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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.


Examples of activity in the network.(A–C) Top: Mean maximal firing rate per theta cycle (average over five trials), outlining the average activity during theta cycles, in the parameter space of gE and gI. Center and bottom: Raster plots (center) and population-average firing rates (bottom) of all cells in selected locations of the E-I parameter space during 16 consecutive θ cycles. Action potentials and firing rates of E and I cells are colored red and blue, respectively. An arrow highlights the position in the parameter space.DOI:http://dx.doi.org/10.7554/eLife.06444.018
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fig5s1: Examples of activity in the network.(A–C) Top: Mean maximal firing rate per theta cycle (average over five trials), outlining the average activity during theta cycles, in the parameter space of gE and gI. Center and bottom: Raster plots (center) and population-average firing rates (bottom) of all cells in selected locations of the E-I parameter space during 16 consecutive θ cycles. Action potentials and firing rates of E and I cells are colored red and blue, respectively. An arrow highlights the position in the parameter space.DOI:http://dx.doi.org/10.7554/eLife.06444.018

Mentions: To investigate how addition of noise promotes emergence of network attractor states we investigated the dynamics of neurons in the simulated circuits. We focus initially on the point in parameter space identified in Figure 2Ab, where grids are found in the presence of moderate noise, and bumps are found when noise is moderate or high. When we examined times of action potentials generated by all neurons in this circuit, we find that in the absence of noise the network generates hyper-synchronous seizure-like states at the start of each theta cycle (Figure 5A and Figure 5—figure supplement 1A). The number of E cells active on each theta cycle differs, but their activity is typically restricted to the rising phase of theta, and there is no consistent structure in the pattern of activated neurons. The number of simultaneously active I cells is also greatest at the start of each theta cycle. The I-cells continue to fire over the theta cycle, but their synchronization declines. When moderate noise is added to the circuit only a subset of E-cells are active on each theta cycle, forming an activity bump (Figure 5B and Figure 5—figure supplement 1B). The I-cells are active at gamma frequency and the formation of an activity bump in the E-cell population is reflected by an inverted bump in the I-cell population activity (Figure 5B). With increased noise there is a similar overall pattern of activity, but spike timing becomes more variable, causing the bumps to drift and reducing the degree of synchronization at gamma frequencies (Figure 5C and Figure 5—figure supplement 1C).10.7554/eLife.06444.017Figure 5.Noise opposes generation of seizure-like states.


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

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

Examples of activity in the network.(A–C) Top: Mean maximal firing rate per theta cycle (average over five trials), outlining the average activity during theta cycles, in the parameter space of gE and gI. Center and bottom: Raster plots (center) and population-average firing rates (bottom) of all cells in selected locations of the E-I parameter space during 16 consecutive θ cycles. Action potentials and firing rates of E and I cells are colored red and blue, respectively. An arrow highlights the position in the parameter space.DOI:http://dx.doi.org/10.7554/eLife.06444.018
© Copyright Policy
Related In: Results  -  Collection

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

fig5s1: Examples of activity in the network.(A–C) Top: Mean maximal firing rate per theta cycle (average over five trials), outlining the average activity during theta cycles, in the parameter space of gE and gI. Center and bottom: Raster plots (center) and population-average firing rates (bottom) of all cells in selected locations of the E-I parameter space during 16 consecutive θ cycles. Action potentials and firing rates of E and I cells are colored red and blue, respectively. An arrow highlights the position in the parameter space.DOI:http://dx.doi.org/10.7554/eLife.06444.018
Mentions: To investigate how addition of noise promotes emergence of network attractor states we investigated the dynamics of neurons in the simulated circuits. We focus initially on the point in parameter space identified in Figure 2Ab, where grids are found in the presence of moderate noise, and bumps are found when noise is moderate or high. When we examined times of action potentials generated by all neurons in this circuit, we find that in the absence of noise the network generates hyper-synchronous seizure-like states at the start of each theta cycle (Figure 5A and Figure 5—figure supplement 1A). The number of E cells active on each theta cycle differs, but their activity is typically restricted to the rising phase of theta, and there is no consistent structure in the pattern of activated neurons. The number of simultaneously active I cells is also greatest at the start of each theta cycle. The I-cells continue to fire over the theta cycle, but their synchronization declines. When moderate noise is added to the circuit only a subset of E-cells are active on each theta cycle, forming an activity bump (Figure 5B and Figure 5—figure supplement 1B). The I-cells are active at gamma frequency and the formation of an activity bump in the E-cell population is reflected by an inverted bump in the I-cell population activity (Figure 5B). With increased noise there is a similar overall pattern of activity, but spike timing becomes more variable, causing the bumps to drift and reducing the degree of synchronization at gamma frequencies (Figure 5C and Figure 5—figure supplement 1C).10.7554/eLife.06444.017Figure 5.Noise opposes generation of seizure-like states.

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.