either be encoded in local intrinsic dynamical quantities (e.g. membrane potentials), or transmitted via synaptic connectivity. We present an intrinsic adaptation rule, termed _flow control_ , for echo state networks that solely relies on locally accessible variables, while still being capable of tuning a global quantity, the spectral radius of the network, towards a desired value. The adaptation rule works online, in the presence of a continuous stream of input signals. It is based on a local comparison between the mean squared recurrent membrane potential and the mean squared activity of the neuron itself. It is derived from a global scaling condition on the dynamic flow of neural activities, and requires the separability between external and recurrent input currents. The effectiveness of the presented mechanism is tested numerically using different external input protocols. Furthermore, the network performance after applying the adaptation is evaluated by training the network to perform a time delayed XOR operation on binary sequences.
