Limits...
Noise promotes independent control of gamma oscillations and grid firing within recurrent attractor networks.

Solanka L, van Rossum MC, Nolan MF - Elife (2015)

Bottom Line: Neural computations underlying cognitive functions require calibration of the strength of excitatory and inhibitory synaptic connections and are associated with modulation of gamma frequency oscillations in network activity.This beneficial role for noise results from disruption of epileptic-like network states.Our results have implications for tuning of normal circuit function and for disorders associated with changes in gamma oscillations and synaptic strength.

View Article: PubMed Central - PubMed

Affiliation: Centre for Integrative Physiology, University of Edinburgh, Edinburgh, United Kingdom.

ABSTRACT
Neural computations underlying cognitive functions require calibration of the strength of excitatory and inhibitory synaptic connections and are associated with modulation of gamma frequency oscillations in network activity. However, principles relating gamma oscillations, synaptic strength and circuit computations are unclear. We address this in attractor network models that account for grid firing and theta-nested gamma oscillations in the medial entorhinal cortex. We show that moderate intrinsic noise massively increases the range of synaptic strengths supporting gamma oscillations and grid computation. With moderate noise, variation in excitatory or inhibitory synaptic strength tunes the amplitude and frequency of gamma activity without disrupting grid firing. This beneficial role for noise results from disruption of epileptic-like network states. Thus, moderate noise promotes independent control of multiplexed firing rate- and gamma-based computational mechanisms. Our results have implications for tuning of normal circuit function and for disorders associated with changes in gamma oscillations and synaptic strength.

No MeSH data available.


Spatial firing fields in networks that contain recurrent I → I synapses.(A–C) Example spatial firing fields (left) and spatial autocorrelation plots (right) for networks with gE = 3 nS and gI = 1 nS (A) and networks with gE = 1 nS and gI = 3 nS (B), corresponding to the three simulated noise levels indicated by σ. Maximal firing rate is indicated to the top right of each spatial firing plot. Range of spatial autocorrelations is normalized between 0 and 1.DOI:http://dx.doi.org/10.7554/eLife.06444.026
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4508578&req=5

fig7s1: Spatial firing fields in networks that contain recurrent I → I synapses.(A–C) Example spatial firing fields (left) and spatial autocorrelation plots (right) for networks with gE = 3 nS and gI = 1 nS (A) and networks with gE = 1 nS and gI = 3 nS (B), corresponding to the three simulated noise levels indicated by σ. Maximal firing rate is indicated to the top right of each spatial firing plot. Range of spatial autocorrelations is normalized between 0 and 1.DOI:http://dx.doi.org/10.7554/eLife.06444.026


Noise promotes independent control of gamma oscillations and grid firing within recurrent attractor networks.

Solanka L, van Rossum MC, Nolan MF - Elife (2015)

Spatial firing fields in networks that contain recurrent I → I synapses.(A–C) Example spatial firing fields (left) and spatial autocorrelation plots (right) for networks with gE = 3 nS and gI = 1 nS (A) and networks with gE = 1 nS and gI = 3 nS (B), corresponding to the three simulated noise levels indicated by σ. Maximal firing rate is indicated to the top right of each spatial firing plot. Range of spatial autocorrelations is normalized between 0 and 1.DOI:http://dx.doi.org/10.7554/eLife.06444.026
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4508578&req=5

fig7s1: Spatial firing fields in networks that contain recurrent I → I synapses.(A–C) Example spatial firing fields (left) and spatial autocorrelation plots (right) for networks with gE = 3 nS and gI = 1 nS (A) and networks with gE = 1 nS and gI = 3 nS (B), corresponding to the three simulated noise levels indicated by σ. Maximal firing rate is indicated to the top right of each spatial firing plot. Range of spatial autocorrelations is normalized between 0 and 1.DOI:http://dx.doi.org/10.7554/eLife.06444.026
Bottom Line: Neural computations underlying cognitive functions require calibration of the strength of excitatory and inhibitory synaptic connections and are associated with modulation of gamma frequency oscillations in network activity.This beneficial role for noise results from disruption of epileptic-like network states.Our results have implications for tuning of normal circuit function and for disorders associated with changes in gamma oscillations and synaptic strength.

View Article: PubMed Central - PubMed

Affiliation: Centre for Integrative Physiology, University of Edinburgh, Edinburgh, United Kingdom.

ABSTRACT
Neural computations underlying cognitive functions require calibration of the strength of excitatory and inhibitory synaptic connections and are associated with modulation of gamma frequency oscillations in network activity. However, principles relating gamma oscillations, synaptic strength and circuit computations are unclear. We address this in attractor network models that account for grid firing and theta-nested gamma oscillations in the medial entorhinal cortex. We show that moderate intrinsic noise massively increases the range of synaptic strengths supporting gamma oscillations and grid computation. With moderate noise, variation in excitatory or inhibitory synaptic strength tunes the amplitude and frequency of gamma activity without disrupting grid firing. This beneficial role for noise results from disruption of epileptic-like network states. Thus, moderate noise promotes independent control of multiplexed firing rate- and gamma-based computational mechanisms. Our results have implications for tuning of normal circuit function and for disorders associated with changes in gamma oscillations and synaptic strength.

No MeSH data available.