Inhibitory microcircuits for top-down plasticity of sensory representations (Wilmes & Clopath 2019)

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Accession:259546
"Rewards influence plasticity of early sensory representations, but the underlying changes in circuitry are unclear. Recent experimental findings suggest that inhibitory circuits regulate learning. In addition, inhibitory neurons are highly modulated by diverse long-range inputs, including reward signals. We, therefore, hypothesise that inhibitory plasticity plays a major role in adjusting stimulus representations. We investigate how top-down modulation by rewards interacts with local plasticity to induce long-lasting changes in circuitry. Using a computational model of layer 2/3 primary visual cortex, we demonstrate how interneuron circuits can store information about rewarded stimuli to instruct long-term changes in excitatory connectivity in the absence of further reward. In our model, stimulus-tuned somatostatin-positive interneurons develop strong connections to parvalbumin-positive interneurons during reward such that they selectively disinhibit the pyramidal layer henceforth. This triggers excitatory plasticity, leading to increased stimulus representation. We make specific testable predictions and show that this two-stage model allows for translation invariance of the learned representation."
Reference:
1 . Wilmes KA, Clopath C (2019) Inhibitory microcircuits for top-down plasticity of sensory representations. Nat Commun 10:5055 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism:
Cell Type(s): Neocortex L2/3 pyramidal GLU cell; Neocortex V1 interneuron chandelier SOM GABA cell; Neocortex V1 interneuron basket PV GABA cell; Neocortex V1 interneuron bipolar VIP/CR GABA cell; Abstract integrate-and-fire leaky neuron;
Channel(s):
Gap Junctions: Gap junctions;
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: Brian 2;
Model Concept(s): Sensory coding; Long-term Synaptic Plasticity;
Implementer(s): Wilmes, Katharina A. [katharina.wilmes at googlemail.com];
Search NeuronDB for information about:  Neocortex L2/3 pyramidal GLU cell; Neocortex V1 interneuron basket PV GABA cell; Neocortex V1 interneuron bipolar VIP/CR GABA cell; Neocortex V1 interneuron chandelier SOM GABA cell; 
1. System Requirements: >=Python 3.6, numpy, matplotlib, scipy, brian2, brian2tools, sacred

- install brian2: https://brian2.readthedocs.io/en/stable/introduction/install.html
- install brian2tools https://brian2.readthedocs.io/en/stable/introduction/install.html
- install sacred from the git repository: https://sacred.readthedocs.io/en/latest/quickstart.html
	git clone https://github.com/IDSIA/sacred.git
	cd sacred
	[sudo] python setup.py install


2. run run_code.py : 
this script runs the main model Spikingmodel.py and stores the results in a local folder './Spiking_model/2'
The script runs for approx. 48 minutes, depending on the computer. The results file results.pkl will have a size of 2GB.

3. When 2. is completed, run plot_Spikingmodel.py to plot the Figures from the data in results.pkl into the folder ./Spiking_model/2

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