Limits...
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.


Related in: MedlinePlus

Sensitivity of bump attractor spontaneous drift to variations in gE and gI and noise levels.(A) Schematic of the bump attractor drift estimation procedure. The first 500 ms of a simulation trial are used to initialize the bump attractor. Onset of theta modulated input current was at 500 ms. The estimated centers of bump attractors measured by the least squares fit of symmetric Gaussians were at 1 s (initial position) and 9 s (final position). The drift was then estimated as the distance on twisted torus between the initial and final position. Simulation time was 10 s. (B) Color plots show bump attractor drifts averaged over five simulation trials, for the simulated ranges of excitatory and inhibitory synaptic strengths and levels of noise. Networks without noise can form stable bump attractors in a subset of their parameter region. Networks with noise suffer from attractor drift in majority of the parameter region. Black lines in (B) indicate the region from Figure 2D–F where gridness score = 0.5.DOI:http://dx.doi.org/10.7554/eLife.06444.011
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4508578&req=5

fig4s1: Sensitivity of bump attractor spontaneous drift to variations in gE and gI and noise levels.(A) Schematic of the bump attractor drift estimation procedure. The first 500 ms of a simulation trial are used to initialize the bump attractor. Onset of theta modulated input current was at 500 ms. The estimated centers of bump attractors measured by the least squares fit of symmetric Gaussians were at 1 s (initial position) and 9 s (final position). The drift was then estimated as the distance on twisted torus between the initial and final position. Simulation time was 10 s. (B) Color plots show bump attractor drifts averaged over five simulation trials, for the simulated ranges of excitatory and inhibitory synaptic strengths and levels of noise. Networks without noise can form stable bump attractors in a subset of their parameter region. Networks with noise suffer from attractor drift in majority of the parameter region. Black lines in (B) indicate the region from Figure 2D–F where gridness score = 0.5.DOI:http://dx.doi.org/10.7554/eLife.06444.011

Mentions: In noisy networks the presence of low grid scores for networks with high bump scores (Figure 4C) is explained by sensitivity of these network configurations to noise-induced drift. This is illustrated by the region of parameter space from Figure 2Ab, where gI is relatively high and gE relatively low, and which in deterministic simulations fails to generate bumps or grids. With moderate noise, this point generates bumps that show little drift (Figure 4Ac), whereas as noise is increased further the bump begins to drift (Figure 4Ae). In contrast, at the point illustrated in Figure 2Aa, which forms grids and bumps in the presence or absence of noise, activity bumps are relatively stable in each condition (Figure 4Aa,d), although drift increases with greater noise (Figure 4—figure supplement 1). Thus, intrinsic noise has two opposing effects on bump formation. For much of the parameter space we consider moderate noise promotes emergence of bumps and grids, while across all of parameter space noise reduces bump stability leading to deterioration of grids.


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

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

Sensitivity of bump attractor spontaneous drift to variations in gE and gI and noise levels.(A) Schematic of the bump attractor drift estimation procedure. The first 500 ms of a simulation trial are used to initialize the bump attractor. Onset of theta modulated input current was at 500 ms. The estimated centers of bump attractors measured by the least squares fit of symmetric Gaussians were at 1 s (initial position) and 9 s (final position). The drift was then estimated as the distance on twisted torus between the initial and final position. Simulation time was 10 s. (B) Color plots show bump attractor drifts averaged over five simulation trials, for the simulated ranges of excitatory and inhibitory synaptic strengths and levels of noise. Networks without noise can form stable bump attractors in a subset of their parameter region. Networks with noise suffer from attractor drift in majority of the parameter region. Black lines in (B) indicate the region from Figure 2D–F where gridness score = 0.5.DOI:http://dx.doi.org/10.7554/eLife.06444.011
© Copyright Policy
Related In: Results  -  Collection

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

fig4s1: Sensitivity of bump attractor spontaneous drift to variations in gE and gI and noise levels.(A) Schematic of the bump attractor drift estimation procedure. The first 500 ms of a simulation trial are used to initialize the bump attractor. Onset of theta modulated input current was at 500 ms. The estimated centers of bump attractors measured by the least squares fit of symmetric Gaussians were at 1 s (initial position) and 9 s (final position). The drift was then estimated as the distance on twisted torus between the initial and final position. Simulation time was 10 s. (B) Color plots show bump attractor drifts averaged over five simulation trials, for the simulated ranges of excitatory and inhibitory synaptic strengths and levels of noise. Networks without noise can form stable bump attractors in a subset of their parameter region. Networks with noise suffer from attractor drift in majority of the parameter region. Black lines in (B) indicate the region from Figure 2D–F where gridness score = 0.5.DOI:http://dx.doi.org/10.7554/eLife.06444.011
Mentions: In noisy networks the presence of low grid scores for networks with high bump scores (Figure 4C) is explained by sensitivity of these network configurations to noise-induced drift. This is illustrated by the region of parameter space from Figure 2Ab, where gI is relatively high and gE relatively low, and which in deterministic simulations fails to generate bumps or grids. With moderate noise, this point generates bumps that show little drift (Figure 4Ac), whereas as noise is increased further the bump begins to drift (Figure 4Ae). In contrast, at the point illustrated in Figure 2Aa, which forms grids and bumps in the presence or absence of noise, activity bumps are relatively stable in each condition (Figure 4Aa,d), although drift increases with greater noise (Figure 4—figure supplement 1). Thus, intrinsic noise has two opposing effects on bump formation. For much of the parameter space we consider moderate noise promotes emergence of bumps and grids, while across all of parameter space noise reduces bump stability leading to deterioration of grids.

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.


Related in: MedlinePlus