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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.


Calibration of the gain of the velocity inputs in networks that contain direct I → I synapses.(A–C) Bump attractor speed as a function of the strength of the velocity current for the three simulated levels of noise indicated by σ. Values of gE and gI are indicated by arrows in (D–I). 10 simulation runs were performed for each level of noise (blue markers). In each run the speed of the bump was calculated in response to the injected velocity input and the data were used to fit a linear relationship using an estimation procedure outlined in Appendix 1 (black line). (D–F) Slope of the estimated velocity gain of the attractor networks as a function of gE and gI for all simulated levels of noise. (G–I) Same as in (D–F) but the plots show error of fit for the estimated linear relationships. Arrows in (D–I) show locations of the data plotted in (A–C). Black lines in (D–I) indicate the region from Figure 7A–C where gridness score = 0.5.DOI:http://dx.doi.org/10.7554/eLife.06444.029
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fig7s4: Calibration of the gain of the velocity inputs in networks that contain direct I → I synapses.(A–C) Bump attractor speed as a function of the strength of the velocity current for the three simulated levels of noise indicated by σ. Values of gE and gI are indicated by arrows in (D–I). 10 simulation runs were performed for each level of noise (blue markers). In each run the speed of the bump was calculated in response to the injected velocity input and the data were used to fit a linear relationship using an estimation procedure outlined in Appendix 1 (black line). (D–F) Slope of the estimated velocity gain of the attractor networks as a function of gE and gI for all simulated levels of noise. (G–I) Same as in (D–F) but the plots show error of fit for the estimated linear relationships. Arrows in (D–I) show locations of the data plotted in (A–C). Black lines in (D–I) indicate the region from Figure 7A–C where gridness score = 0.5.DOI:http://dx.doi.org/10.7554/eLife.06444.029


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

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

Calibration of the gain of the velocity inputs in networks that contain direct I → I synapses.(A–C) Bump attractor speed as a function of the strength of the velocity current for the three simulated levels of noise indicated by σ. Values of gE and gI are indicated by arrows in (D–I). 10 simulation runs were performed for each level of noise (blue markers). In each run the speed of the bump was calculated in response to the injected velocity input and the data were used to fit a linear relationship using an estimation procedure outlined in Appendix 1 (black line). (D–F) Slope of the estimated velocity gain of the attractor networks as a function of gE and gI for all simulated levels of noise. (G–I) Same as in (D–F) but the plots show error of fit for the estimated linear relationships. Arrows in (D–I) show locations of the data plotted in (A–C). Black lines in (D–I) indicate the region from Figure 7A–C where gridness score = 0.5.DOI:http://dx.doi.org/10.7554/eLife.06444.029
© Copyright Policy
Related In: Results  -  Collection

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

fig7s4: Calibration of the gain of the velocity inputs in networks that contain direct I → I synapses.(A–C) Bump attractor speed as a function of the strength of the velocity current for the three simulated levels of noise indicated by σ. Values of gE and gI are indicated by arrows in (D–I). 10 simulation runs were performed for each level of noise (blue markers). In each run the speed of the bump was calculated in response to the injected velocity input and the data were used to fit a linear relationship using an estimation procedure outlined in Appendix 1 (black line). (D–F) Slope of the estimated velocity gain of the attractor networks as a function of gE and gI for all simulated levels of noise. (G–I) Same as in (D–F) but the plots show error of fit for the estimated linear relationships. Arrows in (D–I) show locations of the data plotted in (A–C). Black lines in (D–I) indicate the region from Figure 7A–C where gridness score = 0.5.DOI:http://dx.doi.org/10.7554/eLife.06444.029
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.