Shashaank Vattikuti , Phyllis Thangaraj, Hua W. Xie, Stephen J. Gotts, Alex Martin, Carson C. Chow. Canonical Cortical Circuit Model Explains Rivalry, Intermittent Rivalry, and Rivalry Memory. PLoS Computational Biology (2016).
It has been shown that the same canonical cortical circuit model with mutual inhibition and a fatigue process can explain perceptual rivalry and other neurophysiological responses to a range of static stimuli. However, it has been proposed that this model cannot explain responses to dynamic inputs such as found in intermittent rivalry and rivalry memory, where maintenance of a percept when the stimulus is absent is required. This challenges the universality of the basic canonical cortical circuit. Here, we show that by including an overlooked realistic small nonspecific background neural activity, the same basic model can reproduce intermittent rivalry and rivalry memory without compromising static rivalry and other cortical phenomena. The background activity induces a mutual-inhibition mechanism for short-term memory, which is robust to noise and where fine-tuning of recurrent excitation or inclusion of sub-threshold currents or synaptic facilitation is unnecessary. We prove existence conditions for the mechanism and show that it can explain experimental results from the quartet apparent motion illusion, which is a prototypical intermittent rivalry stimulus.
When the brain is presented with an ambiguous stimulus like the Necker cube or what is known as the quartet illusion, the perception will alternate or rival between the possible interpretations. There are neurons in the brain whose activity is correlated with the perception and not the stimulus. Hence, perceptual rivalry provides a unique probe of cortical function and could possibly serve as a diagnostic tool for cognitive disorders such as autism. A mathematical model based on the known biology of the brain has been developed to account for perceptual rivalry when the stimulus is static. The basic model also accounts for other neural responses to stimuli that do not elicit rivalry. However, these models cannot explain illusions where the stimulus is intermittently switched on and off and the same perception returns after an off period because there is no built-in mechanism to hold the memory. Here, we show that the inclusion of experimentally observed low-level background neural activity is sufficient to explain rivalry for static inputs, and rivalry for intermittent inputs. We validate the model with new experiments.
This paper is the latest of a continuing series of papers outlining how a canonical cortical circuit of excitatory and inhibitory cells can explain psychophysical and electrophysiological data of perceptual and cortical dynamics under a wide range of stimuli and conditions. I’ve summarized some of the work before (e.g. see here). In this particular paper, we show how the same circuit previously shown to explain winner-take-all behavior, normalization, and oscillations at various time scales, can also possess memory in the absence of input. Previous work has shown that if you have a circuit with effective mutual inhibition between two pools representing different percepts and include some type of fatigue process such as synaptic depression or spike frequency adaptation, then the circuit exhibits various dynamics depending on the parameters and input conditions. If the inhibition strength is relatively low and the two pools receive equal inputs then the model will have a symmetric fixed point where both pools are equally active. As the inhibition strength (or input strength) increases, then there can be a bifurcation to oscillations between the two pools with a frequency that is dependent on the strengths of inhibition, recurrent excitation, input, and the time constant of the fatigue process. A further increase in inhibition leads to a bifurcation to a winner-take-all (WTA) state where one of the pools dominates the others. However, the same circuit would be expected to not possess “rivalry memory”, where the same percept returns after the stimulus is completely removed for a duration that is long compared to the average oscillation period (dominance time). The reason is that during rivalry, the dominant pool is weakened while the suppressed pool is strengthened by the fatigue process. Thus when the stimulus is removed and returned, the suppressed pool would be expected to win the competition and become dominant. This reasoning had led people, including myself, to believe that rivalry memory could not be explained by this same model.
However, one thing Shashaank observed and that I hadn’t really noticed before was that the winner-take-all state can persist for arbitrarily low input strength. We prove a little theorem in the paper showing that if the gain function (or FI curve) is concave (i.e. does not bend up), then the winner-take-all will persist for arbitrarily low input if the inhibition is strong enough. Most importantly, the input does not need to be tuned and could be provided by the natural background activity known to exist in the brain. Even zero mean noise is sufficient to maintain the WTA state. This low-activity WTA state can then serve as a memory since whatever was active during a state with strong input can remain active when the input is turned off and the neurons just receive low level background activity. It is thus a purely mutual inhibition maintained memory. We dubbed this “topological memory” because it is like a kink in the carpet that never disappears and persists over a wide range of parameter values and input strengths. Although, we only consider rivalry memory in this paper, the mechanism could also apply in other contexts such as working memory. In this paper, we also focus on a specific rivalry illusion called the quartet illusion, which makes the model slightly more complicated but we show how it naturally reduces to a two pool model. We are currently finishing a paper quantifying precisely how excitatory and inhibitory strengths affect rivalry and other cortical phenomena so watch this space. We also have submitted an abstract to neuroscience demonstrating how you can get WTA and rivalry in a balanced-state network.
Update: link to paper is fixed.