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7

Read dayan and abbot "theoretical neuroscience" Learn differential equations Know the relationship between voltage, current, resistance and conductance Differential equations is absolutely essential though. you don't need to learn to solve them (the computer will do that for you), you just need to learn to know what they mean. How do researchers ...


5

Check out all of the videos in this playlist https://www.youtube.com/watch?v=lrppe54fixc&list=PL1hKzFfV5qJlVTjD8XWzyiaHxBZZ7iWs9&index=3 The particular video I linked gives the example of the "Papez circuit". Other interviewees mention other examples as well.


4

Not intuitive, but to save the value of a curve plotted in a graph: Right click the graph window, and choose "Pick Vector" Click on the desired curve (it should change color i.e. to red) In the NEURON Main Menu > Vector > Save to File Type in the file name > Save File will have two columns, first one for X-axis values (i.e. time) and second for the Y-axis ...


3

The Hodgkin and Huxley model of neuronal firing is based on non-linear differential equations. A significant portion of research on sensation and perception is based on such models.


3

This isn't exactly what you are referring to, but I think provides a similar function and has been shown in vertebrate vision: https://en.wikipedia.org/wiki/Normalization_model Divisive normalization as a canonical computation across the brain While this does not implement histogram equalization, I think it is actually a better-suited explanation of the ...


3

A repository of publicly available NEURON models can be found on ModelDB by filtering for Models that contain the Modeling Application: NEURON.


3

ρ(t) in Equation 1.1 cannot be summed and averaged because the idealized spikes are infinitesimally narrow and the chance of two spikes from different trials occurring at the same time t is zero. In Equation 1.2, the idea of convolving each idealized spike with a "well behaved function" h(t). If you use a rectangular function around zero as h(t), with a ...


3

According to this research paper by Lockary and Goodman, the resting membrane potential of C.Elegans is two fold because its a stepped resting potential of -70mV and -35mV. After an initial activation a resting potential of -35mV is held following a higher depolarisation, so on your graph the initial depolarisation would be a little higher, and then drops to ...


3

The weights in an artificial neural network are an approximation of multiple processes combined that take place in biological neurons. Myelination plays a role, but not a major one. Weights in artificial neural networks can be positive or negative numbers. Weight magnitude. The magnitude of a weight is analogous to a combination of increased dendritic ...


3

The weight matrix is typically considered to be a strength of connectivity metric between nodes in the parallel computronium model that neural networks are based on. That fact is fairly evident when you investigate ANN learning algorithms. For instance, the backpropagation algorithm for feedforward networks is designed to strengthen a string of associations ...


3

Short answer Mostly Cl- is disregarded in calculations of the resting membrane potential and action potential voltage changes, because it is less important for the neural membrane characteristics than Na+ and K+. Background In some neurons Cl- is not actively transported. In terms of the resting membrane potential, Cl- hence settles its gradient passively ...


3

In normal neurons, Chloride's reversal potential is near the resting potential for the neuron and also happens to be near the leak conductance reversal potential for the neuron. While not exactly the same these three are sometimes confused. The difference between these three reversal potentials is subtle. Chloride Reversal Potential: is the potential ...


2

Typing the following into the NEURON console will reset and run the simulation: run()


2

What they mean is that as long as you rotate $C_i$ and $C_j$ by the same amount, the dot product will be the same. This is rotational invariance because the dot product is invariant to coherent rotation of the relevant vectors. In neural terms, the correlation between the activity of two neurons (in a population representing a one dimensional circular ...


2

If anyone ever finds this and has the same problem: The original paper has a flaw in the paper: alphac=(exp((Vd/mV-10)/11)-exp((Vd/mV-6.5)/27))/(18.975)* (Vd<=50*mV)/ms+2*exp((6.5-Vd/mV)/27)*(Vd>50*mV)/ms : Hz An errata published later changes this to: ...


2

The oscillations measured by EEG, ECoG, and MEG are thought to originate in the apical dendrites' fluctuating potentials. Try looking into the forward problem of EEG. Like the inverse problem but in reverse. Given a known source potential (apparently it's very common to model cortical neurons' LFP as a distribution of current dipoles?) you solve the Poisson ...


1

This is actual a pretty old and often debated question. It is called "Lady Lovelace's Objection" and first appeared in Alan Turing's seminal paper "Computing Machinery and Intelligence". Below is my response to the objection, as well as Alan Turing's response which I wrote for a philosophy course about a year ago. Perhaps it will be of interest to you? ...


1

Because of variation between organisms, cells in the same organism, or even the same cell separated by a few days, can have different parameter set. However, I think this is where parameter fitting becomes useful. As suggested in the comments knowing how parameters change between cells or over time can be very insightful. Parameter Estimation is very ...


1

You really want to read the work by Gyorgy Buzsaki. The recent Schomburg et al., 2014 is quite illuminating although quite dense. Try my F1000 review for starters. http://f1000.com/prime/contributor/evaluate/article/718892254 The original paper can be found here http://www.sciencedirect.com/science/article/pii/S0896627314007818


1

i once wrote a paper doing biophysical modeling of neurons that could create plateau potentials. While the paper itself is not exactly what you are looking for, there should be lots of good references in there: http://www.jneurosci.org/content/33/2/424.short Sanders H, Berends M, Major G, Goldman MS, Lisman JE. (2013) NMDA and GABAB (KIR) Conductances: ...


1

A paper just came out last week in Neuron demonstrating dendrite-specific gating: http://www.cell.com/neuron/fulltext/S0896-6273(16)00054-4?elsca1=etoc&elsca2=email&elsca3=0896-6273_20160302_89_5_&elsca4=Cell%20Press Structured Dendritic Inhibition Supports Branch-Selective Integration in CA1 Pyramidal Cells Erik B. Bloss, Mark S. Cembrowski, ...


1

I'm assuming that you want some kind of "computer vision" model (in that you want to be able to provide the model with input stimuli in the form of an image), and that you want to predict some kind of behaviour? (e.g., RT from a search task). Fleshing out the different processes involved is not going to be trivial, so there probably isn't a ...


1

It is generally thought that thalamic input comes in layer 4, feed back from higher areas come through layer 1 to layer 2/3 and feed forward is sent from the deeper layers. see Canonical Microcircuits for Predictive Coding Andre Bastos, W. Martin Usrey, Rick Adams, George Mangun, Pascal Fries, and Karl Friston ...


1

More than two years later, I now have a heuristic (which is a close as I can get to an objective metric) that I rely on. As I write about in one of my blog posts, there are a couple of tasks that humans are capable of that are still really hard for machines. They require the following things for a machine (or a neural model): Balancing Planning and ...


1

Unintuitive, but it's in the right click > View sub-menu: Pan/translate: Right click the chart View > Translate Left click drag will pan around the chart Zoom: Right click the chart View > Zoom In/Out Left click drag horizontally or vertically to zoom the x or y axis Zoom in on a region: Right click the chart View > NewView Left click on the chart ...



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