Dysfunction in learning and motivational systems are thought to contribute to addictive behaviours. Previous models have suggested that dopaminergic roles in learning and motivation could produce addictive behaviours through pharmacological manipulations that provide excess dopaminergic signalling towards these learning and motivational systems. Redish (2004) suggested a role based on dopaminergic signals of value prediction error, while (Zhang et al., 2009) suggested a role based on dopaminergic signals of motivation. However, both models present significant limitations. They do not explain the reduced sensitivity to drug-related costs/negative consequences, the increased impulsivity generally found in people with a substance use disorder, craving behaviours, and non-pharmacological dependence, all of which are key hallmarks of addictive behaviours. Here, we propose a novel mathematical definition of salience, that combines aspects of dopamine's role in both learning and motivation within the reinforcement learning framework. Using a single parameter regime, we simulated addictive behaviours that the (Zhang et al., 2009; Redish, 2004) models also produce but we went further in simulating the downweighting of drug-related negative prediction-errors, steeper delay discounting of drug rewards, craving behaviours and aspects of behavioural/non-pharmacological addictions. The current salience model builds on our recently proposed conceptual theory that salience modulates internal representation updating and may contribute to addictive behaviours by producing misaligned internal representations (Kalhan et al., 2021). Critically, our current mathematical model of salience argues that the seemingly disparate learning and motivational aspects of dopaminergic functioning may interact through a salience mechanism that modulates internal representation updating.

Poor context integration, the process of incorporating both previous and current information in decision making, is a cognitive symptom of schizophrenia. The maintenance of the contextual information has been shown to be sensitive to changes in excitation-inhibition (EI) balance. Many regions of the brain are sensitive to EI imbalances, however, so it is unknown how systemic manipulations affect the specific regions that are important to context integration. We constructed a multi-structure, biophysically-realistic agent that could perform context-integration as is assessed by the dot pattern expectancy task. The agent included a perceptual network, a memory network, and a decision making system and was capable of successfully performing the dot pattern expectancy task. Systemic manipulation of the agent's EI balance produced localized dysfunction of the memory structure, which resulted in schizophrenia-like deficits at context integration. When the agent's pyramidal cells were less excitatory, the agent fixated upon the cue and initiated responding later than the default agent, which were like the deficits one would predict that individuals on the autistic spectrum would make. This modelling suggests that it may be possible to parse between different types of context integration deficits by adding distractors to context integration tasks and by closely examining a participant's reaction times.

We suggest that the hippocampus plays two roles that allow rodents to solve the hidden-platform water maze: self-localization and route replay. When an animal explores an environment such as the water maze, the combination of place fields and correlational (Hebbian) long-term potentiation produces a weight matrix in the CA3 recurrent collaterals such that cells with overlapping place fields are more strongly interconnected than cells with nonoverlapping fields. When combined with global inhibition, this forms an attractor with coherent representations of position as stable states. When biased by local view information, this allows the animal to determine its position relative to the goal when it returns to the environment. We call this self-localization. When an animal traces specific routes within an environment, the weights in the CA3 recurrent collaterals become asymmetric. We show that this stores these routes in the recurrent collaterals. When primed with noise in the absence of sensory input, a coherent representation of position still forms in the CA3 population, but then that representation drifts, retracing a route. We show that these two mechanisms can coexist and form a basis for memory consolidation, explaining the anterograde and limited retrograde amnesia seen following hippocampal lesions.

O'Keefe (1991) has proposed that spatial information in rats might be represented as phasors: phase and amplitude of a sine wave encoding angle and distance to a landmark. We describe computer simulations showing that operations on phasors can be efficiently realized by arrays of spiking neurons that recode the temporal dimension of the sine wave spatially. Some cells in motor and parietal cortex exhibit response properties compatible with this proposal.