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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.


Amplitude and frequency of gamma oscillations in the gE and gI parameter regions where grid fields are robust.Amplitude (top) and frequency (bottom) of detected gamma oscillations for simulations in which gridness score is greater than 0.5, in deterministic networks (A), networks with an intermediate level of noise (B) and in networks with the highest simulated level of noise (C). The data in this figure are from simulations in Figure 3.DOI:http://dx.doi.org/10.7554/eLife.06444.016
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fig3s4: Amplitude and frequency of gamma oscillations in the gE and gI parameter regions where grid fields are robust.Amplitude (top) and frequency (bottom) of detected gamma oscillations for simulations in which gridness score is greater than 0.5, in deterministic networks (A), networks with an intermediate level of noise (B) and in networks with the highest simulated level of noise (C). The data in this figure are from simulations in Figure 3.DOI:http://dx.doi.org/10.7554/eLife.06444.016

Mentions: When we considered only regions of parameter space that generate robust grid fields (grid score >0.5), we found circuits generating almost the complete observed range of gamma amplitudes (0.02 < autocorrelation peak < 0.59) and frequencies (31 Hz < frequency < 102 Hz) (Figure 3—figure supplement 4). For example, considering the crescent shaped region of E-I space that supports grid firing in the presence of intermediate noise (the region within the isocline in Figure 3E), when gI is high and gE low then the amplitude of gamma is relatively low and the frequency high. Moving towards the region where gI is high and gE is low, the amplitude of gamma is increased and the frequency is reduced. Thus, variation of synaptic strength across this region of E-I space can be used to tune the properties of gamma activity while maintaining the ability of the network to generate grid fields.


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

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

Amplitude and frequency of gamma oscillations in the gE and gI parameter regions where grid fields are robust.Amplitude (top) and frequency (bottom) of detected gamma oscillations for simulations in which gridness score is greater than 0.5, in deterministic networks (A), networks with an intermediate level of noise (B) and in networks with the highest simulated level of noise (C). The data in this figure are from simulations in Figure 3.DOI:http://dx.doi.org/10.7554/eLife.06444.016
© Copyright Policy
Related In: Results  -  Collection

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

fig3s4: Amplitude and frequency of gamma oscillations in the gE and gI parameter regions where grid fields are robust.Amplitude (top) and frequency (bottom) of detected gamma oscillations for simulations in which gridness score is greater than 0.5, in deterministic networks (A), networks with an intermediate level of noise (B) and in networks with the highest simulated level of noise (C). The data in this figure are from simulations in Figure 3.DOI:http://dx.doi.org/10.7554/eLife.06444.016
Mentions: When we considered only regions of parameter space that generate robust grid fields (grid score >0.5), we found circuits generating almost the complete observed range of gamma amplitudes (0.02 < autocorrelation peak < 0.59) and frequencies (31 Hz < frequency < 102 Hz) (Figure 3—figure supplement 4). For example, considering the crescent shaped region of E-I space that supports grid firing in the presence of intermediate noise (the region within the isocline in Figure 3E), when gI is high and gE low then the amplitude of gamma is relatively low and the frequency high. Moving towards the region where gI is high and gE is low, the amplitude of gamma is increased and the frequency is reduced. Thus, variation of synaptic strength across this region of E-I space can be used to tune the properties of gamma activity while maintaining the ability of the network to generate grid fields.

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.