WIP: Neuron voltage subtraction reset for accuracy

nengo_loihi/emulator/interface.py

@@ -317,18 +317,32 @@ def update(self, rng):
         u2[self.noise.target_u] += noise[self.noise.target_u]
         self._overflow(u2, bits=U_BITS, name="u2")
 
-        self.voltage[:] = self._decay_voltage(self.voltage, u2)
-        # We have not been able to create V overflow on the chip, so we do
-        # not include it here. See github.com/nengo/nengo-loihi/issues/130
-        # self.overflow(self.v, bits=V_BIT, name="V")
-
-        np.clip(self.voltage, self.vmin, self.vmax, out=self.voltage)
-        self.voltage[self.ref_count > 0] = 0
-        # TODO^: don't zero voltage in case neuron is saving overshoot
-
-        self.spiked[:] = (self.voltage > self.vth)
-        self.voltage[self.spiked] = 0
-        self.ref_count[self.spiked] = self.ref[self.spiked]
+        if 0:
+            self.voltage[:] = self._decay_voltage(self.voltage, u2)
+            # We have not been able to create V overflow on the chip, so we do
+            # not include it here. See github.com/nengo/nengo-loihi/issues/130
+            # self.overflow(self.v, bits=V_BIT, name="V")
+
+            np.clip(self.voltage, self.vmin, self.vmax, out=self.voltage)
+            self.voltage[self.ref_count > 0] = 0
+            # TODO^: don't zero voltage in case neuron is saving overshoot
+
+            self.spiked[:] = (self.voltage > self.vth)
+            self.voltage[self.spiked] = 0
+            self.ref_count[self.spiked] = self.ref[self.spiked]
+        else:
+            v = self._decay_voltage(self.voltage, u2)
+            self.voltage[self.ref_count <= 0] = v[self.ref_count <= 0]
+            # We have not been able to create V overflow on the chip, so we do
+            # not include it here. See github.com/nengo/nengo-loihi/issues/130
+            # self.overflow(self.v, bits=V_BIT, name="V")
+
+            np.clip(self.voltage, self.vmin, self.vmax, out=self.voltage)
+
+            self.spiked[:] = (self.voltage > self.vth) & (self.ref_count <= 0)
+            self.voltage[self.spiked] -= self.vth[self.spiked]
+            self.ref_count[self.spiked] = self.ref[self.spiked]
+
         # decrement ref_count
         np.clip(self.ref_count - 1, 0, None, out=self.ref_count)
 

nengo_loihi/neurons.py

@@ -43,7 +43,8 @@ def loihi_lif_rates(neuron_type, x, gain, bias, dt):
     j = neuron_type.current(x, gain, bias) - 1
     out = np.zeros_like(j)
     period = tau_ref + tau_rc * np.log1p(1. / j[j > 0])
-    out[j > 0] = (neuron_type.amplitude / dt) / np.ceil(period / dt)
+    # out[j > 0] = (neuron_type.amplitude / dt) / np.ceil(period / dt)
+    out[j > 0] = neuron_type.amplitude / period
     return out
 
 
@@ -52,7 +53,8 @@ def loihi_spikingrectifiedlinear_rates(neuron_type, x, gain, bias, dt):
 
     out = np.zeros_like(j)
     period = 1. / j[j > 0]
-    out[j > 0] = (neuron_type.amplitude / dt) / np.ceil(period / dt)
+    # out[j > 0] = (neuron_type.amplitude / dt) / np.ceil(period / dt)
+    out[j > 0] = neuron_type.amplitude / period
     return out
 
 

nengo_loihi/tests/test_neurons.py

@@ -29,7 +29,7 @@ def test_loihi_rates(dt, neuron_type, Simulator, plt, allclose):
     x = np.linspace(-0.1, 1, n)
 
     encoders = np.ones((n, 1))
-    max_rates = 400 * np.ones(n)
+    max_rates = 300 * np.ones(n)
     intercepts = 0 * np.ones(n)
     gain, bias = neuron_type.gain_bias(max_rates, intercepts)
     j = x * gain + bias