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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.


Firing rates of E cells.(A) Average firing rate of all E cells during simulations of animal movement as a function of gE and gI. Black lines outline the region from Figure 2D–F where gridness score = 0.5. (B) Relationship between gridness score and firing frequency of E cells.DOI:http://dx.doi.org/10.7554/eLife.06444.022
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fig6s3: Firing rates of E cells.(A) Average firing rate of all E cells during simulations of animal movement as a function of gE and gI. Black lines outline the region from Figure 2D–F where gridness score = 0.5. (B) Relationship between gridness score and firing frequency of E cells.DOI:http://dx.doi.org/10.7554/eLife.06444.022

Mentions: Our analysis points towards suppression of seizure-like events as the mechanism by which moderate noise promotes grid firing, while interactions between noise and theta appear important for the capacity to multiplex grid firing with a wide range of gamma frequencies. However, we wanted to know if other factors might contribute to these beneficial roles of noise. Grid fields may also fail to form if overall activity levels are too low, in which case neurons with grid fields instead encode head direction (Bonnevie et al., 2013). This observation is unlikely to explain our results as the mean firing rate of E cells in networks that generated grid firing fields (grid score >0.5, networks with gE or gI set to 0 excluded) was in fact lower than the firing rate of networks without grid fields (1.2; 1.0; 1.0 Hz grid fields vs 3.0; 2.7; 1.2 Hz no grid fields, in networks with σ = 0; 150; 300 pA respectively). There was also no systematic relationship between grid score and firing frequency (Figure 6—figure supplement 3). We also wanted to know if other properties of grid fields vary as a function of gE and gI. Parameters used to calibrate velocity integration by the grid network varied very little with changes in gE and gI (Figure 6—figure supplement 4), whereas drift increased with gI (Figure 4—figure supplement 1) and place cell input was most effective in opposing attractor drift in noisy networks with high gridness scores (Figure 6—figure supplement 5). These data are consistent with suppression of seizure like events as the mechanism by which noise promotes grid firing, while interactions between noise and theta frequency inputs profoundly influence the dynamics of attractor networks that 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)

Firing rates of E cells.(A) Average firing rate of all E cells during simulations of animal movement as a function of gE and gI. Black lines outline the region from Figure 2D–F where gridness score = 0.5. (B) Relationship between gridness score and firing frequency of E cells.DOI:http://dx.doi.org/10.7554/eLife.06444.022
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4508578&req=5

fig6s3: Firing rates of E cells.(A) Average firing rate of all E cells during simulations of animal movement as a function of gE and gI. Black lines outline the region from Figure 2D–F where gridness score = 0.5. (B) Relationship between gridness score and firing frequency of E cells.DOI:http://dx.doi.org/10.7554/eLife.06444.022
Mentions: Our analysis points towards suppression of seizure-like events as the mechanism by which moderate noise promotes grid firing, while interactions between noise and theta appear important for the capacity to multiplex grid firing with a wide range of gamma frequencies. However, we wanted to know if other factors might contribute to these beneficial roles of noise. Grid fields may also fail to form if overall activity levels are too low, in which case neurons with grid fields instead encode head direction (Bonnevie et al., 2013). This observation is unlikely to explain our results as the mean firing rate of E cells in networks that generated grid firing fields (grid score >0.5, networks with gE or gI set to 0 excluded) was in fact lower than the firing rate of networks without grid fields (1.2; 1.0; 1.0 Hz grid fields vs 3.0; 2.7; 1.2 Hz no grid fields, in networks with σ = 0; 150; 300 pA respectively). There was also no systematic relationship between grid score and firing frequency (Figure 6—figure supplement 3). We also wanted to know if other properties of grid fields vary as a function of gE and gI. Parameters used to calibrate velocity integration by the grid network varied very little with changes in gE and gI (Figure 6—figure supplement 4), whereas drift increased with gI (Figure 4—figure supplement 1) and place cell input was most effective in opposing attractor drift in noisy networks with high gridness scores (Figure 6—figure supplement 5). These data are consistent with suppression of seizure like events as the mechanism by which noise promotes grid firing, while interactions between noise and theta frequency inputs profoundly influence the dynamics of attractor networks that 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.