<p>Consider that elimination of such a phenomena is not ideal. It's been proposed that actual neuronal networks exist under the tension of synchronous decoupling. </p>

<p><a href="http://www.sciencedirect.com/science/article/pii/S0896627308001281" rel="nofollow">http://www.sciencedirect.com/science/article/pii/S0896627308001281</a></p>

<p>To answer your question though, you probably should consider that reciprocal connections might not be between two individual neurons. Rather, axons in the cortex are clumped into minicolumns, each one having 20+ pyramidals in supragranular layers. It's conjecture, but it's possible that the reciprocal connections are between different pyramidals within minicolumns (or even different pyramidals within columns or whatever other macro-structure you want). </p>

CC BY-SA 3.0
2012-08-05T06:16:49.430Z
You are exactly right.  You should edit your answer to demonstrate more certainty: reciprocity is not a property of single neurons.  It's a property of nuclei.

But a caveat... this is only for chemical synapses.  Electrically coupled (gap junction) neurons can be reciprocal. 

An another caveat.... retrograde signalers like THC act backwards on chemical synapses... so there is *some* form of reciprocity at the single neuron level, but it's not part of standard models really.  And what they do is modulate the connection... not send electrical signals.
Keegan Keplinger
CC BY-SA 3.0
2012-08-06T04:37:45.240Z
@Xurtio I see. I was equivocating a bit because I couldn't produce a direct source. It's just inference from other things I know of the nervous system. I'll try and find sources and edit the answer to be more certain.
Preece
CC BY-SA 3.0
2012-07-25T09:24:34.580Z
2012-07-25T09:24:34.580Z
Preece
<p>I have rather technical question regarding STDP dynamics. 
I am working on a neural network implementing an STDP learning algorithm, and have noticed that it is extremely anti-reciprocal. When two neurons have bidirectional connectivity between them, the conditions that will strengthen one direction will inevitably weaken the other, as far as I can understand. If B consistently fires immediately following a spike in A, the A->B synapse will strengthen, however the B->A synapse would weaken, as the pre-synapse / post-synapse roles are reverse. 
This wouldn't be much of a problem if it weren't for empirical evidence of high reciprocity in cortical connectivity.  </p>

<p><strong>Am I missing something? Is this a known shortcoming of the model?</strong> </p>

CC BY-SA 3.0
2012-07-19T19:13:37.560Z
Couldn't this problem be solved by having a certain transduction delay (signal transduction, synaptic transmission) in both connections?
H.Muster
CC BY-SA 3.0
2012-07-20T10:27:06.083Z
Yes I just got that answer from a friend in the field as well. It seems to be the solution - aligning the STDP function not on time 0 but rather on a certain -Xms to the left. Do you have any idea what transmission delay is reasonable?
Amir
CC BY-SA 3.0
2012-07-20T11:26:10.847Z
This surely depends on what you intend to model (i.e., cells, which part of the brain). Unfortunately, I do not have a reverence discussing this topic at hand.
H.Muster
CC BY-SA 3.0
2012-08-25T00:52:15.287Z
A synapse does not exist in isolation in the brain. That does limit how correlated the firing rates of two neurons can become. Furthermore, reciprocal connectivity is may be to the same neurons but not the same place on the neuron. For example neuron A could synapse far from B's soma but B could synapse close to A's soma.
mac389
CC BY-SA 3.0
2015-11-19T02:44:27.810Z
Not all synapses use STDP as a learning rule.  Some synapses have normal Hebbian learning.
honi
<computational-modeling><theoretical-neuroscience><neural-network><connectionism><plasticity>
How can STDP fit with reciprocal connectivity?
Amir
# How can STDP fit with reciprocal connectivity?

(Asked by: username_0 on 2012-07-19)

I have rather technical question regarding STDP dynamics.
I am working on a neural network implementing an STDP learning algorithm, and have noticed that it is extremely anti-reciprocal. When two neurons have bidirectional connectivity between them, the conditions that will strengthen one direction will inevitably weaken the other, as far as I can understand. If B consistently fires immediately following a spike in A, the A->B synapse will strengthen, however the B->A synapse would weaken, as the pre-synapse / post-synapse roles are reverse.
This wouldn't be much of a problem if it weren't for empirical evidence of high reciprocity in cortical connectivity.

**Am I missing something? Is this a known shortcoming of the model?**

**Tags:** computational-modeling, theoretical-neuroscience, neural-network, connectionism, plasticity

> **Comments:**
> **username_1**: Couldn't this problem be solved by having a certain transduction delay (signal transduction, synaptic transmission) in both connections?  
> **username_0**: Yes I just got that answer from a friend in the field as well. It seems to be the solution - aligning the STDP function not on time 0 but rather on a certain -Xms to the left. Do you have any idea what transmission delay is reasonable?  
> **username_1**: This surely depends on what you intend to model (i.e., cells, which part of the brain). Unfortunately, I do not have a reverence discussing this topic at hand.  
> **username_2**: A synapse does not exist in isolation in the brain. That does limit how correlated the firing rates of two neurons can become. Furthermore, reciprocal connectivity is may be to the same neurons but not the same place on the neuron. For example neuron A could synapse far from B's soma but B could synapse close to A's soma.  
> **username_3**: Not all synapses use STDP as a learning rule.  Some synapses have normal Hebbian learning.  

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## 1 answers

### Answer by username_4 on 2012-07-25. Score: 2

Consider that elimination of such a phenomena is not ideal. It's been proposed that actual neuronal networks exist under the tension of synchronous decoupling.

<http://www.sciencedirect.com/science/article/pii/S0896627308001281>

To answer your question though, you probably should consider that reciprocal connections might not be between two individual neurons. Rather, axons in the cortex are clumped into minicolumns, each one having 20+ pyramidals in supragranular layers. It's conjecture, but it's possible that the reciprocal connections are between different pyramidals within minicolumns (or even different pyramidals within columns or whatever other macro-structure you want).

> **Comments:**
> **username_5**: You are exactly right.  You should edit your answer to demonstrate more certainty: reciprocity is not a property of single neurons.  It's a property of nuclei.  But a caveat... this is only for chemical synapses.  Electrically coupled (gap junction) neurons can be reciprocal.   An another caveat.... retrograde signalers like THC act backwards on chemical synapses... so there is *some* form of reciprocity at the single neuron level, but it's not part of standard models really.  And what they do is modulate the connection... not send electrical signals.  
> **username_4**: @Xurtio I see. I was equivocating a bit because I couldn't produce a direct source. It's just inference from other things I know of the nervous system. I'll try and find sources and edit the answer to be more certain.  

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