wrote dynamics for exp-syn

Author: Alexander Ororbia

ngclearn/components/synapses/exponentialSynapse.py

@@ -28,7 +28,7 @@ class ExponentialSynapse(DenseSynapse): ## dynamic exponential synapse cable
     | i_syn - derived total electrical current variable
 
     Args:
-        name: the string name of this cell
+        name: the string name of this synapse
 
         shape: tuple specifying shape of this synaptic cable (usually a 2-tuple
             with number of inputs by number of outputs)
@@ -76,7 +76,7 @@ def __init__(
         self.i_syn = Compartment(postVals) ## electrical current output
         self.g_syn = Compartment(postVals) ## conductance variable
         if is_nonplastic:
-            self.weights.set(self.weights * 0 + 1.)
+            self.weights.set(self.weights.value * 0 + 1.)
 
     @transition(output_compartments=["outputs", "i_syn", "g_syn"])
     @staticmethod
@@ -88,7 +88,7 @@ def advance_state(
         _out = jnp.matmul(s, weights) ## sum all pre-syn spikes at t going into post-neuron)
         dgsyn_dt = _out * g_syn_bar - g_syn/tau_syn
         g_syn = g_syn + dgsyn_dt * dt ## run Euler step to move conductance
-        i_syn = g_syn * (v - syn_rest)
+        i_syn = -g_syn * (v - syn_rest)
         outputs = i_syn #jnp.matmul(inputs, Wdyn * Rscale) + biases
         return outputs, i_syn, g_syn
 

tests/components/synapses/test_exponentialSynapse.py

@@ -6,9 +6,7 @@
 from ngcsimlib.compilers import compile_command, wrap_command
 from numpy.testing import assert_array_equal
 
-from ngcsimlib.compilers.process import Process, transition
-from ngcsimlib.component import Component
-from ngcsimlib.compartment import Compartment
+from ngcsimlib.compilers.process import Process
 from ngcsimlib.context import Context
 import ngclearn.utils.weight_distribution as dist
 
@@ -18,10 +16,17 @@ def test_exponentialSynapse1():
     dkey = random.PRNGKey(1234)
     dkey, *subkeys = random.split(dkey, 6)
     dt = 1.  # ms
+    ## excitatory properties
+    tau_syn = 2.
+    E_rest = 0.
+    ## inhibitory properties
+    #tau_syn = 5.
+    #E_rest = -80.
     # ---- build a single exp-synapse system ----
     with Context(name) as ctx:
         a = ExponentialSynapse(
-            name="a", shape=(1,1), resources_init=dist.constant(value=1.),key=subkeys[0]
+            name="a", shape=(1,1), tau_syn=tau_syn, g_syn_bar=2.4, syn_rest=E_rest, weight_init=dist.constant(value=1.),
+            key=subkeys[0]
         )
 
         advance_process = (Process("advance_proc")
@@ -33,25 +38,24 @@ def test_exponentialSynapse1():
                          >> a.reset)
         ctx.wrap_and_add_command(jit(reset_process.pure), name="reset")
 
-    a.weights.set(jnp.ones((1, 1)))
-    in_pulse = jnp.ones((1, 1)) * 0.425
+    sp_train = jnp.array([1., 0., 1.], dtype=jnp.float32)
+    post_syn_neuron_volt = jnp.ones((1, 1)) * -65. ## post-syn neuron is at rest
 
-    outs_truth = jnp.array([[0.07676563, 0.14312361, 0.16848783]])
-    Wdyn_truth = jnp.array([[0.180625,   0.33676142, 0.39644194]])
+    outs_truth = jnp.array([[156.,  78., 195.]])
 
     outs = []
-    Wdyn = []
     ctx.reset()
     for t in range(3):
+        in_pulse = jnp.expand_dims(sp_train[t], axis=0)
         a.inputs.set(in_pulse)
+        a.v.set(post_syn_neuron_volt)
         ctx.run(t=t * dt, dt=dt)
+        print("g: ",a.g_syn.value)
+        print("i: ", a.i_syn.value)
         outs.append(a.outputs.value)
-        Wdyn.append(a.Wdyn.value)
     outs = jnp.concatenate(outs, axis=1)
-    Wdyn = jnp.concatenate(Wdyn, axis=1)
-    # print(outs)
-    # print(Wdyn)
+    #print(outs)
+
     np.testing.assert_allclose(outs, outs_truth, atol=1e-8)
-    np.testing.assert_allclose(Wdyn, Wdyn_truth, atol=1e-8)
 
-#test_exponentialSynapse1()
+test_exponentialSynapse1()