diff --git a/brainpy/_src/dnn/linear.py b/brainpy/_src/dnn/linear.py
index 2570835f..a1d31e08 100644
--- a/brainpy/_src/dnn/linear.py
+++ b/brainpy/_src/dnn/linear.py
@@ -980,7 +980,15 @@ def __init__(
     self.sharding = sharding
 
 
-class JitFPHomoLinear(Layer):
+class JitFPLinear(Layer):
+  def get_connect_matrix(self):
+    return bm.jitconn.get_conn_matrix(self.prob, self.seed,
+                                      shape=(self.num_out, self.num_in),
+                                      transpose=self.transpose,
+                                      outdim_parallel=not self.atomic)
+
+
+class JitFPHomoLinear(JitFPLinear):
   r"""Synaptic matrix multiplication with the just-in-time connectivity.
 
   It performs the computation of:
@@ -1058,14 +1066,8 @@ def _batch_mv(self, x):
                                    transpose=self.transpose,
                                    outdim_parallel=not self.atomic)
 
-  def get_connect_matrix(self):
-    return bm.jitconn.get_connect_matrix(self.prob, self.seed,
-                                         shape=(self.num_out, self.num_in),
-                                         transpose=self.transpose,
-                                         outdim_parallel=not self.atomic)
-
 
-class JitFPUniformLinear(Layer):
+class JitFPUniformLinear(JitFPLinear):
   r"""Synaptic matrix multiplication with the just-in-time connectivity.
 
   It performs the computation of:
@@ -1144,14 +1146,8 @@ def _batch_mv(self, x):
                                       transpose=self.transpose,
                                       outdim_parallel=not self.atomic)
 
-  def get_connect_matrix(self):
-    return bm.jitconn.get_connect_matrix(self.prob, self.seed,
-                                         shape=(self.num_out, self.num_in),
-                                         transpose=self.transpose,
-                                         outdim_parallel=not self.atomic)
-
 
-class JitFPNormalLinear(Layer):
+class JitFPNormalLinear(JitFPLinear):
   r"""Synaptic matrix multiplication with the just-in-time connectivity.
 
   It performs the computation of:
@@ -1230,14 +1226,8 @@ def _batch_mv(self, x):
                                      transpose=self.transpose,
                                      outdim_parallel=not self.atomic)
 
-  def get_connect_matrix(self):
-    return bm.jitconn.get_connect_matrix(self.prob, self.seed,
-                                         shape=(self.num_out, self.num_in),
-                                         transpose=self.transpose,
-                                         outdim_parallel=not self.atomic)
-
 
-class EventJitFPHomoLinear(Layer):
+class EventJitFPHomoLinear(JitFPLinear):
   r"""Synaptic matrix multiplication with the just-in-time connectivity.
 
   It performs the computation of:
@@ -1315,14 +1305,8 @@ def _batch_mv(self, x):
                                          transpose=self.transpose,
                                          outdim_parallel=not self.atomic)
 
-  def get_connect_matrix(self):
-    return bm.jitconn.get_connect_matrix(self.prob, self.seed,
-                                         shape=(self.num_out, self.num_in),
-                                         transpose=self.transpose,
-                                         outdim_parallel=not self.atomic)
 
-
-class EventJitFPUniformLinear(Layer):
+class EventJitFPUniformLinear(JitFPLinear):
   r"""Synaptic matrix multiplication with the just-in-time connectivity.
 
   It performs the computation of:
@@ -1401,14 +1385,8 @@ def _batch_mv(self, x):
                                             transpose=self.transpose,
                                             outdim_parallel=not self.atomic)
 
-  def get_connect_matrix(self):
-    return bm.jitconn.get_connect_matrix(self.prob, self.seed,
-                                         shape=(self.num_out, self.num_in),
-                                         transpose=self.transpose,
-                                         outdim_parallel=not self.atomic)
-
 
-class EventJitFPNormalLinear(Layer):
+class EventJitFPNormalLinear(JitFPLinear):
   r"""Synaptic matrix multiplication with the just-in-time connectivity.
 
   It performs the computation of:
@@ -1486,9 +1464,3 @@ def _batch_mv(self, x):
                                            shape=(self.num_out, self.num_in),
                                            transpose=self.transpose,
                                            outdim_parallel=not self.atomic)
-
-  def get_connect_matrix(self):
-    return bm.jitconn.get_connect_matrix(self.prob, self.seed,
-                                         shape=(self.num_out, self.num_in),
-                                         transpose=self.transpose,
-                                         outdim_parallel=not self.atomic)
diff --git a/brainpy/_src/math/jitconn/matvec.py b/brainpy/_src/math/jitconn/matvec.py
index ad168133..8a7ba398 100644
--- a/brainpy/_src/math/jitconn/matvec.py
+++ b/brainpy/_src/math/jitconn/matvec.py
@@ -20,7 +20,7 @@
   'mv_prob_homo',
   'mv_prob_uniform',
   'mv_prob_normal',
-  'get_connect_matrix',
+  'get_conn_matrix',
 ]
 
 
@@ -258,7 +258,7 @@ def mv_prob_normal(
                             transpose=transpose, outdim_parallel=outdim_parallel)[0]
 
 
-def get_connect_matrix(
+def get_conn_matrix(
     conn_prob: float,
     seed: Optional[int] = None,
     *,
diff --git a/brainpy/_src/math/jitconn/tests/test_get_connect_matrix.py b/brainpy/_src/math/jitconn/tests/test_get_conn_matrix.py
similarity index 87%
rename from brainpy/_src/math/jitconn/tests/test_get_connect_matrix.py
rename to brainpy/_src/math/jitconn/tests/test_get_conn_matrix.py
index 4b948fc2..a58be6e8 100644
--- a/brainpy/_src/math/jitconn/tests/test_get_connect_matrix.py
+++ b/brainpy/_src/math/jitconn/tests/test_get_conn_matrix.py
@@ -32,10 +32,10 @@ def __init__(self, *args, platform='cpu', **kwargs):
     shape=shapes,
     prob=[0.1],
   )
-  def test_get_connect_matrix(self, transpose, outdim_parallel, shape, prob):
+  def test_get_conn_matrix(self, transpose, outdim_parallel, shape, prob):
     print(
       f'test_get_connect_matrix: transpose={transpose}, outdim_parallel={outdim_parallel}, shape={shape}, prob={prob}')
-    conn = bm.jitconn.get_connect_matrix(prob, shape=shape, transpose=transpose, outdim_parallel=outdim_parallel)
+    conn = bm.jitconn.get_conn_matrix(prob, shape=shape, transpose=transpose, outdim_parallel=outdim_parallel)
     shape = (shape[1], shape[0]) if transpose else shape
     assert conn.shape == shape
     assert conn.dtype == jnp.bool_
diff --git a/brainpy/math/jitconn.py b/brainpy/math/jitconn.py
index 441b19f2..e1c4eafb 100644
--- a/brainpy/math/jitconn.py
+++ b/brainpy/math/jitconn.py
@@ -7,6 +7,6 @@
   mv_prob_uniform as mv_prob_uniform,
   mv_prob_normal as mv_prob_normal,
 
-  get_connect_matrix as get_connect_matrix,
+  get_conn_matrix as get_conn_matrix,
 )