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While metaplasticity suffices in providing a means of final weight stability, with our current set of parameters we are unable to achieve place field stability. As displayed by the matrix of firing degrees for cell 60 (shown above in [link] ), after continued potentiation of weights, the cell's place field will rapidly expand to span the entire track. This is a result of the high firing rate at which metaplasticity takes effect: the upper bound on firing rate is reached only once the synaptic weights have become strong enough to independently stimulate each other, resulting in continuous firing regardless of external input/position and place fields spread across the track. More tuning of metaplasticity will be needed in order to obtain the desired final place field stability. This will most likely be achieved by increasing the rate of NMDAR removal or decreasing the rate of NMDAR insertion, which should decrease the maximum firing rate and halt backward shift to effectively achieve place field stability.

After evaluating additive and multiplicative STDP alongside CaDP in explaining the phenomena associated with spatial memory in hippocampal place cells, we find that CaDP provides the most reasonable explanation for the mechanisms of synaptic plasticity. In CaDP, the mechanisms of potentiation and depression of weights are based upon biologically-supported pathways, whereas STDP does not explicitly draw connections between its weight changes and the biological mechanisms involved. In particular, the stabilization of synaptic weights is reasonably explained by coupling metaplasticity with CaDP, where STDP requires the use of an unrealistic upper weight limit to stabilize its place fields. The additional parameters involved with the model allow for more variations in the behavior of the system under different calcium LTD/LTP bounds, conductances, etc. One downside from a computational standpoint, especially when modeling a large network, lies in the additional parameters that are required to model the plasticity compared to STDP, which requires fewer parameters and yields similar results. However, if more biological accuracy and a wider range of tunability is desired, CaDP is the model of choice for synaptic plasticity.

Conclusions/future work

In this report we discussed the modeling of hippocampal place cells using two different plasticity models: Spike-time Dependent Plasticity and Calcium Dependent Plasticity. We have described the equations behind the plasticity models and their specific effects on the cell network interactions. We have discussed some of the differences between the two models, which include CaDP's rate-dependent plasticity as well as its inclusion of a second LTD window. We demonstrated that both models account for the backward shift of place fields observed experimentally. We also find that by coupling metaplasticity with CaDP, we can achieve final stability of synaptic weights; whereas STDP relies on weight bounds to achieve this effect.

While implementing metaplasticity successfully stabilizes synaptic weights, our simplified model does not exhibit final place field stability. Further work will be needed to tune the parameters involved with metaplasticity in order to obtain final place field stability. Once we are able to achieve the desired effects with metaplasticity, a possible area of interest would be to develop a means of coupling metaplasticity with STDP and comparing the results to that of CaDP with metaplasticity.

Other areas of interest include augmenting the 120-cell ring model to contain overlapping place fields at the beginning of the simulation and observing how this may affect the final weight distribution. Another modification to this model would be to implement a Gaussian distribution of place cell input firing rates instead of a uniform distribution with constant input rate. The original synaptic weight distribution could also be modified such that the 120 cell network contained connections to all other cells instead of using the simplified ring architecture. It can also be set to have randomized initial weights as well to make the initial state of the place cells more realistic. Once these modifications have been well studied, it could then be incorporated into a larger model of the hippocampal neural network, involving inhibitory connections as well as grid cell and head-direction cell inputs.

The backward shift and the stabilization of place fields have been closely associated with the development of spatial memory. Modeling and understanding the biochemical and biophysical processes behind synaptic plasticity and how they explain experimental results will aid us in better comprehending the mechanisms behind spatial memory. Further progress in this field may provide us with the knowledge to better understand not only how we develop a memory of our environment but also how plasticity mechanisms in the hippocampus are involved in diseases such as Alzheimer's or Epilepsy.


First and foremost, I would like to thank my mentor, Katie Ward, for all the help and guidance that she has given me this summer. I would also like to thank Dr. Cox for his support and encouragement. My thanks also goes out to Georgene Jalbuena for assisting me in learning LaTeX. Finally, I would like to thank Rice University's VIGRE program for sponsoring this research.

Questions & Answers

can someone help me with some logarithmic and exponential equations.
Jeffrey Reply
sure. what is your question?
okay, so you have 6 raised to the power of 2. what is that part of your answer
I don't understand what the A with approx sign and the boxed x mean
it think it's written 20/(X-6)^2 so it's 20 divided by X-6 squared
I'm not sure why it wrote it the other way
I got X =-6
ok. so take the square root of both sides, now you have plus or minus the square root of 20= x-6
oops. ignore that.
so you not have an equal sign anywhere in the original equation?
Commplementary angles
Idrissa Reply
im all ears I need to learn
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what is a good calculator for all algebra; would a Casio fx 260 work with all algebra equations? please name the cheapest, thanks.
Kevin Reply
a perfect square v²+2v+_
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Abdirahman Reply
algebra 2 Inequalities:If equation 2 = 0 it is an open set?
Kim Reply
or infinite solutions?
The answer is neither. The function, 2 = 0 cannot exist. Hence, the function is undefined.
Embra Reply
if |A| not equal to 0 and order of A is n prove that adj (adj A = |A|
Nancy Reply
rolling four fair dice and getting an even number an all four dice
ramon Reply
Kristine 2*2*2=8
Bridget Reply
Differences Between Laspeyres and Paasche Indices
Emedobi Reply
No. 7x -4y is simplified from 4x + (3y + 3x) -7y
Mary Reply
is it 3×y ?
Joan Reply
J, combine like terms 7x-4y
Bridget Reply
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I'm not good at math so would you help me
what is the problem that i will help you to self with?
how do you translate this in Algebraic Expressions
linda Reply
Need to simplify the expresin. 3/7 (x+y)-1/7 (x-1)=
Crystal Reply
. After 3 months on a diet, Lisa had lost 12% of her original weight. She lost 21 pounds. What was Lisa's original weight?
Chris Reply
what's the easiest and fastest way to the synthesize AgNP?
Damian Reply
types of nano material
abeetha Reply
I start with an easy one. carbon nanotubes woven into a long filament like a string
many many of nanotubes
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what is nanomaterials​ and their applications of sensors.
Ramkumar Reply
what is nano technology
Sravani Reply
what is system testing?
preparation of nanomaterial
Victor Reply
Yes, Nanotechnology has a very fast field of applications and their is always something new to do with it...
Himanshu Reply
good afternoon madam
what is system testing
what is the application of nanotechnology?
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
anybody can imagine what will be happen after 100 years from now in nano tech world
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
name doesn't matter , whatever it will be change... I'm taking about effect on circumstances of the microscopic world
how hard could it be to apply nanotechnology against viral infections such HIV or Ebola?
silver nanoparticles could handle the job?
not now but maybe in future only AgNP maybe any other nanomaterials
can nanotechnology change the direction of the face of the world
Prasenjit Reply
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.
Ali Reply
the Beer law works very well for dilute solutions but fails for very high concentrations. why?
bamidele Reply
how did you get the value of 2000N.What calculations are needed to arrive at it
Smarajit Reply
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Source:  OpenStax, The art of the pfug. OpenStax CNX. Jun 05, 2013 Download for free at http://cnx.org/content/col10523/1.34
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