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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.


Continuous attractors in networks that contain direct E → E synapses.(A) Examples of E cell population firing rate snapshots from simulations in which velocity and place cell inputs are inactivated. Each row shows a simulation trial with a value of gE and gI highlighted by an arrow in panel (B). The corresponding probability of bump formation (P(bumps)) is indicated to the left. Maximal firing rate for each row is indicated to the right. (B) Color plots show probability of bump formation (P(bumps)), for the simulated range of gE and gI and the three simulated noise levels indicated by σ. Each color point is an average of five 10 s simulation runs. Arrows show positions in the parameter space of examples in (A). Black lines indicate the region from Figure 7—figure supplement 6 where the gridness score = 0.5.DOI:http://dx.doi.org/10.7554/eLife.06444.033
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fig7s8: Continuous attractors in networks that contain direct E → E synapses.(A) Examples of E cell population firing rate snapshots from simulations in which velocity and place cell inputs are inactivated. Each row shows a simulation trial with a value of gE and gI highlighted by an arrow in panel (B). The corresponding probability of bump formation (P(bumps)) is indicated to the left. Maximal firing rate for each row is indicated to the right. (B) Color plots show probability of bump formation (P(bumps)), for the simulated range of gE and gI and the three simulated noise levels indicated by σ. Each color point is an average of five 10 s simulation runs. Arrows show positions in the parameter space of examples in (A). Black lines indicate the region from Figure 7—figure supplement 6 where the gridness score = 0.5.DOI:http://dx.doi.org/10.7554/eLife.06444.033


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

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

Continuous attractors in networks that contain direct E → E synapses.(A) Examples of E cell population firing rate snapshots from simulations in which velocity and place cell inputs are inactivated. Each row shows a simulation trial with a value of gE and gI highlighted by an arrow in panel (B). The corresponding probability of bump formation (P(bumps)) is indicated to the left. Maximal firing rate for each row is indicated to the right. (B) Color plots show probability of bump formation (P(bumps)), for the simulated range of gE and gI and the three simulated noise levels indicated by σ. Each color point is an average of five 10 s simulation runs. Arrows show positions in the parameter space of examples in (A). Black lines indicate the region from Figure 7—figure supplement 6 where the gridness score = 0.5.DOI:http://dx.doi.org/10.7554/eLife.06444.033
© Copyright Policy
Related In: Results  -  Collection

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

fig7s8: Continuous attractors in networks that contain direct E → E synapses.(A) Examples of E cell population firing rate snapshots from simulations in which velocity and place cell inputs are inactivated. Each row shows a simulation trial with a value of gE and gI highlighted by an arrow in panel (B). The corresponding probability of bump formation (P(bumps)) is indicated to the left. Maximal firing rate for each row is indicated to the right. (B) Color plots show probability of bump formation (P(bumps)), for the simulated range of gE and gI and the three simulated noise levels indicated by σ. Each color point is an average of five 10 s simulation runs. Arrows show positions in the parameter space of examples in (A). Black lines indicate the region from Figure 7—figure supplement 6 where the gridness score = 0.5.DOI:http://dx.doi.org/10.7554/eLife.06444.033
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.