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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.


Related in: MedlinePlus

Probability of bump formation and network activity plots in networks with structured E → E and unstructured E → I and I → E connections.Since the presence of bump attractors is necessary for grid computation, we tested whether networks with only structured E-E connections can generate activity bumps. We used the Gaussian fitting procedure (cf. ‘Materials and methods’) to estimate the presence of bump attractors in these networks. (A) Probability of bump formation as a function of the E-E synaptic scaling factor (gE → E) and the width of the synaptic profile (σE → E). Arrow highlights the position in the parameter space corresponding to the raster plots (center) and network activity snapshots (bottom) for E and I cells. Firing rate in the network activity color plots are in the range of 0 (dark blue) to the maximum firing rate indicated to the right of the plot (dark red). In these networks gE = 1 nS and gI = 0.1 nS. (B) Same as (A) but gE = 3 nS and gI = 1 nS. (C) Same as (A) and (B) but in these simulations the synaptic scaling factor of E-E connections and the width of the synaptic profile were fixed (gE → E = 3 nS and σE → E = 0.0833) and gE and gI varied in the range of 0–6 nS. Simulations that produced excessive spiking activity and did not finish in a specified time limit (3 hr) are indicated by white color. Many networks suffer from runaway excitation and inhibition (A) or generate only background synaptic activity characterized by low firing rates of E and I cells (B and C). The Gaussian fitting procedure used to estimate the probability of bump formation can nevertheless yield a high bump score due to the fact that this procedure can also give a high score to intermittent pockets of activity (A) or pockets of background activity of E cells (B and C). This activity, however, is not stable enough to generate grid firing fields.DOI:http://dx.doi.org/10.7554/eLife.06444.035
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fig7s10: Probability of bump formation and network activity plots in networks with structured E → E and unstructured E → I and I → E connections.Since the presence of bump attractors is necessary for grid computation, we tested whether networks with only structured E-E connections can generate activity bumps. We used the Gaussian fitting procedure (cf. ‘Materials and methods’) to estimate the presence of bump attractors in these networks. (A) Probability of bump formation as a function of the E-E synaptic scaling factor (gE → E) and the width of the synaptic profile (σE → E). Arrow highlights the position in the parameter space corresponding to the raster plots (center) and network activity snapshots (bottom) for E and I cells. Firing rate in the network activity color plots are in the range of 0 (dark blue) to the maximum firing rate indicated to the right of the plot (dark red). In these networks gE = 1 nS and gI = 0.1 nS. (B) Same as (A) but gE = 3 nS and gI = 1 nS. (C) Same as (A) and (B) but in these simulations the synaptic scaling factor of E-E connections and the width of the synaptic profile were fixed (gE → E = 3 nS and σE → E = 0.0833) and gE and gI varied in the range of 0–6 nS. Simulations that produced excessive spiking activity and did not finish in a specified time limit (3 hr) are indicated by white color. Many networks suffer from runaway excitation and inhibition (A) or generate only background synaptic activity characterized by low firing rates of E and I cells (B and C). The Gaussian fitting procedure used to estimate the probability of bump formation can nevertheless yield a high bump score due to the fact that this procedure can also give a high score to intermittent pockets of activity (A) or pockets of background activity of E cells (B and C). This activity, however, is not stable enough to generate grid firing fields.DOI:http://dx.doi.org/10.7554/eLife.06444.035

Mentions: Finally, we asked if addition of synaptic connections between excitatory cells modifies the relationship between gamma, noise, gE and gI. While the E-I model is consistent with the connectivity between stellate cells in layer 2 of the MEC, adjacent pyramidal cells may also have grid firing properties. Unlike stellate cells, pyramidal cells interact with one another directly via excitatory connections and indirectly via inhibitory interneurons (Couey et al., 2013). To assess the impact of E-E connections, we first extended the E-I model to allow each E cell to excite other E cells that are nearby in neuron space. The dependence of grid firing, gamma oscillations, and bump formation on noise, gE and gI was similar to E-I networks (Figure 7—figure supplements 6–9). We also attempted to evaluate networks in which E-E connections were structured, but E-I and I-E connections were uniformly distributed. However, in these networks we were unable to identify parameters that support formation of stable activity bumps (Figure 7—figure supplement 10). This is consistent with instability of simpler network attractors based on E-E connections (Seung et al., 2000).


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

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

Probability of bump formation and network activity plots in networks with structured E → E and unstructured E → I and I → E connections.Since the presence of bump attractors is necessary for grid computation, we tested whether networks with only structured E-E connections can generate activity bumps. We used the Gaussian fitting procedure (cf. ‘Materials and methods’) to estimate the presence of bump attractors in these networks. (A) Probability of bump formation as a function of the E-E synaptic scaling factor (gE → E) and the width of the synaptic profile (σE → E). Arrow highlights the position in the parameter space corresponding to the raster plots (center) and network activity snapshots (bottom) for E and I cells. Firing rate in the network activity color plots are in the range of 0 (dark blue) to the maximum firing rate indicated to the right of the plot (dark red). In these networks gE = 1 nS and gI = 0.1 nS. (B) Same as (A) but gE = 3 nS and gI = 1 nS. (C) Same as (A) and (B) but in these simulations the synaptic scaling factor of E-E connections and the width of the synaptic profile were fixed (gE → E = 3 nS and σE → E = 0.0833) and gE and gI varied in the range of 0–6 nS. Simulations that produced excessive spiking activity and did not finish in a specified time limit (3 hr) are indicated by white color. Many networks suffer from runaway excitation and inhibition (A) or generate only background synaptic activity characterized by low firing rates of E and I cells (B and C). The Gaussian fitting procedure used to estimate the probability of bump formation can nevertheless yield a high bump score due to the fact that this procedure can also give a high score to intermittent pockets of activity (A) or pockets of background activity of E cells (B and C). This activity, however, is not stable enough to generate grid firing fields.DOI:http://dx.doi.org/10.7554/eLife.06444.035
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Related In: Results  -  Collection

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fig7s10: Probability of bump formation and network activity plots in networks with structured E → E and unstructured E → I and I → E connections.Since the presence of bump attractors is necessary for grid computation, we tested whether networks with only structured E-E connections can generate activity bumps. We used the Gaussian fitting procedure (cf. ‘Materials and methods’) to estimate the presence of bump attractors in these networks. (A) Probability of bump formation as a function of the E-E synaptic scaling factor (gE → E) and the width of the synaptic profile (σE → E). Arrow highlights the position in the parameter space corresponding to the raster plots (center) and network activity snapshots (bottom) for E and I cells. Firing rate in the network activity color plots are in the range of 0 (dark blue) to the maximum firing rate indicated to the right of the plot (dark red). In these networks gE = 1 nS and gI = 0.1 nS. (B) Same as (A) but gE = 3 nS and gI = 1 nS. (C) Same as (A) and (B) but in these simulations the synaptic scaling factor of E-E connections and the width of the synaptic profile were fixed (gE → E = 3 nS and σE → E = 0.0833) and gE and gI varied in the range of 0–6 nS. Simulations that produced excessive spiking activity and did not finish in a specified time limit (3 hr) are indicated by white color. Many networks suffer from runaway excitation and inhibition (A) or generate only background synaptic activity characterized by low firing rates of E and I cells (B and C). The Gaussian fitting procedure used to estimate the probability of bump formation can nevertheless yield a high bump score due to the fact that this procedure can also give a high score to intermittent pockets of activity (A) or pockets of background activity of E cells (B and C). This activity, however, is not stable enough to generate grid firing fields.DOI:http://dx.doi.org/10.7554/eLife.06444.035
Mentions: Finally, we asked if addition of synaptic connections between excitatory cells modifies the relationship between gamma, noise, gE and gI. While the E-I model is consistent with the connectivity between stellate cells in layer 2 of the MEC, adjacent pyramidal cells may also have grid firing properties. Unlike stellate cells, pyramidal cells interact with one another directly via excitatory connections and indirectly via inhibitory interneurons (Couey et al., 2013). To assess the impact of E-E connections, we first extended the E-I model to allow each E cell to excite other E cells that are nearby in neuron space. The dependence of grid firing, gamma oscillations, and bump formation on noise, gE and gI was similar to E-I networks (Figure 7—figure supplements 6–9). We also attempted to evaluate networks in which E-E connections were structured, but E-I and I-E connections were uniformly distributed. However, in these networks we were unable to identify parameters that support formation of stable activity bumps (Figure 7—figure supplement 10). This is consistent with instability of simpler network attractors based on E-E connections (Seung et al., 2000).

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