Dynamic excitatory and inhibitory gain modulation can produce flexible, robust and optimal decision-making

<div><p>Behavioural and neurophysiological studies in primates have increasingly shown the involvement of urgency signals during the temporal integration of sensory evidence in perceptual decision-making. Neuronal correlates of such signals have been found in the parietal cortex, and in separate studies, demonstrated attention-induced gain modulation of both excitatory and inhibitory neurons. Although previous computational models of decision-making have incorporated gain modulation, their abstract forms do not permit an understanding of the contribution of inhibitory gain modulation. Thus, the effects of co-modulating both excitatory and inhibitory neuronal gains on decision-making dynamics and behavioural performance remain unclear. In this work, we incorporate time-dependent co-modulation of the gains of both excitatory and inhibitory neurons into our previous biologically based decision circuit model. We base our computational study in the context of two classic motion-discrimination tasks performed in animals. Our model shows that by simultaneously increasing the gains of both excitatory and inhibitory neurons, a variety of the observed dynamic neuronal firing activities can be replicated. In particular, the model can exhibit winner-take-all decision-making behaviour with higher firing rates and within a significantly more robust model parameter range. It also exhibits short-tailed reaction time distributions even when operating near a dynamical bifurcation point. The model further shows that neuronal gain modulation can compensate for weaker recurrent excitation in a decision neural circuit, and support decision formation and storage. Higher neuronal gain is also suggested in the more cognitively demanding reaction time than in the fixed delay version of the task. Using the exact temporal delays from the animal experiments, fast recruitment of gain co-modulation is shown to maximize reward rate, with a timescale that is surprisingly near the experimentally fitted value. Our work provides insights into the simultaneous and rapid modulation of excitatory and inhibitory neuronal gains, which enables flexible, robust, and optimal decision-making.</p></div

Sandy ideas and coloured days: Some computational implications of embodiment

This paper is an exploration of the relationship between language and vision from the perspective of language evolution on the one hand, and metaphor on the other. Recent research has suggested that the origins of human language capacity can be traced to the evolution of a region in the brain that permits the interaction of information from sensory and motor cortices. In light of this, it is hypothesised that the computational mechanisms of language are derived from those of the sensory-motor domain, and that the pervasiveness of metaphor is one manifestation of language's computational antecedants. A variety of cognitive and computational implications are drawn from these hypotheses

Distributed representations of action sequences in anterior cingulate cortex: A recurrent neural network approach

Anterior cingulate cortex (ACC) has been the subject of intense debate over the past 2 decades, but its specific computational function remains controversial. Here we present a simple computational model of ACC that incorporates distributed representations across a network of interconnected processing units. Based on the proposal that ACC is concerned with the execution of extended, goal-directed action sequences, we trained a recurrent neural network to predict each successive step of several sequences associated with multiple tasks. In keeping with neurophysiological observations from nonhuman animals, the network yields distributed patterns of activity across ACC neurons that track the progression of each sequence, and in keeping with human neuroimaging data, the network produces discrepancy signals when any step of the sequence deviates from the predicted step. These simulations illustrate a novel approach for investigating ACC function

Opponency Revisited: Competition and Cooperation Between Dopamine and Serotonin

Affective valence lies on a spectrum ranging from punishment to reward. The coding of such spectra in the brain almost always involves opponency between pairs of systems or structures. There is ample evidence for the role of dopamine in the appetitive half of this spectrum, but little agreement about the existence, nature, or role of putative aversive opponents such as serotonin. In this review, we consider the structure of opponency in terms of previous biases about the nature of the decision problems that animals face, the conflicts that may thus arise between Pavlovian and instrumental responses, and an additional spectrum joining invigoration to inhibition. We use this analysis to shed light on aspects of the role of serotonin and its interactions with dopamine