yunhui

Results 25 issues of yunhui

I have purchased a VPN service which support connection by OpenVPN by importing a profile from URL. However it seems that importing from URL works only in OpenVPN-Connect client, not...

Hello, thanks for this interesting tool. I am using Matlab R2017b Update7. I have installed the tool according to setup guide, but I have noticed several issues. Issue 1: Matlab...

## Describe the bug It seems that envpool's vectorized environment is not compatible with gymnasium's NormalizeObservation wrapper due to missing "num_envs", "is_vector_env" and "single_observation_space" attributes in the environments returned by...

question

## Overview In function `default_cell_loader()` of "bmtk/simulator/filternet/default_setters/cell_loaders.py", variable `linear_filter_son` (line 121) and `linear_filter_soff` (line 124) take the same variable `spatial_filter` as input. This causes the two former variables to store...

The following changes are made in `bmtk/simulator/filternet/lgnmodel/fitfuns.py` to significantly speed up network preparation: * Optimize function `ff()` to use vectorized computation instead of looping over an NumPy array. * Replace...

In line 57-60 of `bmtk/simulator/filternet/lgnmodel/cellmodel.py`, the `dominant_unit` and `nondominant_unit` are created as: ```python self.dominant_unit = LNUnit(self.dominant_filter, ScalarTransferFunction('s'), amplitude=self.dominant_filter.amplitude) self.nondominant_unit = LNUnit(self.nondominant_filter, ScalarTransferFunction('s'), amplitude=self.dominant_filter.amplitude) ``` This looks strange to me, as...

The consine bump basis function of LGN cells in [Pillow et.al. J Neurosci 2005](https://www.jneurosci.org/content/25/47/11003) is: ``` B(t) = (cos(log[t+tau] - phi) + 1) / 2, if phi-pi < log[t+tau] <...

In #626 we mentioned that the rotation method in delay variables does not implement an autograd functionality. However I have tested this in training and found that the parameters can...

For a model written to process single input data, is it possible to convert the model to process batched input data simply by using `jax.vmap`? Or do we have to...

In the documentation of [monitor every multiple steps](https://brainpy.readthedocs.io/en/latest/tutorial_simulation/monitor_per_multiple_steps.html), two methods are provided. One using `brainpy.math.for_loop` and the other using `model.jit_step_run`. I have profiled the running speed of the given two...