Reward signaling by the rodent medial frontal cortex

Generalized Simultaneous Localization and Mapping (G-SLAM) as unification framework for natural and artificial intelligences: towards reverse engineering the hippocampal/entorhinal system and principles of high-level cognition

Simultaneous localization and mapping (SLAM) represents a fundamental problem for autonomous embodied systems, for which the hippocampal/entorhinal system (H/E-S) has been optimized over the course of evolution. We have developed a biologically-inspired SLAM architecture based on latent variable generative modeling within the Free Energy Principle and Active Inference (FEP-AI) framework, which affords flexible navigation and planning in mobile robots. We have primarily focused on attempting to reverse engineer H/E-S "design" properties, but here we consider ways in which SLAM principles from robotics may help us better understand nervous systems and emergent minds. After reviewing LatentSLAM and notable features of this control architecture, we consider how the H/E-S may realize these functional properties not only for physical navigation, but also with respect to high-level cognition understood as generalized simultaneous localization and mapping (G-SLAM). We focus on loop-closure, graph-relaxation, and node duplication as particularly impactful architectural features, suggesting these computational phenomena may contribute to understanding cognitive insight (as proto-causal-inference), accommodation (as integration into existing schemas), and assimilation (as category formation). All these operations can similarly be describable in terms of structure/category learning on multiple levels of abstraction. However, here we adopt an ecological rationality perspective, framing H/E-S functions as orchestrating SLAM processes within both concrete and abstract hypothesis spaces. In this navigation/search process, adaptive cognitive equilibration between assimilation and accommodation involves balancing tradeoffs between exploration and exploitation; this dynamic equilibrium may be near optimally realized in FEP-AI, wherein control systems governed by expected free energy objective functions naturally balance model simplicity and accuracy. With respect to structure learning, such a balance would involve constructing models and categories that are neither too inclusive nor exclusive. We propose these (generalized) SLAM phenomena may represent some of the most impactful sources of variation in cognition both within and between individuals, suggesting that modulators of H/E-S functioning may potentially illuminate their adaptive significances as fundamental cybernetic control parameters. Finally, we discuss how understanding H/E-S contributions to G-SLAM may provide a unifying framework for high-level cognition and its potential realization in artificial intelligences.

Dissociable contributions of phasic dopamine activity to reward and prediction

Sensory cues that precede reward acquire predictive (expected value) and incentive (drive reward-seeking action) properties. Mesolimbic dopamine neurons' responses to sensory cues correlate with both expected value and reward-seeking action. This has led to the proposal that phasic dopamine responses may be sufficient to inform value-based decisions, elicit actions, and/or induce motivational states; however, causal tests are incomplete. Here, we show that direct dopamine neuron stimulation, both calibrated to physiological and greater intensities, at the time of reward can be sufficient to induce and maintain reward seeking (reinforcing) although replacement of a cue with stimulation is insufficient to induce reward seeking or act as an informative cue. Stimulation of descending cortical inputs, one synapse upstream, are sufficient for reinforcement and cues to future reward. Thus, physiological activation of mesolimbic dopamine neurons can be sufficient for reinforcing properties of reward without being sufficient for the predictive and incentive properties of cues.