diff --git a/tests/test_grad.py b/tests/test_grad.py
new file mode 100644
index 000000000..a76b4a336
--- /dev/null
+++ b/tests/test_grad.py
@@ -0,0 +1,41 @@
+import jax
+
+jax.config.update("jax_enable_x64", True)
+jax.config.update("jax_platform_name", "cpu")
+
+import jax.numpy as jnp
+from jax import value_and_grad
+
+import jaxley as jx
+from jaxley.channels import HH
+
+
+def test_grad_against_finite_diff_initial_state():
+    comp = jx.Compartment()
+    comp.insert(HH())
+    comp.record()
+    comp.stimulate(jx.step_current(0.1, 0.2, 0.1, 0.025, 5.0))
+
+    def simulate():
+        return jnp.sum(jx.integrate(comp))
+
+    # Finite differences.
+    comp.set("HH_m", 0.2)
+    r1 = simulate()
+    comp.set("HH_m", 0.21)
+    r2 = simulate()
+    finitediff_grad = (r2 - r1) / 0.01
+
+    # Autodiff gradient.
+    def simulate(params):
+        return jnp.sum(jx.integrate(comp, params=params))
+
+    grad_fn = value_and_grad(simulate)
+    comp.set("HH_m", 0.2)
+    comp.make_trainable("HH_m")
+    params = comp.get_parameters()
+    v, g = grad_fn(params)
+    autodiff_grad = g[0]["HH_m"]
+
+    # Less than 5% error on the gradient difference.
+    assert jnp.abs(autodiff_grad - finitediff_grad) / autodiff_grad < 0.05