Attentional selection and communication through coherence: Scope and limitations

Abstract

Synchronous neural oscillations are strongly associated with a variety of perceptual, cognitive, and behavioural processes. It has been proposed that the role of the synchronous oscillations in these processes is to facilitate information transmission between brain areas, the 'communication through coherence,' or CTC hypothesis. The details of how this mechanism would work, however, and its causal status, are still unclear. Here we investigate computationally a proposed mechanism for selective attention that directly implicates the CTC as causal. The mechanism involves alpha band (about 10 Hz) oscillations, originating in the pulvinar nucleus of the thalamus, being sent to communicating cortical areas, organizing gamma (about 40 Hz) oscillations there, and thus facilitating phase coherence and communication between them. This is proposed to happen contingent on control signals sent from higher-level cortical areas to the thalamic reticular nucleus, which controls the alpha oscillations sent to cortex by the pulvinar. We studied the scope of this mechanism in parameter space, and limitations implied by this scope, using a computational implementation of our conceptual model. Our results indicate that, although the CTC-based mechanism can account for some effects of top-down and bottom-up attentional selection, its limitations indicate that an alternative mechanism, in which oscillatory coherence is caused by communication between brain areas rather than being a causal factor for it, might operate in addition to, or even instead of, the CTC mechanism.

Fig 1. Representative results of [30] (and the current study) demonstrating single realizations of gamma frequency ‘cortical’ quasi-cycle excitatory processes (V1E and V2E), with noisy alpha sine waves added to their respective, connected, inhibitory processes (V1I and V2I).

The V2E process is comprised of its own oscillations plus 0.5 times that of V1E with a lag of Δψ = 0 radians. Signal = 20 onset occurs at 5 x 10⁴ timepoints and continues until end of the run. The location and extent of maximum signal onset effect, ‘max sigoe,’ and mean signal onset effect, ‘extended sigoe,’ are indicated in the figure. See text for how the onset effects are computed. (A) Phase offset of the alpha waves is Δϕ = 0; max sigoe = 24.5, extended sigoe = 8.3, phase coherence between V1E and V2E ρ = 0.64. (B) Phase offset of the alpha waves is Δϕ = π; max sigoe = 15.35, extended sigoe = 3.76, ρ = 0.51.

Fig 2. Results of [30] demonstrating the effects of phase offsets of alpha (Δϕ) and gamma (Δψ) oscillations on (C) phase coherence of the combined oscillations, (D) maximum signal onset effect, and (E) mean signal onset effect.

Fig 1 displays representations of these effects. The gamma phase offset was Δψ = 0. Thus, the maximum phase coherence and signal onset effects occur near Δϕ = 0. Reproduced from Fig 4 of [30] with permission.

Fig 3. Structural model of top-down and bottom-up attentional selection.

EI pairs are red (excitatory, E) and blue (inhibitory, I) disks; red arrows are excitatory, blue arrows are inhibitory, black arrows represent input signals, or stimuli,—they add increments in neural activity to the processes to which they connect. Top-down signals, green arrows, excite thalamic reticular nucleus (TRN) inhibitory neurons in the same way as do inputs from V1. The (visual) signals excite the V1 neurons that are tuned to them, e.g., vertical (say, signal X) or horizontal (say signal Y) lines on the retina excite V1 neurons tuned to vertical or horizontal lines respectively via tuned neurons in the LGN. See text for more detailed explanation.

Fig 4. Results of simulations of model in Fig 3 using selected values of the parameters appearing in Eqs (4)–(7).

The graphs depict the effects of cortical connectivity and cortical noise on the four outcome variables. Each point represents the average of 10 realizations. The standard errors are approximately the size of the data points or smaller and thus are not displayed. Parameter values other than the one changed are those in Row 1 of Table 2. The arrow points to an outcome listed in Table 2 for the indicated value of the changing parameter. MI = mutual information.

Fig 5. Results of simulations of model in Fig 3 using selected values of the parameters appearing in Eqs (4)—(7).

The graphs depict the effects of signal amplitude and signal noise on the four outcome variables. Each point represents the average of 10 realizations. The standard errors are approximately the size of the data points or smaller and thus are not displayed. Parameter values other than the one changed are those in Row 1 of Table 2. The arrow points to an outcome listed in Table 2 for the indicated value of the changing parameter. MI = mutual information.

Fig 6. Results of single realizations of simulations with gamma oscillations only in the V1 process.

The V2E process is comprised of random noise plus 0.5 times that of V1E with a lag of Δψ = 0 radians (9), whereas the V2I process is comprised of random noise only (10). Note that V2E and V2I are not connected in these simulations. Signal = 20 onset occurs at 5 x 10⁴ timepoints and continues until end of the run. (A) No alpha input to V1 or to V2 processes: max sigoe = -36.8, extended sigoe = -5.7, phase coherence between V1E and V2E ρ = 0.8, MI = 2.8. (B) Alpha input only to V1I process: max sigoe = -35.4, extended sigoe = -7.3, ρ = 0.76, MI = 1.9.