* Bugfixes: * In rare cases the 'w' variable could not be recorded. * Bugfix in early stopping of simulations (simulate_until). * Bugfix in CUDA code generation using dense matrix format. * Bugfix in code generation related to Runge-Kutta 4th order method and refractoriness. * Bugfix in cython wrapper for Monitor (github issue 3)
4.7.1.4
* `reset()` has a new argument *monitors* to control the clearing of monitored results. * Improved error messages for non-implemented features on CUDA devices. * Bugfixes: * fixed bugs related to compuation of mean-firing rate for spiking models. * fixed a bug related to IndividualNeuron accessor.
4.7.1.3
* Extended the available set of hand-written SpMV codes (in particular SSE and AVX-512) * Bugfixes: * the usage of storage_format="dense" and a constant weight (e.g. in the COBA example) leads to an error message. * Network.enable_learning() had no period/offset arguments. * TypeError occured in connect_fixed_number_post() cause by a type mismatch on a temporary variable.
4.7.1.2
* Bugfixes: * Handling of non-uniform delays in connect_with_func * Added Monitor.reset() to reinitialize monitors. Called automatically by reset().
4.7.1.1
* Changed default compiler flags used by ANNarchy: * cython modules build during setup.py use only `-O3` * c++ (model compile) default flags are now: `-O3 -ffast-math -fno-finite-math-only -march=native` (following Brian2 default flags) * Improved automatic format selection for rate-coded models (experimental, we greatly appreciate bug reports). * HomogeneousCorrelatedSpikeTrains: `corr` can now be a single value. * TimedPoissonPopulation: `rates` can now be a single value.
* Bugfixes: * Monitor: fixed start and stop times. * CUDA: device configuration (annarchy.json) was ignored.
4.7.1
* Automatic format selection for rate-coded models (experimental, we greatly appreciate bug reports). * Bugfixes for data structures introduced in 4.7.0. * Improved code generation, e. g. AVX instructions for default psp of rate-coded models, added dense matrices for spiking models.