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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.


Sensitivity of grid firing to changes in feedback inhibition, excitation and noise levels in networks with connection probability between pairs of neurons drawn according to the synaptic profile functions in Figure 1B.(A–C) Example spatial firing fields (left) and spatial autocorrelation plots (right) of E and I cells for networks without noise (A; σ = 0 pA), with noise set to σ = 150 pA (B), and noise set to σ = 300 pA (C) and with the strengths of recurrent synaptic connections indicated by arrows in (D–F). Maximal firing rate is indicated in the top right of each spatial firing plot. The range of spatial autocorrelations is normalized between 0 and 1. (D–F) Gridness score as a function of gE and gI for networks with each noise level. Each item in the color plot is an average gridness score of two simulation runs. Arrows indicate the positions of grid field and autocorrelation examples from simulations illustrated in (A–C). Simulations that did not finish in a specified time interval (5 hr) are indicated by white color.DOI:http://dx.doi.org/10.7554/eLife.06444.006
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fig2s1: Sensitivity of grid firing to changes in feedback inhibition, excitation and noise levels in networks with connection probability between pairs of neurons drawn according to the synaptic profile functions in Figure 1B.(A–C) Example spatial firing fields (left) and spatial autocorrelation plots (right) of E and I cells for networks without noise (A; σ = 0 pA), with noise set to σ = 150 pA (B), and noise set to σ = 300 pA (C) and with the strengths of recurrent synaptic connections indicated by arrows in (D–F). Maximal firing rate is indicated in the top right of each spatial firing plot. The range of spatial autocorrelations is normalized between 0 and 1. (D–F) Gridness score as a function of gE and gI for networks with each noise level. Each item in the color plot is an average gridness score of two simulation runs. Arrows indicate the positions of grid field and autocorrelation examples from simulations illustrated in (A–C). Simulations that did not finish in a specified time interval (5 hr) are indicated by white color.DOI:http://dx.doi.org/10.7554/eLife.06444.006

Mentions: When intrinsic noise was increased further, to 300 pA, the parameter space that supports grid firing was reduced in line with our initial expectations (Figure 2Ca,b,F and Supplementary file 1I–L). To systematically explore the range of gE and gI over which the network is most sensitive to the beneficial effects of noise we subtracted grid scores for simulations with 150 pA noise from scores with deterministic simulations (Figure 2G). This revealed that the unexpected beneficial effect of noise was primarily in the region of the parameter space where recurrent inhibition was strong. In this region, increasing noise above a threshold led to high grid scores, while further increases in noise progressively impaired grid firing (Figure 2H). In probabilistically connected networks, the range of gE and gI supporting grid firing was reduced, but the shape of the parameter space and dependence on noise was similar to the standard networks (Figure 2—figure supplement 1), indicating that the dependence of grid firing on gE and gI, and the effects of noise, are independent of the detailed implementation of the E-I attractor networks.


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 grid firing to changes in feedback inhibition, excitation and noise levels in networks with connection probability between pairs of neurons drawn according to the synaptic profile functions in Figure 1B.(A–C) Example spatial firing fields (left) and spatial autocorrelation plots (right) of E and I cells for networks without noise (A; σ = 0 pA), with noise set to σ = 150 pA (B), and noise set to σ = 300 pA (C) and with the strengths of recurrent synaptic connections indicated by arrows in (D–F). Maximal firing rate is indicated in the top right of each spatial firing plot. The range of spatial autocorrelations is normalized between 0 and 1. (D–F) Gridness score as a function of gE and gI for networks with each noise level. Each item in the color plot is an average gridness score of two simulation runs. Arrows indicate the positions of grid field and autocorrelation examples from simulations illustrated in (A–C). Simulations that did not finish in a specified time interval (5 hr) are indicated by white color.DOI:http://dx.doi.org/10.7554/eLife.06444.006
© Copyright Policy
Related In: Results  -  Collection

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

fig2s1: Sensitivity of grid firing to changes in feedback inhibition, excitation and noise levels in networks with connection probability between pairs of neurons drawn according to the synaptic profile functions in Figure 1B.(A–C) Example spatial firing fields (left) and spatial autocorrelation plots (right) of E and I cells for networks without noise (A; σ = 0 pA), with noise set to σ = 150 pA (B), and noise set to σ = 300 pA (C) and with the strengths of recurrent synaptic connections indicated by arrows in (D–F). Maximal firing rate is indicated in the top right of each spatial firing plot. The range of spatial autocorrelations is normalized between 0 and 1. (D–F) Gridness score as a function of gE and gI for networks with each noise level. Each item in the color plot is an average gridness score of two simulation runs. Arrows indicate the positions of grid field and autocorrelation examples from simulations illustrated in (A–C). Simulations that did not finish in a specified time interval (5 hr) are indicated by white color.DOI:http://dx.doi.org/10.7554/eLife.06444.006
Mentions: When intrinsic noise was increased further, to 300 pA, the parameter space that supports grid firing was reduced in line with our initial expectations (Figure 2Ca,b,F and Supplementary file 1I–L). To systematically explore the range of gE and gI over which the network is most sensitive to the beneficial effects of noise we subtracted grid scores for simulations with 150 pA noise from scores with deterministic simulations (Figure 2G). This revealed that the unexpected beneficial effect of noise was primarily in the region of the parameter space where recurrent inhibition was strong. In this region, increasing noise above a threshold led to high grid scores, while further increases in noise progressively impaired grid firing (Figure 2H). In probabilistically connected networks, the range of gE and gI supporting grid firing was reduced, but the shape of the parameter space and dependence on noise was similar to the standard networks (Figure 2—figure supplement 1), indicating that the dependence of grid firing on gE and gI, and the effects of noise, are independent of the detailed implementation of the E-I attractor networks.

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.