# Defining synapses and inhibitory and excitatory neurons **URL:** \ **Category:** Support\ **Tags:** synapses\ **Created:** [26 February 2021 19:25 UTC](https://brian.discourse.group/t/defining-synapses-and-inhibitory-and-excitatory-neurons/325 "2021-02-26T19:25:13Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![qwert](https://avatars.discourse-cdn.com/v4/letter/q/7bcc69/32.png) [@qwert](https://brian.discourse.group/u/qwert)\ **Post date:** [26 February 2021 19:25 UTC](https://brian.discourse.group/t/defining-synapses-and-inhibitory-and-excitatory-neurons/325/1 "2021-02-26T19:25:13Z")
Hi everyone! I am new to the Brian2 simulator and I am a bit confused about 2 things: 1. When defining a synapse, is a weight indirectly added to the neuron, or do I need to specify a weight? I have this code that I wrote: ```auto synapse_equations = ''' dcontrol/dt = -control/(tau_value) : amp*meter**-2 total_post = (control/(coming_neurons - 1))*int(coming_neurons > 1) + (control/(coming_neurons))*int(coming_neurons == 1) + 0*amp*meter**-2*int(coming_neurons == 0) : 1 (summed) coming_neurons : 1 ''' syn = Synapses(neuron, neuron, synapse_equations, on_pre = 'control += w', method = 'euler') ``` where the variable “control” tracks the amount of amps/meter^2 from a pre-synaptic neuron to a post-synaptic neuron, and once it fires an action potential, a weight of w is added to “control” of the pre-synaptic variable. The variable “total” collects all of the “control” amounts from each pre-synaptic neuron and sums them up together and then divides everything by the number of pre-synaptic neurons. The variable “coming\_neurons” represents the number of pre-synaptic neurons connected to a post-synaptic neuron. I am worried that if a weight is automatically added once you use Synapse(), then by specifying another equation using on\_pre, then I would be adding a weight two times. Is this the case or is it not automatically added when you use Synapse()? 1. I am trying to use inhibitory and excitatory neurons for my neuron model, but I haven’t found a way to specify if neurons are excitatory or inhibitory. I saw this example on Brian2’s website: [https://brian2.readthedocs.io/en/stable/examples/frompapers.Stimberg\_et\_al\_2018.example\_1\_COBA.html](https://brian2.readthedocs.io/en/stable/examples/frompapers.Stimberg_et_al_2018.example_1_COBA.html), where different synaptic conductances and neurotransmitters were used in order to tell the difference between the neurons. Is this the only way of specifying inhibitory and excitatory neurons in Brian2, or are there other ways? Thank you in advance for your help! ---
**Author:** ![mstimberg](https://yyz2.discourse-cdn.com/free1/user_avatar/brian.discourse.group/mstimberg/32/11_2.png) [@mstimberg](https://brian.discourse.group/u/mstimberg)\ **Post date:** [1 March 2021 14:03 UTC](https://brian.discourse.group/t/defining-synapses-and-inhibitory-and-excitatory-neurons/325/2 "2021-03-01T14:03:24Z")
Hi and welcome. I am not sure I understand completely, so please clarify things if my answer is off-target. In your `Synapses` model, you have one variable `control` that gets increased by `w` for every incoming spike, and every time step you update the post-synaptic variable `total` based on the value of `control` on all synapses connecting to it. I don’t quite see where you think this could add `w` twice, the only part where `w` is added is in `on_pre`, i.e. triggered by pre-synaptic spikes. However, you are saying that > [@qwert](#): > > a weight of w is added to “control” of the pre-synaptic variable which is not quite correct since you are adding `w` to the _synaptic_ variable `control`. I.e., each synapse has its own `control` variable, even though for all synapses that come from the same pre-synaptic cell, this value will be identical. The result would be identical if instead you declared `control` in the equations of the pre-synaptic neuron (so it only gets stored/updated once per cell) and update it not via `on_pre` in the `Synapses`, but via the `reset` statement in the `NeuronGroup`. Your equation for `total_post` then has to refer to `control_pre`. Just to make this clear: this will not change anything in the result, it will just calculate things more efficiently. Regarding efficiency, one more remark: you do not need the `coming_neurons` variable, this value is already provided by Brian as `N_incoming`. But more importantly, you are recalculating the normalization for every synapse and every time step, even though it will not change during the run. Instead, you could calculate it once before the simulation starts. Something along the lines of: ```Python synapses_equations = ''' total_post = normalization * control_pre : 1 (summed) normalization : 1 (constant) ''' syn = Synapses(...) syn.connect(...) syn.normalization = '1.0/(N_incoming - 1)*int(N_incoming > 1) + (1.0/N_incoming)*int(N_incoming == 1) + 0*amp*meter**-2*int(N_incoming == 0)' ``` > [@qwert](#): > > Is this the only way of specifying inhibitory and excitatory neurons in Brian2, or are there other ways? Do you mean to make a difference between different neuron types (e.g. regular spiking excitatory pyramidal cells vs. fast spiking inhibitory interneurons) or just between excitatory and inhibitory synapses? For both, there is no general way, all depends on the desired complexity of your model. E.g. in a very simple integrate-and-fire model like the [CUBA example](https://brian2.readthedocs.io/en/stable/examples/CUBA.html), both types of neurons are modeled in the same way and the only difference between the synapses is their sign. On the other end of the spectrum, you could model each cell type in detail with different types of ion channels and different sizes/capacitances/etc. and use detailed neurotransmitter models for AMPA, NMDA, GABA, …, synapses. And of course there are many intermediate solutions, e.g. for relatively abstract integrate-and-fire models the most common way to model the difference between excitatory and inhibitory neurons is to simply give the inhibitory neurons a shorter time constant, and similarly you can get a crude approximation of different neurotransmitters by changing the time constant of your synapses. ---
**Author:** ![qwert](https://avatars.discourse-cdn.com/v4/letter/q/7bcc69/32.png) [@qwert](https://brian.discourse.group/u/qwert)\ **Post date:** [1 March 2021 19:30 UTC](https://brian.discourse.group/t/defining-synapses-and-inhibitory-and-excitatory-neurons/325/3 "2021-03-01T19:30:12Z")
Hi Marcel, Thank you for your reply. You were definitely not off in understanding my questions. It clarified a lot of things for me! Thank you also for taking the time in explaining how I could make my code more efficient. I really appreciate it. 🙂