Example of the tsodyks2_synapse in NEST

This synapse model implements synaptic short-term depression and short-term f according to [1] and [2]. It solves Eq (2) from [1] and modulates U according
This connection merely scales the synaptic weight, based on the spike history parameters of the kinetic model. Thus, it is suitable for all types of synapt that is current or conductance based.
The parameter A_se from the publications is represented by the synaptic weight. The variable x in the synapse properties is the factor that scales the synaptic weight.
Parameters: The following parameters can be set in the status dictionary: U double - probability of release increment (U1) [0,1], default=0. u double - Maximum probability of release (U_se) [0,1], default=0. x double - current scaling factor of the weight, default=U tau_rec double - time constant for depression in ms, default=800 ms tau_fac double - time constant for facilitation in ms, default=0 (off)
Notes:
Under identical conditions, the tsodyks2_synapse produces slightly lower peak amplitudes than the tsodyks_synapse. However, the qualitative behavior is identical.
This compares the two synapse models.
References: [1] Tsodyks, M. V., & Markram, H. (1997). The neural code between neocortical depends on neurotransmitter release probability. PNAS, 94(2), 719-23. [2] Fuhrmann, G., Segev, I., Markram, H., & Tsodyks, M. V. (2002). Coding of information by activity-dependent synapses. Journal of neurophysiology, 8 [3] Maass, W., & Markram, H. (2002). Synapses as dynamic memory buffers. Neur
import nest
import nest.voltage_trace

nest.ResetKernel()

Parameter set for depression
dep_params={"U":0.67, "u":0.67, 'x':1.0, "tau_rec":450.0, "tau_fac":0.0, "weight":250.}

Parameter set for facilitation
fac_params={"U":0.1, "u":0.1, 'x':1.0, "tau_fac":1000.,"tau_rec":100.,"weight":250.}

Now we assign the parameter set to the synapse models.
t1_params=fac_params       # for tsodyks_synapse
t2_params=t1_params.copy() # for tsodyks2_synapse

nest.SetDefaults("tsodyks2_synapse",t1_params)
nest.SetDefaults("tsodyks_synapse",t2_params)
nest.SetDefaults("iaf_psc_exp",{"tau_syn_ex": 3.})

Create three neurons.
neuron = nest.Create("iaf_psc_exp",3)

Neuron one produces spikes. Neurons 2 and 3 receive the spikes via the two synapse models.
nest.Connect([neuron[0]],[neuron[1]],syn_spec="tsodyks_synapse")
nest.Connect([neuron[0]],[neuron[2]],syn_spec="tsodyks2_synapse")

Now create two voltmeters to record the responses.
voltmeter = nest.Create("voltmeter",2)
nest.SetStatus(voltmeter, {"withgid": True, "withtime": True})

Connect the voltmeters to the neurons.
nest.Connect([voltmeter[0]], [neuron[1]])
nest.Connect([voltmeter[1]], [neuron[2]])

Now simulate the standard STP protocol: a burst of spikes, followed by a pause and a recovery response.
nest.SetStatus([neuron[0]], "I_e", 376.0)
nest.Simulate(500.0)
nest.SetStatus([neuron[0]], "I_e", 0.0)
nest.Simulate(500.0)
nest.SetStatus([neuron[0]], "I_e", 376.0)
nest.Simulate(500.0)

Finally, generate voltage traces. Both are shown in the same plot and should be almost completely overlapping.

#nest.voltage_trace.show()
evaluate_tsodyks2_synapse
                                # -*- coding: utf-8 -*-
#
# evaluate_tsodyks2_synapse.py
#
# 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/>.

'''
Example of the tsodyks2_synapse in NEST
---------------------------------------
This synapse model implements synaptic short-term depression and short-term f
according to [1] and [2]. It solves Eq (2) from [1] and modulates U according

This connection merely scales the synaptic weight, based on the spike history
parameters of the kinetic model. Thus, it is suitable for all types of synapt
that is current or conductance based.

The parameter A_se from the publications is represented by the
synaptic weight. The variable x in the synapse properties is the
factor that scales the synaptic weight.

Parameters:
The following parameters can be set in the status dictionary:
U          double - probability of release increment (U1) [0,1], default=0.
u          double - Maximum probability of release (U_se) [0,1], default=0.
x          double - current scaling factor of the weight, default=U
tau_rec    double - time constant for depression in ms, default=800 ms
tau_fac    double - time constant for facilitation in ms, default=0 (off)

Notes:

Under identical conditions, the tsodyks2_synapse produces
slightly lower peak amplitudes than the tsodyks_synapse. However,
the qualitative behavior is identical.

This compares the two synapse models.


References:
[1] Tsodyks, M. V., & Markram, H. (1997). The neural code between neocortical
depends on neurotransmitter release probability. PNAS, 94(2), 719-23.
[2] Fuhrmann, G., Segev, I., Markram, H., & Tsodyks, M. V. (2002). Coding of
information by activity-dependent synapses. Journal of neurophysiology, 8
[3] Maass, W., & Markram, H. (2002). Synapses as dynamic memory buffers. Neur
'''

import nest
import nest.voltage_trace

nest.ResetKernel()

'''
Parameter set for depression
'''
dep_params={"U":0.67, "u":0.67, 'x':1.0, "tau_rec":450.0, "tau_fac":0.0, "weight":250.}

'''
Parameter set for facilitation
'''
fac_params={"U":0.1, "u":0.1, 'x':1.0, "tau_fac":1000.,"tau_rec":100.,"weight":250.}

'''
Now we assign the parameter set to the synapse models.
'''
t1_params=fac_params       # for tsodyks_synapse
t2_params=t1_params.copy() # for tsodyks2_synapse

nest.SetDefaults("tsodyks2_synapse",t1_params)
nest.SetDefaults("tsodyks_synapse",t2_params)
nest.SetDefaults("iaf_psc_exp",{"tau_syn_ex": 3.})

'''
Create three neurons.
'''
neuron = nest.Create("iaf_psc_exp",3)

'''
Neuron one produces spikes. Neurons 2 and 3 receive the spikes via the two synapse models.
'''

nest.Connect([neuron[0]],[neuron[1]],syn_spec="tsodyks_synapse")
nest.Connect([neuron[0]],[neuron[2]],syn_spec="tsodyks2_synapse")

'''
Now create two voltmeters to record the responses.
'''
voltmeter = nest.Create("voltmeter",2)
nest.SetStatus(voltmeter, {"withgid": True, "withtime": True})

'''
Connect the voltmeters to the neurons.
'''
nest.Connect([voltmeter[0]], [neuron[1]])
nest.Connect([voltmeter[1]], [neuron[2]])

'''
Now simulate the standard STP protocol: a burst of spikes, followed by a pause and a recovery response.
'''
nest.SetStatus([neuron[0]], "I_e", 376.0)
nest.Simulate(500.0)
nest.SetStatus([neuron[0]], "I_e", 0.0)
nest.Simulate(500.0)
nest.SetStatus([neuron[0]], "I_e", 376.0)
nest.Simulate(500.0)

'''
Finally, generate voltage traces. Both are shown in the same plot and should be almost completely overlapping.
'''
nest.voltage_trace.from_device([voltmeter[0]])
nest.voltage_trace.from_device([voltmeter[1]])
#nest.voltage_trace.show()