Add plastic synapse weight sign test (#973)

Author: clinssen

models/synapses/neuromodulated_stdp_synapse.nestml

@@ -1,17 +1,20 @@
 # neuromodulated_stdp - Synapse model for spike-timing dependent plasticity modulated by a neurotransmitter such as dopamine
 # ##########################################################################################################################
-# 
+#
 # Description
 # +++++++++++
-# 
+#
 # stdp_dopamine_synapse is a connection to create synapses with
 # dopamine-modulated spike-timing dependent plasticity (used as a
 # benchmark model in [1]_, based on [2]_). The dopaminergic signal is a
 # low-pass filtered version of the spike rate of a user-specific pool
 # of neurons. The spikes emitted by the pool of dopamine neurons are
 # delivered to the synapse via the assigned volume transmitter. The
 # dopaminergic dynamics is calculated in the synapse itself.
-# 
+#
+# Note that in this particular STDP synapse model, the weight is not allowed to be negative. In case an inhibitory STDP synapse needs to be modeled, this model (with weight >= 0 at all times) can be connected to a postsynaptic neuron at its appropriate (inhibitory) input port. The sign of the postsynaptic response is thus handled in the postsynaptic neuron. In principle, an STDP synapse model can be defined that allows for negative weights, but in this case, care should be taken to prevent the sign of the weight from changing during learning, as a biological synapse cannot simply switch from one type to another, say, from glutamatergic to GABAergic.
+#
+#
 # References
 # ++++++++++
 #
@@ -28,7 +31,7 @@
 # +++++++++++++++++++
 #
 # This file is part of NEST.
-# 
+#
 # Copyright (C) 2004 The NEST Initiative
 #
 # NEST is free software: you can redistribute it and/or modify
@@ -47,7 +50,7 @@
 #
 model neuromodulated_stdp_synapse:
     state:
-        w real = 1.
+        w real = 1. [[w >= 0]]
         n real = 0.   # Neuromodulator concentration
         c real = 0.   # Eligibility trace
         pre_tr real = 0.
@@ -60,8 +63,8 @@ model neuromodulated_stdp_synapse:
         tau_c ms = 1000 ms    # Time constant of eligibility trace
         tau_n ms = 200 ms   # Time constant of dopaminergic trace
         b real = 0.   # Dopaminergic baseline concentration
-        Wmax real = 200.    # Maximal synaptic weight
-        Wmin real = 0.    # Minimal synaptic weight
+        Wmax real = 200. [[Wmax >= 0]]    # Maximal synaptic weight
+        Wmin real = 0. [[Wmin >= 0]]    # Minimal synaptic weight
         A_plus real = 1.    # Multiplier applied to weight changes caused by pre-before-post spike pairings. If b (dopamine baseline concentration) is zero, then A_plus is simply the multiplier for facilitation (as in the stdp_synapse model). If b is not zero, then A_plus will be the multiplier for facilitation only if n - b is positive, where n is the instantenous dopamine concentration in the volume transmitter. If n - b is negative, A_plus will be the multiplier for depression.
         A_minus real = 1.5    # Multiplier applied to weight changes caused by post-before-pre spike pairings. If b (dopamine baseline concentration) is zero, then A_minus is simply the multiplier for depression (as in the stdp_synapse model). If b is not zero, then A_minus will be the multiplier for depression only if n - b is positive, where n is the instantenous dopamine concentration in the volume transmitter. If n - b is negative, A_minus will be the multiplier for facilitation.
 

models/synapses/noisy_synapse.nestml

@@ -1,17 +1,17 @@
 # noisy_synapse - Static synapse with Gaussian noise
 # ##################################################
-# 
+#
 # Description
 # +++++++++++
-# 
+#
 # Each presynaptic spike is passed to the postsynaptic partner with a weight sampled as :math:`w + A_\text{noise} \mathcal{N}(0, 1)`.
 #
 #
 # 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

models/synapses/static_synapse.nestml

@@ -1,6 +1,6 @@
 # Static synapse
 # ##############
-# 
+#
 # Description
 # +++++++++++
 # A synapse where the synaptic strength (weight) does not evolve with simulated time, but is defined as a (constant) parameter.
@@ -10,7 +10,7 @@
 # +++++++++++++++++++
 #
 # This file is part of NEST.
-# 
+#
 # Copyright (C) 2004 The NEST Initiative
 #
 # NEST is free software: you can redistribute it and/or modify

models/synapses/stdp_nn_pre_centered_synapse.nestml

@@ -1,13 +1,13 @@
 # stdp_nn_pre_centered - Synapse type for spike-timing dependent plasticity, with nearest-neighbour spike pairing
 # ###############################################################################################################
-# 
+#
 # Description
 # +++++++++++
-# 
+#
 # stdp_nn_pre_centered_synapse is a connector to create synapses with spike
 # time dependent plasticity with the presynaptic-centered nearest-neighbour
 # spike pairing scheme, as described in [1]_.
-# 
+#
 # Each presynaptic spike is taken into account in the STDP weight change rule
 # with the nearest preceding postsynaptic one and the nearest succeeding
 # postsynaptic one (instead of pairing with all spikes, like in stdp_synapse).
@@ -16,35 +16,37 @@
 # occurs, it is accounted in the facilitation rule with all preceding
 # presynaptic spikes that were not earlier than the previous postsynaptic
 # spike. For a clear illustration of this scheme see fig. 7B in [2]_.
-# 
+#
 # The pairs exactly coinciding (so that presynaptic_spike == postsynaptic_spike
 # + dendritic_delay), leading to zero delta_t, are discarded. In this case the
 # concerned pre/postsynaptic spike is paired with the second latest preceding
 # post/presynaptic one (for example, pre=={10 ms; 20 ms} and post=={20 ms} will
 # result in a potentiation pair 20-to-10).
-# 
+#
 # The implementation involves two additional variables - presynaptic and
 # postsynaptic traces [2]_. The presynaptic trace decays exponentially over
 # time with the time constant tau_plus, increases by 1 on a pre-spike
 # occurrence, and is reset to 0 on a post-spike occurrence. The postsynaptic
 # trace (implemented on the postsynaptic neuron side) decays with the time
 # constant tau_minus and increases to 1 on a post-spike occurrence.
-# 
+#
 # .. figure:: https://raw.githubusercontent.com/nest/nestml/master/doc/fig/stdp-nearest-neighbour.png
-# 
+#
 #    Figure 7 from Morrison, Diesmann and Gerstner
-# 
+#
 #    Original caption:
-# 
+#
 #    Phenomenological models of synaptic plasticity based on spike timing", Biological Cybernetics 98 (2008). "Examples of nearest neighbor spike pairing schemes for a pre-synaptic neuron j and a postsynaptic neuron i. In each case, the dark gray indicate which pairings contribute toward depression of a synapse, and light gray indicate which pairings contribute toward potentiation. **(a)** Symmetric interpretation: each presynaptic spike is paired with the last postsynaptic spike, and each postsynaptic spike is paired with the last presynaptic spike (Morrison et al. 2007). **(b)** Presynaptic centered interpretation: each presynaptic spike is paired with the last postsynaptic spike and the next postsynaptic spike (Izhikevich and Desai 2003; Burkitt et al. 2004: Model II). **(c)** Reduced symmetric interpretation: as in **(b)** but only for immediate pairings (Burkitt et al. 2004: Model IV, also implemented in hardware by Schemmel et al. 2006)
-# 
-# 
+#
+# Note that in this particular STDP synapse model, the weight is not allowed to be negative. In case an inhibitory STDP synapse needs to be modeled, this model (with weight >= 0 at all times) can be connected to a postsynaptic neuron at its appropriate (inhibitory) input port. The sign of the postsynaptic response is thus handled in the postsynaptic neuron. In principle, an STDP synapse model can be defined that allows for negative weights, but in this case, care should be taken to prevent the sign of the weight from changing during learning, as a biological synapse cannot simply switch from one type to another, say, from glutamatergic to GABAergic.
+#
+#
 # References
 # ++++++++++
-# 
+#
 # .. [1] Izhikevich E. M., Desai N. S. (2003) Relating STDP to BCM,
 #        Neural Comput. 15, 1511--1523
-# 
+#
 # .. [2] Morrison A., Diesmann M., and Gerstner W. (2008) Phenomenological
 #        models of synaptic plasticity based on spike timing,
 #        Biol. Cybern. 98, 459--478
@@ -54,7 +56,7 @@
 # +++++++++++++++++++
 #
 # This file is part of NEST.
-# 
+#
 # Copyright (C) 2004 The NEST Initiative
 #
 # NEST is free software: you can redistribute it and/or modify
@@ -73,7 +75,7 @@
 #
 model stdp_nn_pre_centered_synapse:
     state:
-        w real = 1   # Synaptic weight
+        w real = 1 [[w >= 0]]   # Synaptic weight
         pre_trace real = 0.
         post_trace real = 0.
 
@@ -85,8 +87,8 @@ model stdp_nn_pre_centered_synapse:
         alpha real = 1.
         mu_plus real = 1.
         mu_minus real = 1.
-        Wmax real = 100.
-        Wmin real = 0.
+        Wmin real = 0. [[Wmin >= 0]]
+        Wmax real = 100. [[Wmax >= 0]]
 
     equations:
         # nearest-neighbour trace of presynaptic neuron

models/synapses/stdp_nn_restr_symm_synapse.nestml

@@ -1,13 +1,13 @@
 # Synapse type for spike-timing dependent plasticity with restricted symmetric nearest-neighbour spike pairing scheme
 # ###################################################################################################################
-# 
+#
 # Description
 # +++++++++++
-# 
+#
 # stdp_nn_restr_synapse is a connector to create synapses with spike time
 # dependent plasticity with the restricted symmetric nearest-neighbour spike
 # pairing scheme (fig. 7C in [1]_).
-# 
+#
 # When a presynaptic spike occurs, it is taken into account in the depression
 # part of the STDP weight change rule with the nearest preceding postsynaptic
 # one, but only if the latter occured not earlier than the previous presynaptic
@@ -20,23 +20,26 @@
 # concerned pre/postsynaptic spike is paired with the second latest preceding
 # post/presynaptic one (for example, pre=={10 ms; 20 ms} and post=={20 ms} will
 # result in a potentiation pair 20-to-10).
-# 
+#
 # The implementation relies on an additional variable - the postsynaptic
 # eligibility trace [1]_ (implemented on the postsynaptic neuron side). It
 # decays exponentially with the time constant tau_minus and increases to 1 on
 # a post-spike occurrence (instead of increasing by 1 as in stdp_synapse).
-# 
+#
 # .. figure:: https://raw.githubusercontent.com/nest/nestml/master/doc/fig/stdp-nearest-neighbour.png
-# 
+#
 #    Figure 7 from Morrison, Diesmann and Gerstner
-# 
+#
 #    Original caption:
-# 
+#
 #    Phenomenological models of synaptic plasticity based on spike timing", Biological Cybernetics 98 (2008). "Examples of nearest neighbor spike pairing schemes for a pre-synaptic neuron j and a postsynaptic neuron i. In each case, the dark gray indicate which pairings contribute toward depression of a synapse, and light gray indicate which pairings contribute toward potentiation. **(a)** Symmetric interpretation: each presynaptic spike is paired with the last postsynaptic spike, and each postsynaptic spike is paired with the last presynaptic spike (Morrison et al. 2007). **(b)** Presynaptic centered interpretation: each presynaptic spike is paired with the last postsynaptic spike and the next postsynaptic spike (Izhikevich and Desai 2003; Burkitt et al. 2004: Model II). **(c)** Reduced symmetric interpretation: as in **(b)** but only for immediate pairings (Burkitt et al. 2004: Model IV, also implemented in hardware by Schemmel et al. 2006)
-# 
+#
+# Note that in this particular STDP synapse model, the weight is not allowed to be negative. In case an inhibitory STDP synapse needs to be modeled, this model (with weight >= 0 at all times) can be connected to a postsynaptic neuron at its appropriate (inhibitory) input port. The sign of the postsynaptic response is thus handled in the postsynaptic neuron. In principle, an STDP synapse model can be defined that allows for negative weights, but in this case, care should be taken to prevent the sign of the weight from changing during learning, as a biological synapse cannot simply switch from one type to another, say, from glutamatergic to GABAergic.
+#
+#
 # References
 # ++++++++++
-# 
+#
 # .. [1] Morrison A., Diesmann M., and Gerstner W. (2008) Phenomenological
 #        models of synaptic plasticity based on spike timing,
 #        Biol. Cybern. 98, 459--478
@@ -46,7 +49,7 @@
 # +++++++++++++++++++
 #
 # This file is part of NEST.
-# 
+#
 # Copyright (C) 2004 The NEST Initiative
 #
 # NEST is free software: you can redistribute it and/or modify
@@ -65,7 +68,7 @@
 #
 model stdp_nn_restr_symm_synapse:
     state:
-        w real = 1.   # Synaptic weight
+        w real = 1. [[w >= 0]]   # Synaptic weight
         pre_trace real = 0.
         post_trace real = 0.
         pre_handled boolean = True
@@ -78,8 +81,8 @@ model stdp_nn_restr_symm_synapse:
         alpha real = 1.
         mu_plus real = 1.
         mu_minus real = 1.
-        Wmax real = 100.
-        Wmin real = 0.
+        Wmin real = 0. [[Wmin >= 0]]
+        Wmax real = 100. [[Wmax >= 0]]
 
     equations:
         # nearest-neighbour trace of presynaptic neuron

models/synapses/stdp_nn_symm_synapse.nestml

@@ -1,47 +1,50 @@
 # Synapse type for spike-timing dependent plasticity with symmetric nearest-neighbour spike pairing scheme
 # ########################################################################################################
-# 
+#
 # Description
 # +++++++++++
-# 
+#
 # stdp_nn_symm_synapse is a connector to create synapses with spike time
 # dependent plasticity with the symmetric nearest-neighbour spike pairing
 # scheme [1]_.
-# 
+#
 # When a presynaptic spike occurs, it is taken into account in the depression
 # part of the STDP weight change rule with the nearest preceding postsynaptic
 # one, and when a postsynaptic spike occurs, it is accounted in the
 # facilitation rule with the nearest preceding presynaptic one (instead of
 # pairing with all spikes, like in stdp_synapse). For a clear illustration of
 # this scheme see fig. 7A in [2]_.
-# 
+#
 # The pairs exactly coinciding (so that presynaptic_spike == postsynaptic_spike
 # + dendritic_delay), leading to zero delta_t, are discarded. In this case the
 # concerned pre/postsynaptic spike is paired with the second latest preceding
 # post/presynaptic one (for example, pre=={10 ms; 20 ms} and post=={20 ms} will
 # result in a potentiation pair 20-to-10).
-# 
+#
 # The implementation involves two additional variables - presynaptic and
 # postsynaptic traces [2]_. The presynaptic trace decays exponentially over
 # time with the time constant tau_plus and increases to 1 on a pre-spike
 # occurrence. The postsynaptic trace (implemented on the postsynaptic neuron
 # side) decays with the time constant tau_minus and increases to 1 on a
 # post-spike occurrence.
-# 
+#
 # .. figure:: https://raw.githubusercontent.com/nest/nestml/master/doc/fig/stdp-nearest-neighbour.png
-# 
+#
 #    Figure 7 from Morrison, Diesmann and Gerstner
-# 
+#
 #    Original caption:
-# 
+#
 #    Phenomenological models of synaptic plasticity based on spike timing", Biological Cybernetics 98 (2008). "Examples of nearest neighbor spike pairing schemes for a pre-synaptic neuron j and a postsynaptic neuron i. In each case, the dark gray indicate which pairings contribute toward depression of a synapse, and light gray indicate which pairings contribute toward potentiation. **(a)** Symmetric interpretation: each presynaptic spike is paired with the last postsynaptic spike, and each postsynaptic spike is paired with the last presynaptic spike (Morrison et al. 2007). **(b)** Presynaptic centered interpretation: each presynaptic spike is paired with the last postsynaptic spike and the next postsynaptic spike (Izhikevich and Desai 2003; Burkitt et al. 2004: Model II). **(c)** Reduced symmetric interpretation: as in **(b)** but only for immediate pairings (Burkitt et al. 2004: Model IV, also implemented in hardware by Schemmel et al. 2006)
-# 
+#
+# Note that in this particular STDP synapse model, the weight is not allowed to be negative. In case an inhibitory STDP synapse needs to be modeled, this model (with weight >= 0 at all times) can be connected to a postsynaptic neuron at its appropriate (inhibitory) input port. The sign of the postsynaptic response is thus handled in the postsynaptic neuron. In principle, an STDP synapse model can be defined that allows for negative weights, but in this case, care should be taken to prevent the sign of the weight from changing during learning, as a biological synapse cannot simply switch from one type to another, say, from glutamatergic to GABAergic.
+#
+#
 # References
 # ++++++++++
-# 
+#
 # .. [1] Morrison A., Aertsen A., Diesmann M. (2007) Spike-timing dependent
 #        plasticity in balanced random networks, Neural Comput. 19:1437--1467
-# 
+#
 # .. [2] Morrison A., Diesmann M., and Gerstner W. (2008) Phenomenological
 #        models of synaptic plasticity based on spike timing,
 #        Biol. Cybern. 98, 459--478
@@ -51,7 +54,7 @@
 # +++++++++++++++++++
 #
 # This file is part of NEST.
-# 
+#
 # Copyright (C) 2004 The NEST Initiative
 #
 # NEST is free software: you can redistribute it and/or modify
@@ -70,7 +73,7 @@
 #
 model stdp_nn_symm_synapse:
     state:
-        w real = 1    # Synaptic weight
+        w real = 1 [[w >= 0]]    # Synaptic weight
         pre_trace real = 0.
         post_trace real = 0.
 
@@ -82,8 +85,8 @@ model stdp_nn_symm_synapse:
         alpha real = 1.
         mu_plus real = 1.
         mu_minus real = 1.
-        Wmax real = 100.
-        Wmin real = 0.
+        Wmax real = 100. [[Wmax >= 0]]
+        Wmin real = 0. [[Wmin >= 0]]
 
     equations:
         # nearest-neighbour trace of presynaptic neuron