diff --git a/scripts/run_simulation.jl b/scripts/run_simulation.jl
index b724908..87de992 100644
--- a/scripts/run_simulation.jl
+++ b/scripts/run_simulation.jl
@@ -4,8 +4,8 @@ using .AnimalFurFHN
 include("../src/visualization.jl")
 using .Visualization
 
-N = 256
-tspan = (1.0, 3000.0)
+N = 100
+tspan = (1.0, 1000.0)
 
 sol = AnimalFurFHN.run_simulation(tspan, N)
 
diff --git a/src/solver.jl b/src/solver.jl
index 07df5c5..91bacdd 100644
--- a/src/solver.jl
+++ b/src/solver.jl
@@ -45,7 +45,7 @@ end
 function run_simulation(tspan::Tuple{Float64,Float64}, N::Int)
     # Initial conditions
     # params, y0 = zebra_conditions(N) # tspan of ~3500 enough
-    params, y0 = cheetah_conditions(N)
+    params, y0 = cell_conditions(N)
 
     prob = ODEProblem(fhn!, y0, tspan, params)
     sol = solve(prob, BS3(), saveat=10.0)  # You can try `Rosenbrock23()` too
diff --git a/src/utils.jl b/src/utils.jl
index cf54588..f79072f 100644
--- a/src/utils.jl
+++ b/src/utils.jl
@@ -12,20 +12,23 @@ end
 
 function cheetah_conditions(N)
     # Turing-spot parameters
-    params = FHNParams(N=N, dx=1.0, Du=0.0025, Dv=0.6, ϵ=0.05, a=0.7, b=0.8)
+    #params = FHNParams(N=N, dx=1.0, Du=5e-6, Dv=2e-3, ϵ=0.025, a=0.6, b=0.15)
+    params = FHNParams(N=N, dx=1.0, Du=0.182, Dv=0.5, ϵ=0.05, a=0.2, b=2.0)
 
-    # Approximate Homogenous Steady State (HSS) for a=0.7, b=0.8
-    u_hss = -1.2
-    v_hss = -0.625
+    u0 = 0.05 .* (2 .* rand(N, N) .- 1)  # noise in [-0.05, 0.05]
+    v0 = 0.05 .* (2 .* rand(N, N) .- 1)
 
-    # Small perturbations around the HSS
-    # rand(N,N) generates values between 0 and 1.
-    # (2 .* rand(N,N) .- 1) generates values between -1 and 1 symmetrically around 0.
-    perturbation_amplitude = 0.01
-    u0 = vec(u_hss .+ perturbation_amplitude .* (2 .* rand(N, N) .- 1))
-    v0 = vec(v_hss .+ perturbation_amplitude .* (2 .* rand(N, N) .- 1))
+    return params, vcat(vec(u0), vec(v0))
+end
 
-    return params, vcat(u0, v0)
+function cell_conditions(N)
+    # Turing-spot parameters
+    params = FHNParams(N=N, dx=1.0, Du=5e-5, Dv=6e-3, ϵ=0.05, a=0.6, b=0.1)
+
+    u0 = 0.534522 .* (2 .* rand(N, N) .- 1)  # noise in [-0.05, 0.05]
+    v0 = 0.381802 .* (2 .* rand(N, N) .- 1)
+
+    return params, vcat(vec(u0), vec(v0))
 end
 
 # helper functions for filling cells in specific places of the matrix