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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.


Scatter plots of gridness score as a function of the detected oscillation frequency.(A–C) The plots show relationships between grid field computations (gridness score) and the frequency of gamma oscillations for deterministic networks (A), networks with moderate noise (B) and networks with the highest simulated noise level (C). Noise level is indicated by σ. The frequency of the oscillation was obtained by computing autocorrelation functions of inhibitory currents impinging onto 25 randomly selected E cells in the network and detecting their first local maxima. The time lag at the first local maximum yielded the frequency of the oscillation, which is plotted on the abscissa. Color coding determines the values of gE and gI, as shown in the 2D colorbar.DOI:http://dx.doi.org/10.7554/eLife.06444.015
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fig3s3: Scatter plots of gridness score as a function of the detected oscillation frequency.(A–C) The plots show relationships between grid field computations (gridness score) and the frequency of gamma oscillations for deterministic networks (A), networks with moderate noise (B) and networks with the highest simulated noise level (C). Noise level is indicated by σ. The frequency of the oscillation was obtained by computing autocorrelation functions of inhibitory currents impinging onto 25 randomly selected E cells in the network and detecting their first local maxima. The time lag at the first local maximum yielded the frequency of the oscillation, which is plotted on the abscissa. Color coding determines the values of gE and gI, as shown in the 2D colorbar.DOI:http://dx.doi.org/10.7554/eLife.06444.015


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

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

Scatter plots of gridness score as a function of the detected oscillation frequency.(A–C) The plots show relationships between grid field computations (gridness score) and the frequency of gamma oscillations for deterministic networks (A), networks with moderate noise (B) and networks with the highest simulated noise level (C). Noise level is indicated by σ. The frequency of the oscillation was obtained by computing autocorrelation functions of inhibitory currents impinging onto 25 randomly selected E cells in the network and detecting their first local maxima. The time lag at the first local maximum yielded the frequency of the oscillation, which is plotted on the abscissa. Color coding determines the values of gE and gI, as shown in the 2D colorbar.DOI:http://dx.doi.org/10.7554/eLife.06444.015
© Copyright Policy
Related In: Results  -  Collection

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

fig3s3: Scatter plots of gridness score as a function of the detected oscillation frequency.(A–C) The plots show relationships between grid field computations (gridness score) and the frequency of gamma oscillations for deterministic networks (A), networks with moderate noise (B) and networks with the highest simulated noise level (C). Noise level is indicated by σ. The frequency of the oscillation was obtained by computing autocorrelation functions of inhibitory currents impinging onto 25 randomly selected E cells in the network and detecting their first local maxima. The time lag at the first local maximum yielded the frequency of the oscillation, which is plotted on the abscissa. Color coding determines the values of gE and gI, as shown in the 2D colorbar.DOI:http://dx.doi.org/10.7554/eLife.06444.015
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.