- kWTA now gets all > elements and all == elements for count(els)<k.
- input-ec weights are intialized only once, and have to be re-wired explicitly.
- neuronal turnover in the dg is to be called explicitly between training sets.
HPC-constructor:
- dims: number of neurons in the input-layer, ec-layer, dg-layer, ca3-layer, and output-layer
- connection_rate_input_ec: self-explanatory
- perforant_path: connection rate ec-dg and ec-ca3
- mossy_fibers: connection rate dg-ca3
- firing_rate_ec, firing_rate_dg, firing_rate_ca3: these decide the number of k active neurons in kWTA
- _gamma: learning rate in unbounded Hebbian learning
- _epsilon: steepness parameter in the transfer function, tanh(sum(in) / _epsilon)
- _nu: learning rate in the contrained Hebbian learning
- _turnover_rate: the relative frequency of neurons in the DG that are to be randomly re-initialized according to the firing rates and connection rates associated with the neurons
- _k_m, _k_r: damping factors for refractoriness in the equations for chaotic neurons, located in the ca3- and output-layers
- _a_i: constant outer stimuli / external input parameter, used in the chaotic neuron equations.
- _alpha: the scaling factor for refractoriness