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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.


(A) Histogram of velocities of a simulated animal.(B) Histogram of bump speeds derived from the animal velocities estimated in Equation 21, for different grid field spacings.DOI:http://dx.doi.org/10.7554/eLife.06444.042
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fig8: (A) Histogram of velocities of a simulated animal.(B) Histogram of bump speeds derived from the animal velocities estimated in Equation 21, for different grid field spacings.DOI:http://dx.doi.org/10.7554/eLife.06444.042

Mentions: Estimate the range of bump speeds that need to be covered (Appendix figure 1).(21)sbumpi=Nxλ gridsanimali,where si are the speeds of the animal/bump, estimated from forward differences of the trajectory of the simulated animal, Nx is the horizontal size of the neural sheet (neurons), and λgrid is the grid field spacing (cm). These speeds will form a distribution of bump speeds that the attractor must achieve in order to path integrate without error (Appendix figure 1B).10.7554/eLife.06444.042Appendix figure 1.(A) Histogram of velocities of a simulated animal.


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

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

(A) Histogram of velocities of a simulated animal.(B) Histogram of bump speeds derived from the animal velocities estimated in Equation 21, for different grid field spacings.DOI:http://dx.doi.org/10.7554/eLife.06444.042
© Copyright Policy
Related In: Results  -  Collection

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

fig8: (A) Histogram of velocities of a simulated animal.(B) Histogram of bump speeds derived from the animal velocities estimated in Equation 21, for different grid field spacings.DOI:http://dx.doi.org/10.7554/eLife.06444.042
Mentions: Estimate the range of bump speeds that need to be covered (Appendix figure 1).(21)sbumpi=Nxλ gridsanimali,where si are the speeds of the animal/bump, estimated from forward differences of the trajectory of the simulated animal, Nx is the horizontal size of the neural sheet (neurons), and λgrid is the grid field spacing (cm). These speeds will form a distribution of bump speeds that the attractor must achieve in order to path integrate without error (Appendix figure 1B).10.7554/eLife.06444.042Appendix figure 1.(A) Histogram of velocities of a simulated animal.

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.