8.4 Modeling cell assemblies  (Page 7/9)

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Network size and patterns

In the simulations there will be 50 E-cells and 50 I-cells. The Network will be "trained" to encode assemblies by running the weight producing algorithm on a set of patterns, each which represents a cell assembly (remember weights are only produced between the E-cells initially). There will be 8 patterns that contain 8 E-Cells in each. Each "event" or pattern will carry equal significance when the weighting algorithm runs. To help visualize the patterns look at the diagram below.

Parameters for cells

Below are tables with parameter that are given exactly from A Lansner and E Fransen.

 Parameter E-Cell I-Cell ${V}_{leak}$ (mV) -50 -70 ${G}_{core}$ 0.04 0.0638 ${G}_{m}$ Soma $\left(\mu S\right)$ 0.0032 0.0016 ${C}_{m}$ Soma $\left(nF\right)$ 0.032 0.016 ${G}_{m}$ $\left(\mu S\right)$ Dentrites 0.0096 0.0096 ${C}_{m}$ Dentrites $\left(nF\right)$ 0.288 0.288 ${V}_{Na}$ 40 50 ${G}_{Na}$ $\left(\mu S\right)$ 1.0 1.0 ${V}_{K}$ -70 -90 ${G}_{K}$ $\left(\mu S\right)$ 0.5 1.0 ${V}_{Ca}$ 150 150 ${G}_{Ca}$ $\left(\mu S\right)$ 0 0 ${G}_{K\left(Ca\right)}$ $\left(\mu S\right)$ 0.0017 0.01 ${\rho }_{AP}$ (mV ${}^{-1}$ ms ${}^{-1}$ ) 4 0.013 ${\delta }_{AP}$ ms ${}^{-1}$ .075 .02
 m h n q p A (mV ${}^{-1}$ ms ${}^{-1}$ ) 0.2 0.08 0.02 0.08 0.7 (ms ${}^{-1}$ ) $\alpha$ B (mV) -40 -40 -15 -25 C (mV) 1 1 0.8 1 17 A (mV ${}^{-1}$ ms ${}^{-1}$ ) 0.06 0.4 0.04 0.005 0.1 (ms ${}^{-1}$ ) $\beta$ B (mV) -49 -36 -40 -20 C (mV) 20 2 0.4 20 17
 m h n q A (mV ${}^{-1}$ ms ${}^{-1}$ ) 0.2 0.08 0.02 0.08 $\alpha$ B (mV) -30 -30 -21 -15 C (mV) 1 0.2 0.2 1 A (mV ${}^{-1}$ ms ${}^{-1}$ ) 0.06 0.4 0.02 0.005 $\beta$ B (mV) -38 -26 -18 -10 C (mV) 20 0.2 0.2 20
 ${V}_{syn}$ Excitatory 0 $mV$ ${V}_{syn}$ Inhibitory -85 $mV$ $s$ variable ${G}_{syn}$ E to E (AMPA) ${w}_{ij}$ x ${w}_{E2E}$ ${G}_{syn}$ E to I (AMPA) ${w}_{ij}$ x ${w}_{E2I}$ ${G}_{syn}$ I to E ${g}_{I2E}$ ${G}_{NMDA}$ E to E ${G}_{syn}$ x ${w}_{NMDA}scalar$ ${\rho }_{NMDA}$ E to E ${G}_{syn}$ x ${\rho }_{NMDA}scalar$ ${\delta }_{NMDA}$ $m{s}^{-1}$ .02

Remaining parameters

There are a few parameters that remain to be given values. A Lanser and Fransen do not give exact values for these unknown parameters, but instead a desired range for EPSP's (Excitatory Post Synaptic Potentials) that the parameters help determine. By experimenting with a single neuron we can find the ideal range for these unknown parameters. Hence, we can then scale the resulting Weights from the algorithm so they are mapped to the ideal range. We need an overall weighting scale constant for connections from E to E cells (AMPA), ${w}_{E2E}$ . A weighting scale constant for E to I cells, ${w}_{E2I}$ Also, a scale general conductance constant for all I to E connections, ${g}_{I2E}$ . The NMDA connections will be proportional to the AMPA weight but scaled by a constant ${w}_{NMDA}scalar$ . The influx parameter ${\rho }_{NMDA}$ for the $\left[C{a}_{NMDA}\right]$ pool is also proportional to the AMPA weight. Lastly, the binary variable $s$ needs to be given a time duration to stay active for. This time duration may be chosen different for each type of connection class as well (meaning E to E, E to I, and I to E).

Most of these unfixed variables relate to the strength of connections between cells and the EPSPs or IPSPs they create. The reason they won't be fixed for all different size simulations is that the values should vary according to size of the networks and assemblies. For instance, if we model on large networks that have large assemblies we would desire that a cell would require synaptic input from more cells in order to fire, than when the network is small. By picking these numbers accordingly we will be able to roughly determine how many cells of an assembly need to be firing in order to fully activate the assembly.

so some one know about replacing silicon atom with phosphorous in semiconductors device?
how to fabricate graphene ink ?
for screen printed electrodes ?
SUYASH
What is lattice structure?
of graphene you mean?
Ebrahim
or in general
Ebrahim
in general
s.
Graphene has a hexagonal structure
tahir
On having this app for quite a bit time, Haven't realised there's a chat room in it.
Cied
what is biological synthesis of nanoparticles
what's the easiest and fastest way to the synthesize AgNP?
China
Cied
types of nano material
I start with an easy one. carbon nanotubes woven into a long filament like a string
Porter
many many of nanotubes
Porter
what is the k.e before it land
Yasmin
what is the function of carbon nanotubes?
Cesar
I'm interested in nanotube
Uday
what is nanomaterials​ and their applications of sensors.
what is nano technology
what is system testing?
preparation of nanomaterial
Yes, Nanotechnology has a very fast field of applications and their is always something new to do with it...
what is system testing
what is the application of nanotechnology?
Stotaw
In this morden time nanotechnology used in many field . 1-Electronics-manufacturad IC ,RAM,MRAM,solar panel etc 2-Helth and Medical-Nanomedicine,Drug Dilivery for cancer treatment etc 3- Atomobile -MEMS, Coating on car etc. and may other field for details you can check at Google
Azam
anybody can imagine what will be happen after 100 years from now in nano tech world
Prasenjit
after 100 year this will be not nanotechnology maybe this technology name will be change . maybe aftet 100 year . we work on electron lable practically about its properties and behaviour by the different instruments
Azam
name doesn't matter , whatever it will be change... I'm taking about effect on circumstances of the microscopic world
Prasenjit
how hard could it be to apply nanotechnology against viral infections such HIV or Ebola?
Damian
silver nanoparticles could handle the job?
Damian
not now but maybe in future only AgNP maybe any other nanomaterials
Azam
Hello
Uday
I'm interested in Nanotube
Uday
this technology will not going on for the long time , so I'm thinking about femtotechnology 10^-15
Prasenjit
can nanotechnology change the direction of the face of the world
At high concentrations (>0.01 M), the relation between absorptivity coefficient and absorbance is no longer linear. This is due to the electrostatic interactions between the quantum dots in close proximity. If the concentration of the solution is high, another effect that is seen is the scattering of light from the large number of quantum dots. This assumption only works at low concentrations of the analyte. Presence of stray light.
the Beer law works very well for dilute solutions but fails for very high concentrations. why?
how did you get the value of 2000N.What calculations are needed to arrive at it
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