diff --git a/brainpy/_src/dyn/projections/__init__.py b/brainpy/_src/dyn/projections/__init__.py
index 3efded3a6..8a7040824 100644
--- a/brainpy/_src/dyn/projections/__init__.py
+++ b/brainpy/_src/dyn/projections/__init__.py
@@ -2,3 +2,4 @@
 from .aligns import *
 from .conn import *
 from .others import *
+from .inputs import *
diff --git a/brainpy/_src/dyn/projections/inputs.py b/brainpy/_src/dyn/projections/inputs.py
new file mode 100644
index 000000000..a1b154f63
--- /dev/null
+++ b/brainpy/_src/dyn/projections/inputs.py
@@ -0,0 +1,96 @@
+from typing import Optional, Any
+
+from brainpy import math as bm
+from brainpy._src.dynsys import Dynamic
+from brainpy._src.mixin import SupportAutoDelay
+from brainpy.types import Shape
+
+__all__ = [
+    'InputVar',
+]
+
+
+class InputVar(Dynamic, SupportAutoDelay):
+    """Define an input variable.
+
+    Example::
+
+        import brainpy as bp
+
+
+        class Exponential(bp.Projection):
+            def __init__(self, pre, post, prob, g_max, tau, E=0.):
+                super().__init__()
+                self.proj = bp.dyn.ProjAlignPostMg2(
+                    pre=pre,
+                    delay=None,
+                    comm=bp.dnn.EventCSRLinear(bp.conn.FixedProb(prob, pre=pre.num, post=post.num), g_max),
+                    syn=bp.dyn.Expon.desc(post.num, tau=tau),
+                    out=bp.dyn.COBA.desc(E=E),
+                    post=post,
+                )
+
+
+        class EINet(bp.DynSysGroup):
+            def __init__(self, num_exc, num_inh, method='exp_auto'):
+                super(EINet, self).__init__()
+
+                # neurons
+                pars = dict(V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,
+                            V_initializer=bp.init.Normal(-55., 2.), method=method)
+                self.E = bp.dyn.LifRef(num_exc, **pars)
+                self.I = bp.dyn.LifRef(num_inh, **pars)
+
+                # synapses
+                w_e = 0.6  # excitatory synaptic weight
+                w_i = 6.7  # inhibitory synaptic weight
+
+                # Neurons connect to each other randomly with a connection probability of 2%
+                self.E2E = Exponential(self.E, self.E, 0.02, g_max=w_e, tau=5., E=0.)
+                self.E2I = Exponential(self.E, self.I, 0.02, g_max=w_e, tau=5., E=0.)
+                self.I2E = Exponential(self.I, self.E, 0.02, g_max=w_i, tau=10., E=-80.)
+                self.I2I = Exponential(self.I, self.I, 0.02, g_max=w_i, tau=10., E=-80.)
+
+                # define input variables given to E/I populations
+                self.Ein = bp.dyn.InputVar(self.E.varshape)
+                self.Iin = bp.dyn.InputVar(self.I.varshape)
+                self.E.add_inp_fun('', self.Ein)
+                self.I.add_inp_fun('', self.Iin)
+
+
+        net = EINet(3200, 800, method='exp_auto')  # "method": the numerical integrator method
+        runner = bp.DSRunner(net, monitors=['E.spike', 'I.spike'], inputs=[('Ein.input', 20.), ('Iin.input', 20.)])
+        runner.run(100.)
+
+        # visualization
+        bp.visualize.raster_plot(runner.mon.ts, runner.mon['E.spike'],
+                                 title='Spikes of Excitatory Neurons', show=True)
+        bp.visualize.raster_plot(runner.mon.ts, runner.mon['I.spike'],
+                                 title='Spikes of Inhibitory Neurons', show=True)
+
+
+    """
+    def __init__(
+            self,
+            size: Shape,
+            keep_size: bool = False,
+            sharding: Optional[Any] = None,
+            name: Optional[str] = None,
+            mode: Optional[bm.Mode] = None,
+            method: str = 'exp_auto'
+    ):
+        super().__init__(size=size, keep_size=keep_size, sharding=sharding, name=name, mode=mode, method=method)
+
+        self.reset_state(self.mode)
+
+    def reset_state(self, batch_or_mode=None):
+        self.input = self.init_variable(bm.zeros, batch_or_mode)
+
+    def update(self, *args, **kwargs):
+        return self.input.value
+
+    def return_info(self):
+        return self.input
+
+    def clear_input(self, *args, **kwargs):
+        self.reset_state(self.mode)
diff --git a/brainpy/_src/dynsys.py b/brainpy/_src/dynsys.py
index 5e89ed7a9..cc825fa26 100644
--- a/brainpy/_src/dynsys.py
+++ b/brainpy/_src/dynsys.py
@@ -95,7 +95,7 @@ class DynamicalSystem(bm.BrainPyObject, DelayRegister, SupportInputProj):
   name : optional, str
     The name of the dynamical system.
   mode: optional, Mode
-    The model computation mode. It should be instance of :py:class:`~.Mode`.
+    The model computation mode. It should be an instance of :py:class:`~.Mode`.
   """
 
   supported_modes: Optional[Sequence[bm.Mode]] = None
@@ -610,10 +610,6 @@ def reset_state(self, *args, **kwargs):
     else:
       raise ValueError('Do not implement the reset_state() function.')
 
-  def clear_input(self, *args, **kwargs):
-    """Empty function of clearing inputs."""
-    pass
-
 
 class Dynamic(DynamicalSystem):
   """Base class to model dynamics.
diff --git a/brainpy/dyn/projections.py b/brainpy/dyn/projections.py
index 2954b7871..b2f4c5304 100644
--- a/brainpy/dyn/projections.py
+++ b/brainpy/dyn/projections.py
@@ -19,4 +19,6 @@
   PoissonInput as PoissonInput,
 )
 
-
+from brainpy._src.dyn.projections.inputs import (
+  InputVar,
+)
diff --git a/docs/apis/brainpy.dyn.projections.rst b/docs/apis/brainpy.dyn.projections.rst
index b1dcb1219..c1f8c1070 100644
--- a/docs/apis/brainpy.dyn.projections.rst
+++ b/docs/apis/brainpy.dyn.projections.rst
@@ -6,12 +6,14 @@ Synaptic Projections
 
 
 
+Reduced Projections
+-------------------
+
 .. autosummary::
    :toctree: generated/
    :nosignatures:
    :template: classtemplate.rst
 
-   VanillaProj
    ProjAlignPostMg1
    ProjAlignPostMg2
    ProjAlignPost1
@@ -20,5 +22,30 @@ Synaptic Projections
    ProjAlignPreMg2
    ProjAlignPre1
    ProjAlignPre2
+
+
+
+Projections
+-----------
+
+.. autosummary::
+   :toctree: generated/
+   :nosignatures:
+   :template: classtemplate.rst
+
+   VanillaProj
    SynConn
+
+
+
+Inputs
+------
+
+.. autosummary::
+   :toctree: generated/
+   :nosignatures:
+   :template: classtemplate.rst
+
+
    PoissonInput
+   InputVar