# User talk:Jmn96/Benchmark II: Model Description

## Student Comments

Your model description is pretty concise. However, model simulation should be included in the Result section instead of model description. In addition, I think the description of state variable u in the table is a little bit vague, but since you've explained it later, maybe re-word it as "repolarizing potential" or something like that in the table so we get a sense of the unit of the state variable. Finally, since this model is greatly simplified to two dimension, you could probably describe this model in more detail and compare with Hodgkin-Huxley model in your Model Description. --Alexis Wu (talk) 23:53, 17 April 2017 (EDT)

- You refer to Figure 3 but you don't really specify what it is. You have figure for Figure 2, but lack one for Figure 3.
- "By modulation of parameter values, we should be able to distinguish changes in large scale neural network output frequencies" -- Which parameter values?
- "I represents the current supplied to the neuron" -- you should bold "I" like you did the other variables, I think it'll make it more clear.
- You are lacking details on the model extension that you are going to perform.

--Noora Khiraoui (talk) 12:33, 18 April 2017 (EDT)Noora Khiraoui

## Instructor Comments

- Benchmark submitted on time?
- Yes

- Rubric submitted on time?
- Yes

- State variables, parameters and inputs to the model clearly distinguished?
- Yes.

- Term-by-term description of model components?
- Yes. However, when you say “the integrate and fire and Hodgkin-Huxley models”, you are confusing two very different models, and also you are not describing the Izhikevich model accurately. The Hodgkin Huxley model captured a very restricted number of ionic conductances, and was designed primarily to capture axonal transmission in the squid giant axon. As described in detail in Chapter 7 of the book, which you need to read carefully, it was subsequently extended by the description of a large number of additional ionic conductances. Izhikevich has done a careful dynamical analysis of these more complex models, and has been able to capture their properties with his much more simple model. So, his model is not a Hodgkin Huxley model, but a greatly simplified model that captures the dynamics of neural models that are more complex than the Hodgkin Huxley model.

- Have you described model assumptions?
- Yes

- Description of equation simulation?
- Yes. A major omission is the description of the synaptic matrix that interconnects all of the neurons. Without describing this, you are missing a major component of the model.

On the whole, this is a good to very good description of the model simulation. Please make corrections along the lines suggested above. Also, please add the details of the model extension for the final term paper.

--Hillel Chiel (talk) 12:27, 9 April 2017 (EDT)