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Self-organization in Balanced State Networks by STDP and Homeostatic Plasticity.

Effenberger F, Jost J, Levina A - PLoS Comput. Biol. (2015)

Bottom Line: Coincident spiking activity of several driver cells can evoke population bursts and driver cells have similar dynamical properties as leader neurons found experimentally.Our model allows us to observe the delicate interplay between structural and dynamical properties of the emergent inhomogeneities.It is simple, robust to parameter changes and able to explain a multitude of different experimental findings in one basic network.

View Article: PubMed Central - PubMed

Affiliation: Max-Planck-Institute for Mathematics in the Sciences, Leipzig, Germany.

ABSTRACT
Structural inhomogeneities in synaptic efficacies have a strong impact on population response dynamics of cortical networks and are believed to play an important role in their functioning. However, little is known about how such inhomogeneities could evolve by means of synaptic plasticity. Here we present an adaptive model of a balanced neuronal network that combines two different types of plasticity, STDP and synaptic scaling. The plasticity rules yield both long-tailed distributions of synaptic weights and firing rates. Simultaneously, a highly connected subnetwork of driver neurons with strong synapses emerges. Coincident spiking activity of several driver cells can evoke population bursts and driver cells have similar dynamical properties as leader neurons found experimentally. Our model allows us to observe the delicate interplay between structural and dynamical properties of the emergent inhomogeneities. It is simple, robust to parameter changes and able to explain a multitude of different experimental findings in one basic network.

No MeSH data available.


Related in: MedlinePlus

Stationary weight of a plastic inhibitory synapse after plasticity.Average stationary weight after 100 seconds of simulation of a 2-neuron model (blue) and its standard deviation (green) as a function of initial postsynaptic firing rate. Analytic solution for the stationary weight (magenta).
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pcbi.1004420.g009: Stationary weight of a plastic inhibitory synapse after plasticity.Average stationary weight after 100 seconds of simulation of a 2-neuron model (blue) and its standard deviation (green) as a function of initial postsynaptic firing rate. Analytic solution for the stationary weight (magenta).

Mentions: Note that the stationary weight depends only on the quotient of A+ and A−, in our case A+/A− = 4. The analytic solution provides a very good fit to the data, Fig 9. Differences observed for large rates are due to the restriction on the maximal inhibitory weight. To find the actual rate ν of the postsynaptic neuron, taking into account the inhibitory synapse, we combine Eqs (2) and (1)ν(ν0)=ν01+νinhdw(wstat(ν0),ν0)=ν01+τνinhlog(A+/A-),where νinh is the rate of the inhibitory neuron.


Self-organization in Balanced State Networks by STDP and Homeostatic Plasticity.

Effenberger F, Jost J, Levina A - PLoS Comput. Biol. (2015)

Stationary weight of a plastic inhibitory synapse after plasticity.Average stationary weight after 100 seconds of simulation of a 2-neuron model (blue) and its standard deviation (green) as a function of initial postsynaptic firing rate. Analytic solution for the stationary weight (magenta).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4559467&req=5

pcbi.1004420.g009: Stationary weight of a plastic inhibitory synapse after plasticity.Average stationary weight after 100 seconds of simulation of a 2-neuron model (blue) and its standard deviation (green) as a function of initial postsynaptic firing rate. Analytic solution for the stationary weight (magenta).
Mentions: Note that the stationary weight depends only on the quotient of A+ and A−, in our case A+/A− = 4. The analytic solution provides a very good fit to the data, Fig 9. Differences observed for large rates are due to the restriction on the maximal inhibitory weight. To find the actual rate ν of the postsynaptic neuron, taking into account the inhibitory synapse, we combine Eqs (2) and (1)ν(ν0)=ν01+νinhdw(wstat(ν0),ν0)=ν01+τνinhlog(A+/A-),where νinh is the rate of the inhibitory neuron.

Bottom Line: Coincident spiking activity of several driver cells can evoke population bursts and driver cells have similar dynamical properties as leader neurons found experimentally.Our model allows us to observe the delicate interplay between structural and dynamical properties of the emergent inhomogeneities.It is simple, robust to parameter changes and able to explain a multitude of different experimental findings in one basic network.

View Article: PubMed Central - PubMed

Affiliation: Max-Planck-Institute for Mathematics in the Sciences, Leipzig, Germany.

ABSTRACT
Structural inhomogeneities in synaptic efficacies have a strong impact on population response dynamics of cortical networks and are believed to play an important role in their functioning. However, little is known about how such inhomogeneities could evolve by means of synaptic plasticity. Here we present an adaptive model of a balanced neuronal network that combines two different types of plasticity, STDP and synaptic scaling. The plasticity rules yield both long-tailed distributions of synaptic weights and firing rates. Simultaneously, a highly connected subnetwork of driver neurons with strong synapses emerges. Coincident spiking activity of several driver cells can evoke population bursts and driver cells have similar dynamical properties as leader neurons found experimentally. Our model allows us to observe the delicate interplay between structural and dynamical properties of the emergent inhomogeneities. It is simple, robust to parameter changes and able to explain a multitude of different experimental findings in one basic network.

No MeSH data available.


Related in: MedlinePlus