Add Bouhadjar sequence learning network tutorial

doc/tutorials/sequence_learning/iaf_psc_exp_nonlineardendrite_neuron.nestml

@@ -0,0 +1,141 @@
+"""
+iaf_psc_exp_nonlineardendrite_neuron
+####################################
+
+
+Copyright statement
++++++++++++++++++++
+
+This file is part of NEST.
+
+Copyright (C) 2004 The NEST Initiative
+
+NEST is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 2 of the License, or
+(at your option) any later version.
+
+NEST is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with NEST.  If not, see <http://www.gnu.org/licenses/>.
+"""
+
+model iaf_psc_exp_nonlineardendrite_neuron:
+
+    state:
+        V_m mV = 0 mV         # membrane potential in mV
+        dAP_trace pA = 0 pA             # dAP trace
+        active_dendrite boolean = false
+        active_dendrite_readout real = 0.    # an extra readout is included so the state can be recorded in NEST; see https://github.com/nest/nestml/issues/986
+        dAP_counts integer = 0
+        ref_counts integer = 0
+        I_dend pA = 0 pA
+        I_dend$ pA/ms = 0 pA/ms
+
+    equations:
+        # exponential shaped postsynaptic current kernel
+        kernel I_kernel1 = exp(-1/tau_syn1*t)
+
+        # alpha shaped postsynaptic current kernel
+        I_dend' = I_dend$ - I_dend / tau_syn2
+        I_dend$' = -I_dend$ / tau_syn2
+
+        # exponential shaped postsynaptic current kernel
+        kernel I_kernel3 = exp(-1/tau_syn3*t)
+
+        # diff. eq. for membrane potential
+        inline I_syn pA = convolve(I_kernel1, I_1) * pA - convolve(I_kernel3, I_3) * pA + I_e
+        V_m' = -(V_m - E_L)/tau_m + (I_syn + I_dend) / C_m
+
+        # diff. eq. for dAP trace
+        dAP_trace' = -evolve_dAP_trace * dAP_trace / tau_h
+
+    parameters:
+        C_m pF = 250 pF                # capacitance of the membrane
+        tau_m ms = 20 ms               # membrane time constant
+        tau_syn1 ms = 10 ms            # time constant of synaptic current, port 1
+        tau_syn2 ms = 10 ms            # time constant of synaptic current, port 2
+        tau_syn3 ms = 10 ms            # time constant of synaptic current, port 3
+        tau_h ms = 400 ms              # time constant of the dAP trace
+        V_th mV = 25 mV                # spike threshold
+        V_reset mV = 0 mV              # reset voltage
+        E_L mV = 0mV                   # resting potential.
+        evolve_dAP_trace real = 1      # set to 0 to stop integrating dAP_trace
+        I_e pA = 0pA                   # external current.
+
+        # dendritic action potential
+        theta_dAP pA = 60 pA                            # current threshold for a dendritic action potential
+        I_p pA = 250 pA                                 # current clamp value for I_dAP during a dendritic action potential
+        tau_dAP ms = 60 ms                              # time window over which the dendritic current clamp is active
+        dAP_timeout_ticks integer = steps(tau_dAP)
+
+        # refractory parameters
+        t_ref ms = 10 ms                                # refractory period
+        ref_timeout_ticks integer = steps(t_ref)
+
+        I_dend_incr pA/ms = pA * exp(1) / tau_syn2
+
+
+    input:
+        I_1 <- spike
+        I_2 <- spike
+        I_3 <- spike
+
+    output:
+        spike
+
+    onReceive(I_2):
+        I_dend$ += I_2 * s * I_dend_incr
+
+    update:
+        # solve ODEs
+        integrate_odes()
+
+        # current-threshold, emit a dendritic action potential
+        if I_dend > theta_dAP or active_dendrite:
+            if dAP_counts == 0:
+
+                if active_dendrite == false:
+                    # starting dAP
+                    dAP_trace += 1 pA
+                    active_dendrite = true
+                    active_dendrite_readout = 1.
+                    I_dend = I_p
+                    dAP_counts = dAP_timeout_ticks
+                else:
+                    # ending dAP
+                    I_dend = 0 pA
+                    active_dendrite = false
+                    active_dendrite_readout = 0.
+
+                    # the following assignment to I_dend$ reproduces a bug in the original implementation. It is included here to replicate the results of the original model
+                    c1 real = -resolution() * exp(-resolution() / tau_syn2) / tau_syn2**2
+                    c2 real = (-resolution() + tau_syn2) * exp(-resolution() / tau_syn2) / tau_syn2
+                    I_dend$ = I_p * c1 / (1 - c2) / ms
+
+            else:
+                dAP_counts -= 1
+                I_dend = I_p
+
+        # threshold crossing and refractoriness
+        if ref_counts == 0:
+            if V_m > V_th:
+                emit_spike()
+                ref_counts = ref_timeout_ticks
+                V_m = V_reset
+                dAP_counts = 0
+                I_dend = 0 pA
+                active_dendrite = false
+                active_dendrite_readout = 0.
+        else:
+            ref_counts -= 1
+            V_m = V_reset
+            active_dendrite = false
+            active_dendrite_readout = 0.
+            dAP_counts = 0
+            I_dend = 0 pA
+

doc/tutorials/tutorials_list.rst

@@ -21,6 +21,10 @@ Creating neuron models
 
   Create a model that emits spikes according to an inhomogeneous Poisson distribution.
 
+* :doc:`Sequence learning network </tutorials/sequence_learning/sequence_learning>`
+
+  A network learns to predict and autonomously replay sequences of items.
+
 
 Creating synapse models
 -----------------------

models/synapses/stdsp_synapse.nestml

@@ -0,0 +1,82 @@
+"""
+stdsp_synapse - Synapse model for spike-timing dependent plasticity with postsynaptic third-factor modulation
+#############################################################################################################
+
+Description
++++++++++++
+
+References
+++++++++++
+
+"""
+model stdsp_synapse:
+    state:
+        permanence real = 1.
+        t_last_pre_spike ms = 0 ms
+        pre_trace real = 0.
+        w real = 100.    # dummy synaptic weight
+
+    parameters:
+        d ms = 1 ms    # Synaptic transmission delay
+
+        tau_pre_trace ms = 80 ms
+        lambda_h real = 1.
+        zt pA = 1 pA
+        lambda_plus real = .01
+        lambda_minus real = 1.
+        Wmax real = 100.
+        permanence_max real = 100.
+        permanence_min real = 0.
+        dt_min ms = 4 ms
+        dt_max ms = 100 ms
+
+        permanence_threshold real = 10.
+
+        Wmin real = 0.
+
+    equations:
+        pre_trace' = -pre_trace / tau_pre_trace
+
+    input:
+        pre_spikes <- spike
+        post_spikes <- spike
+        dAP_trace pA <- continuous
+
+    output:
+        spike(weight real, delay ms)
+
+    onReceive(post_spikes):
+        time_since_last_spike ms = t - t_last_pre_spike
+
+        if time_since_last_spike < dt_max and time_since_last_spike > dt_min:
+            # facilitation
+            norm_perm real = permanence / permanence_max + lambda_plus * pre_trace
+            permanence = min(norm_perm * permanence_max, permanence_max)
+
+            # homeostasis
+            permanence += lambda_h * (zt - dAP_trace) / pA * permanence_max
+            permanence = min(permanence, permanence_max)
+            permanence = max(permanence, permanence_min)
+
+    onReceive(pre_spikes):
+        t_last_pre_spike = t
+
+        pre_trace += 1.
+
+        # depress synapse
+        permanence -= lambda_minus * permanence_max
+        permanence = max(permanence, permanence_min)
+
+        if permanence > permanence_threshold:
+            # set a dummy "weight" so the weight can be recorded
+            w = Wmax
+
+            # deliver spike to postsynaptic partner
+            emit_spike(w, d)
+        else:
+            # set a dummy "weight" so the weight can be recorded
+            w = 0.
+
+    update:
+        # solve ODEs
+        integrate_odes()