# Example of the tsodyks2_synapse in NEST¶

Run this example as a Jupyter notebook:

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 any type of synapse 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 - probability of release increment (U1) [0,1], default=0.

• u - Maximum probability of release (U_se) [0,1], default=0.

• x - current scaling factor of the weight, default=U

• tau_rec - time constant for depression in ms, default=800 ms

• tau_fac - 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¶

```import matplotlib.pyplot as plt
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": 250.0}
```

Parameter set for facilitation

```fac_params = {"U": 0.1, "u": 0.1, "x": 1.0, "tau_fac": 1000.0, "tau_rec": 100.0, "weight": 250.0}
```

Now we assign the parameter set to the synapse models.

```tsodyks_params = dict(fac_params, synapse_model="tsodyks_synapse")  # for tsodyks_synapse
tsodyks2_params = dict(fac_params, synapse_model="tsodyks2_synapse")  # for tsodyks2_synapse
```

Create three neurons.

```neuron = nest.Create("iaf_psc_exp", 3, params={"tau_syn_ex": 3.0})
```

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_params)
nest.Connect(neuron[0], neuron[2], syn_spec=tsodyks2_params)
```

Now create two voltmeters to record the responses.

```voltmeter = nest.Create("voltmeter", 2)
```

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.

```neuron[0].I_e = 376.0

nest.Simulate(500.0)
neuron[0].I_e = 0.0
nest.Simulate(500.0)
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])
plt.show()
```

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