From 581c434a12edb4991a1f395b23c080c0bb7891c4 Mon Sep 17 00:00:00 2001 From: Abdul Samad Date: Tue, 20 Aug 2024 15:41:14 +0500 Subject: [PATCH] Enh: example description Co-authored-by: Nicholas Tolley <55253912+ntolley@users.noreply.github.com> Signed-off-by: samadpls --- examples/howto/sbi_hnncore_tutorial.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/howto/sbi_hnncore_tutorial.py b/examples/howto/sbi_hnncore_tutorial.py index fa0c04516..23a9c3ffa 100644 --- a/examples/howto/sbi_hnncore_tutorial.py +++ b/examples/howto/sbi_hnncore_tutorial.py @@ -68,7 +68,7 @@ def set_params(param_values, net=None): ############################################################################### # Here, we generate our parameter grid and run the simulations. We're varying -# the 'weight_pyr' parameter between 10^-4 and 10^-1. +# the 'weight_pyr' parameter between 1e-4 and 1e-1. val = np.linspace(-4, -1, n_simulations) @@ -102,8 +102,8 @@ def extract_dipole_data(sim_results): dipole_data = extract_dipole_data(simulation_results) ############################################################################### -# Now we prepare our data for the SBI algorithm. 'thetas' are our parameters, -# and 'xs' are our observed data (the dipole activity). These will be used by +# Now we prepare our data for the SBI algorithm. `thetas` are our parameters, +# and `xs` are our observed data (the dipole activity). These will be used by # the SBI algorithm to learn the relationship between parameters and # the resulting neural activity.