Implementation of the paper Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints by Habenschuss et al.
MIT License
Implementation of the paper Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints by Habenschuss et al. This paper gives learning rules for a spiking neural network just based on Bayesian reasoning; therefore, the method can be used for unsupervised training of networks.
Contains code to runs different experiments on the proposed model and also on a model that is based not on a Binomial but Gaussian input distribution.
The code was written and the experiments conducted during a one week lasting seminar at the Max-Planck Institute for Dynamics and Self-Organization in 2019.
eta_b
has to be sufficiently large, otherwise homeostasis is not strong enough to keep r
similar for all output neuronsA_k(V)
contributes exponentially while b_k
contributes only linearly. Therefore, a factor of ten between eta_V
and eta_b
is not always optimal.When images of digits between zero and five with the same ratio are shown to a network with 12 output neurons, for each class two neurons that are class-receptive arise. The neurons slowly learn to react to one of the input types.