<p>According to the <a href="http://www.sciencemag.org/content/357/6355/1033/suppl/DC1" rel="nofollow noreferrer">Supplementary Materials</a> the synaptic plasticity rule can be inferred from the data to be:</p>

<p>$$W(z) = W_0(z) + \alpha \sum_{t,t'=0}^{T} R_{CA1}(t')K(t'-t)R_{CA3}(z-x_A(t))$$</p>

<p>Where:</p>

<ul>
<li>$z$ is the preferred location of the place cell</li>
<li>$W_0$ is the initial synaptic weight</li>
<li>$\alpha$ is a learning-rate constant</li>
<li>$T$ is the total running time of the experiment with $t'$ being when the plateau starts, although I'm not sure about $t'$ and it's significance</li>
<li>$R_{CA1}$ is a binary function which is 1 in the presence of the plateau</li>
<li>$K(t'-t)$ is an unknown kernel that determines <strong>synaptic weights change depending on the activity of the cells</strong> at time $t'=t$ </li>
<li>$R_{CA3}(z-x_A(t))$ is a model of the firing rate of the place-cell given the preferred location and the current location of the animal</li>
</ul>

<p>Given that the formula for weight change depends on both:</p>

<ol>
<li>The firing rate of the place cell</li>
<li>A kernel which takes the firing rate of the place cell into account</li>
</ol>

<p>I can say that the plateau selects for firing place cells. However, I'm still not clear on what they mean by storing "entire sequence of events that occurred for several seconds before and after plateau-potential initiation".</p>

CC BY-SA 4.0
2020-02-22T16:20:26.230Z
I have a clarification. t' is the time of the plateau (a postsynaptic dendritic event). When a presynaptic spike occurs at time t, the degree of change in synaptic strength at that input depends on the time interval between that presynaptic spike and the postsynaptic plateau: t' - t. Because the kernel extends for multiple seconds around the time of the plateau, hundreds of active inputs can be strengthened, and their final strengths after plasticity will be a read out of the order they were activated in a long sequence.
Aaron
CC BY-SA 3.0
2017-09-15T01:59:32.750Z
2017-09-15T01:59:32.750Z
Seanny123
<p>In "<a href="http://science.sciencemag.org/content/357/6355/1033" rel="nofollow noreferrer">Behavioral time scale synaptic plasticity underlies CA1 place fields</a>" by Bittner et al. an alternative to Hebbian plasticity is proposed called Behavioral Time-scale Synaptic Plasticity (BTSP). Basically, instead of depending on Hebbian plasticity, a calcium-ion plateau can be used to create associations. From the paper:</p>

<blockquote>
  <p>BTSP can rapidly store the entire sequence of events that occurred for
  several seconds before and after plateau-potential initiation within
  the synaptic weights of area CA1.</p>
</blockquote>

<p>However, despite reading the paper, I'm still confused about the mechanism they think performs this. Does the "plateau" cause all spiking neurons to strengthen their connections or does the "plateau" use some other mechanism to influence neurons? Is excitation or potentiation even the correct way to understand this?</p>

<cognitive-neuroscience><neurophysiology><plasticity>
Effect of plateau in BTSP
Seanny123
# Effect of plateau in BTSP

(Asked by: username_0 on 2017-09-15)

In "[Behavioral time scale synaptic plasticity underlies CA1 place fields](http://science.sciencemag.org/content/357/6355/1033)" by Bittner et al. an alternative to Hebbian plasticity is proposed called Behavioral Time-scale Synaptic Plasticity (BTSP). Basically, instead of depending on Hebbian plasticity, a calcium-ion plateau can be used to create associations. From the paper:

> BTSP can rapidly store the entire sequence of events that occurred for
> several seconds before and after plateau-potential initiation within
> the synaptic weights of area CA1.

However, despite reading the paper, I'm still confused about the mechanism they think performs this. Does the "plateau" cause all spiking neurons to strengthen their connections or does the "plateau" use some other mechanism to influence neurons? Is excitation or potentiation even the correct way to understand this?

**Tags:** cognitive-neuroscience, neurophysiology, plasticity


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## 1 answers

### Answer by username_0 on 2017-09-15. Score: 1 | Accepted answer

According to the [Supplementary Materials](http://www.sciencemag.org/content/357/6355/1033/suppl/DC1) the synaptic plasticity rule can be inferred from the data to be:

$$W(z) = W\_0(z) + \alpha \sum\_{t,t'=0}^{T} R\_{CA1}(t')K(t'-t)R\_{CA3}(z-x\_A(t))$$

Where:

* $z$ is the preferred location of the place cell
* $W\_0$ is the initial synaptic weight
* $\alpha$ is a learning-rate constant
* $T$ is the total running time of the experiment with $t'$ being when the plateau starts, although I'm not sure about $t'$ and it's significance
* $R\_{CA1}$ is a binary function which is 1 in the presence of the plateau
* $K(t'-t)$ is an unknown kernel that determines **synaptic weights change depending on the activity of the cells** at time $t'=t$
* $R\_{CA3}(z-x\_A(t))$ is a model of the firing rate of the place-cell given the preferred location and the current location of the animal

Given that the formula for weight change depends on both:

1. The firing rate of the place cell
2. A kernel which takes the firing rate of the place cell into account

I can say that the plateau selects for firing place cells. However, I'm still not clear on what they mean by storing "entire sequence of events that occurred for several seconds before and after plateau-potential initiation".

> **Comments:**
> **username_1**: I have a clarification. t' is the time of the plateau (a postsynaptic dendritic event). When a presynaptic spike occurs at time t, the degree of change in synaptic strength at that input depends on the time interval between that presynaptic spike and the postsynaptic plateau: t' - t. Because the kernel extends for multiple seconds around the time of the plateau, hundreds of active inputs can be strengthened, and their final strengths after plasticity will be a read out of the order they were activated in a long sequence.  

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