Limits...
Synaptic plasticity enables adaptive self-tuning critical networks.

Stepp N, Plenz D, Srinivasa N - PLoS Comput. Biol. (2015)

Bottom Line: We show that a combination of short- and long-term synaptic plasticity enables these networks to exhibit criticality in the face of intrinsic, i.e. self-sustained, asynchronous spiking.Brief external perturbations lead to adaptive, long-term modification of intrinsic network connectivity through long-term excitatory plasticity, whereas long-term inhibitory plasticity enables rapid self-tuning of the network back to a critical state.The critical state is characterized by a branching parameter oscillating around unity, a critical exponent close to -3/2 and a long tail distribution of a self-similarity parameter between 0.5 and 1.

View Article: PubMed Central - PubMed

Affiliation: Center for Neural and Emergent Systems, Information and System Sciences Lab, HRL Laboratories LLC, Malibu, California, United States of America.

ABSTRACT
During rest, the mammalian cortex displays spontaneous neural activity. Spiking of single neurons during rest has been described as irregular and asynchronous. In contrast, recent in vivo and in vitro population measures of spontaneous activity, using the LFP, EEG, MEG or fMRI suggest that the default state of the cortex is critical, manifested by spontaneous, scale-invariant, cascades of activity known as neuronal avalanches. Criticality keeps a network poised for optimal information processing, but this view seems to be difficult to reconcile with apparently irregular single neuron spiking. Here, we simulate a 10,000 neuron, deterministic, plastic network of spiking neurons. We show that a combination of short- and long-term synaptic plasticity enables these networks to exhibit criticality in the face of intrinsic, i.e. self-sustained, asynchronous spiking. Brief external perturbations lead to adaptive, long-term modification of intrinsic network connectivity through long-term excitatory plasticity, whereas long-term inhibitory plasticity enables rapid self-tuning of the network back to a critical state. The critical state is characterized by a branching parameter oscillating around unity, a critical exponent close to -3/2 and a long tail distribution of a self-similarity parameter between 0.5 and 1.

Show MeSH
The difference in entropy ΔH between excitatory and inhibitory synaptic conductance changes due to STDP and change in synaptic efficacy due to STP.A) During early states, STDP is primarily responsible for changes to synaptic connection strength (ΔH > 0). Otherwise, STP appears more active (ΔH < 0) when the network is at rest. B) During strong perturbations, I-STDP becomes much more active than STP.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4295840&req=5

pcbi.1004043.g010: The difference in entropy ΔH between excitatory and inhibitory synaptic conductance changes due to STDP and change in synaptic efficacy due to STP.A) During early states, STDP is primarily responsible for changes to synaptic connection strength (ΔH > 0). Otherwise, STP appears more active (ΔH < 0) when the network is at rest. B) During strong perturbations, I-STDP becomes much more active than STP.

Mentions: Synaptic plasticity appears to be responsible for a network’s ability to self-tune, both for maintenance of background activity and reaction to strong perturbations. Given two types of plasticity at work, we judged their relative roles by examining the Shannon entropy of their respective changes to synaptic weight (see Methods). Fig. 10 shows the entropy of synaptic efficacy, produced by STP, and conductance change, produced by STDP, as they change over time. A greater entropy suggests greater role of the corresponding plasticity mechanism. Our analysis shows that during early states of the simulation, STDP is primarily responsible for changes to synaptic connection strength. STP dominates when the network is at rest. During strong perturbations, I-STDP becomes more active than STP, further demonstrating the importance of I-STDP to maintain the network at criticality.


Synaptic plasticity enables adaptive self-tuning critical networks.

Stepp N, Plenz D, Srinivasa N - PLoS Comput. Biol. (2015)

The difference in entropy ΔH between excitatory and inhibitory synaptic conductance changes due to STDP and change in synaptic efficacy due to STP.A) During early states, STDP is primarily responsible for changes to synaptic connection strength (ΔH > 0). Otherwise, STP appears more active (ΔH < 0) when the network is at rest. B) During strong perturbations, I-STDP becomes much more active than STP.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4295840&req=5

pcbi.1004043.g010: The difference in entropy ΔH between excitatory and inhibitory synaptic conductance changes due to STDP and change in synaptic efficacy due to STP.A) During early states, STDP is primarily responsible for changes to synaptic connection strength (ΔH > 0). Otherwise, STP appears more active (ΔH < 0) when the network is at rest. B) During strong perturbations, I-STDP becomes much more active than STP.
Mentions: Synaptic plasticity appears to be responsible for a network’s ability to self-tune, both for maintenance of background activity and reaction to strong perturbations. Given two types of plasticity at work, we judged their relative roles by examining the Shannon entropy of their respective changes to synaptic weight (see Methods). Fig. 10 shows the entropy of synaptic efficacy, produced by STP, and conductance change, produced by STDP, as they change over time. A greater entropy suggests greater role of the corresponding plasticity mechanism. Our analysis shows that during early states of the simulation, STDP is primarily responsible for changes to synaptic connection strength. STP dominates when the network is at rest. During strong perturbations, I-STDP becomes more active than STP, further demonstrating the importance of I-STDP to maintain the network at criticality.

Bottom Line: We show that a combination of short- and long-term synaptic plasticity enables these networks to exhibit criticality in the face of intrinsic, i.e. self-sustained, asynchronous spiking.Brief external perturbations lead to adaptive, long-term modification of intrinsic network connectivity through long-term excitatory plasticity, whereas long-term inhibitory plasticity enables rapid self-tuning of the network back to a critical state.The critical state is characterized by a branching parameter oscillating around unity, a critical exponent close to -3/2 and a long tail distribution of a self-similarity parameter between 0.5 and 1.

View Article: PubMed Central - PubMed

Affiliation: Center for Neural and Emergent Systems, Information and System Sciences Lab, HRL Laboratories LLC, Malibu, California, United States of America.

ABSTRACT
During rest, the mammalian cortex displays spontaneous neural activity. Spiking of single neurons during rest has been described as irregular and asynchronous. In contrast, recent in vivo and in vitro population measures of spontaneous activity, using the LFP, EEG, MEG or fMRI suggest that the default state of the cortex is critical, manifested by spontaneous, scale-invariant, cascades of activity known as neuronal avalanches. Criticality keeps a network poised for optimal information processing, but this view seems to be difficult to reconcile with apparently irregular single neuron spiking. Here, we simulate a 10,000 neuron, deterministic, plastic network of spiking neurons. We show that a combination of short- and long-term synaptic plasticity enables these networks to exhibit criticality in the face of intrinsic, i.e. self-sustained, asynchronous spiking. Brief external perturbations lead to adaptive, long-term modification of intrinsic network connectivity through long-term excitatory plasticity, whereas long-term inhibitory plasticity enables rapid self-tuning of the network back to a critical state. The critical state is characterized by a branching parameter oscillating around unity, a critical exponent close to -3/2 and a long tail distribution of a self-similarity parameter between 0.5 and 1.

Show MeSH