 So, my topic is the cable analysis of superior colliculus neuron and I worked in Mirage Action and Missile Passage Lab and Nima Nithyanandu is part of Neuroscience and we guided me throughout the work. So, I will start with the introduction of the superior colliculus and then I will slowly move to the materials and method. So, superior colliculus forms two rose frostal bumps on the dorsal aspect of the membrane and superior colliculus is described basically as a peaceful reflex center and one of the characteristic of superior colliculus is that it is a very laminar structure that means we can broadly divide the superior colliculus in different layers like superficial, intermediate and new barriers and we are interested in SGS which is a type of sorry one of the superficial layer and in SGI which is the intermediate layer. So, most of the study involved they basically wants to know how the local circuits between these layers behaves and how the visual information is processed in these layers. So, we can see that these are the two square monitors and then if you take the corner section you can easily figure out the different layers of superficial, superior colliculus. So, what is this study about? This study basically uses the patch clamp technique which is a widely used technique to study a single cell to record the electrical activity of a single neuron. So, we can see that this is a diagram which shows a cell and this is a fiber piped and we use certain amplifiers to amplify the signal and this diagram shows the 10x this is the 10x objective image and you can see that this might be the signal from there. So, I have used here the cable analysis. So, cable analysis is a physical biophysical model to understand the electrical and geometrical property of the neuron. So, what was the objective of the study? Why we are interested in the study? So, previous study from all apps shows that the superficial layer that is SGS and intermediate layer that is SGI doesn't behave equally when we stimulate them. So, voltage imaging study says that the superficial layer and superficial layer the response the spread of the response is a bit more as compared to the intermediate. So, I wanted to investigate whether the neurons of these two layers are actually different in terms of electrical and property or how is the morphology. So, I will first talk about what the Raul model neuron is and what is the cable theory before going into the material method. So, Raul proposed that any neuron can be expressed as a combination of resistance and capacitance in Raul and the processes of the neuron that is dendrite can be represented by a single cylinder of finite electronic length and explain what electronic length is. So, in this diagram we can see that it is very similar to RC circuit that is resistance and capacitance circuit and the SOMA can be modeled as a combination of capacitance and resistance and the whole entire processes that is dendrite is represented by a single cylinder of finite length L. We talk about electronic length because this length is expressed in terms of length constant lambda instead of non centimeter. So, we can see that the neuron can be represented as a combination of SOMA and a cylinder. So, there are lot of variations in the morphology of neuron and we can see that some neurons are extensively branching. So, a national question arises that whether we can express all the neuron in this model or not. So, even the extensively branching neuron is expected to fit into the Raul's model provided they fit certain criteria that these are the criteria that specific memory resistance and axial resistivity should be uniform along the dendrite tree and the boundary conditions should be identical. But the most important criteria is this one that the geometric ratio that is the ratio of this D I indicates the diameter of the daughter branches and this D P indicates the diameter of parent branch. So, if the sum of the daughter branches raise the power 3 by 2 upon thus this the diameter of parent branch raise the power 3 by 2 this ratio should be equal to 1. It means the sum of daughter and sum of parent branch should be equal. So, this is the one of the most important criteria for fitting to the Raul's model. So, this is how this diagrams shows that this is an extensively branching neuron with the extensive branches and we can see that this can be reduced into an equivalent set in the model. So, this diagram shows that this is a neuron which is current clamp and we record the voltage response from three different side that is this side, this side and this side and we can see that as the distance varies that means distance of the recording varies from Soma. The voltage recorded also varies. So, this is what this graph says this is the voltage response voltage and this is the time axis and these are the three recordings which we get from three different side and we can see that the voltage dropped at the distance increases. So, this was taken from some other actually just for clarifying the concept. So, this idea is basically this idea found the basic idea of the table theory which says that voltage varies with distance and time. So, this entire thing is expressed as a differential equation and the solution of this differential equation can actually be used to analyze Raul's model neuron. So, it has been shown in the previous papers that if we apply the table equation under Raul Newton in the classical Raul Newton neuron. We can easily derive basic formula to estimate the length electronic length of the equivalent center that is there and the time constant. This time constant is the time which is taken by a particular neuron to reach the 63.2 percent of the steady state. So, and this is the basis of the processes and for using this formula we need parameters such as R which is the ratio of tau 1 and tau 2 and A1 which is the amplitude of the slowest component and explain what it is. But these things are derived from the current responses which we get as I stated. So, this is the response which we get when we current clamp sorry when we voltage clamp a neuron at minus 90 millivolt and then give a pulse of minus 100 millivolt for 20 millisecond and then a pulse of another pulse of minus 80 millivolt for another 20 millisecond and we can see this is the voltage this is the current response. So, this part is a exponential fit this part fits into the exponential model and this is the general equation of the exponential model and we tried a curve fitting in this part the curve fitting in this part and we try to get the parameter that is the tau value which we need for calculating the L and R and this is the core sense. So, the model which was used in the curve fitting is the sum of exponential. So, not all neuron fit into this model. So, mathematical criteria for fitting into this model is that R value which is the ratio of first to slowest time constant and certainly less than 90 and A1 should be less than A2 where A1 is A2 is the core sense this these are the first to slowest amplitude. So, the methods used we first prepared rain slices from young grads using the standard protocol and then we passed them we recorded the current response under that the pulse I have already shown that and we then tried curve fitting and then the analysis were made. So, these experiments were repeated in the randomly selected neuron in the two layers that is superficial layer and the deeper layer. So, when we tried started trying curve fitting we found that there are certain variations in the result depending upon the method of the curve fitting we are using. So, I tried three different method for curve fitting that is simplex, Nivemberg, Markworth and Jebysse. And another thing we found that this is the peak of the response and this is the site where a lot of noise or out glass is present. So, it is better to exclude these data set while curve fitting. So, this exit shows the time interval which was excluded from the data set for curve fitting and this exit shows the sum of the square of error and we can easily make out that the Levenberg Markworth is one of the best method as compared to simplex and Jebysse. So, this was repeated for ten another cells and we found that Levenberg Markworth is best and it is in terms of how many number of terms we should consider in the model there was a lot of variability that means some Neuron pitted into some of two some pitted in three or four. So, the results we found that the Levenberg Markworth was best fitting method and if we exclude first 0.2 to 0.3 millisecond from the peak in the data set and include almost 90 more than 90 percent of the data points the curve fitting in the least sum of squared errors and we tried this for ten different cells and we used this protocol for another further for results. So, we tried like 30 cells, 20 cells in the superficial layer and 10 cells in the intermediate layer and we found that the electronic length the resistance of the processes and the time constant was not significantly different in these two layer. Though we find the average is a bit the Neuron of the superficial layer appears to be a bit smaller as compared to the SCI and the time constant in this layer appears to be a bit more but these differences are not significant. The most important thing which we formed was in superficial layer we find the number of the cases of the failure was very high that is 11 of Neuron failed out of 20 and SCI only 3 failed out of 10. So, the number of the failure is very large as compared to the in superficial layer as compared to the intermediate layer. So, we conclude that there is high failure rate in the superficial layer Neuron. This indicates that there might be a diverse population in this layer, population of the cell in this layer and they have not found any significant difference in length, time constant and process resistance in now but still we need a larger sample size to make any definitive earns.