The prefrontal cortex has long been thought to subserve both working memory (the holding of information online for processing) and executive functions (deciding how to manipulate working memory and perform processing). Although many computational models of working memory have been developed, the mechanistic basis of executive function remains elusive, often amounting to a homunculus. This article presents an attempt to deconstruct this homunculus through powerful learning mechanisms that allow a computational model of the prefrontal cortex to control both itself and other brain areas in a strategic, task-appropriate manner. These learning mechanisms are based on subcortical structures in the midbrain, basal ganglia, and amygdala, which together form an actor-critic architecture. The critic system learns which prefrontal representations are task relevant and trains the actor, which in turn provides a dynamic gating mechanism for controlling working memory updating. Computationally, the learning mechanism is designed to simultaneously solve the temporal and structural credit assignment problems. The model’s performance compares favorably with standard backpropagation-based temporal learning mechanisms on the challenging 1-2-AX working memory task and other benchmark working memory tasks.

All intelligence relies on search --- for example, the search for an intelligent agent's next action. Search is only likely to succeed in resource-bounded agents if they have already been biased towards finding the right answer. In artificial agents, the primary source of bias is engineering. This dissertation

A selection problem arises whenever two or more competing systems seek simultaneous access to a restricted resource. Consideration of several selection architectures suggests there are significant advantages for systems which incorporate a central switching mechanism. We propose that the vertebrate basal ganglia have evolved as a centralised selection device, specialised to resolve conflicts over access to limited motor and cognitive resources. Analysis of basal ganglia functional architecture and its position within a wider anatomical framework suggests it can satisfy many of the requirements expected of an efficient selection mechanism. Key words: behaviour, action, movement, switching, model, architecture, motor control Citation: Redgrave, P., Prescott, T.J. and Gurney, K. (1999). The basal ganglia: a vertebrate solution to the selection problem?, Neuroscience, 89, 1009--1023. INTRODUCTION Despite a prodigious volume of work in recent years there is still no consensus co...

This article presents a biologically inspired model for motor skills imitation. The model is composed of modules whose functinalities are inspired by corresponding brain regions responsible for the control of movement in primates. These modules are high-level abstractions of the spinal cord, the primary and premotor cortexes (M1 and PM), the cerebellum, and the temporal cortex. Each module is modeled at a connectionist level. Neurons in PM respond both to visual observation of movements and to corresponding motor commands produced by the cerebellum. As such, they give an abstract representation of mirror neurons. Learning of new combinations of movements is done in PM and in the cerebellum. Premotor cortexes and cerebellum are modeled by the DRAMA neural architecture which allows learning of times series and of spatio-temporal invariance in multimodal inputs. The model is implemented in a mechanical simulation of two humanoid avatars, the imitator and the imitatee. Three types of sequences learning are presented: (1) learning of repetitive patterns of arm and leg movements; (2) learning of oscillatory movements of shoulders and elbows, using video data of a human demonstration; 3) learning of precise movements of the extremities for grasp and reach

We review recent research in robotics, neuroscience, evolutionary neurobiology, and ethology with the aim of highlighting some points of agreement and convergence. Specifically, we compare Brooks' (1986) subsumption architecture for robot control with research in neuroscience demonstrating layered control systems in vertebrate brains, and with research in ethology that emphasizes the decomposition of control into multiple, intertwined behavior systems. From this perspective we then describe interesting parallels between the subsumption architecture and the natural layered behavior system that determines defense reactions in the rat. We then consider the action selection problem for robots and vertebrates and argue that, in addition to subsumption-like conflict resolution mechanisms, the vertebrate nervous system employs specialized selection mechanisms located in a group of central brain structures termed the basal ganglia. We suggest that similar specialized switching mechanisms might...

Abstract. This volume is intended to help advance the field of artificial neural networks along the lines of complexity present in animal brains. In particular, we are interested in examining the biological phenomena of modularity and specialized learning. These topics are already the subject of research in another area of artificial intelligence. The design of complete autonomous agents (CAA), such as mobile robots or virtual reality characters, has been dominated by modular architectures and context-driven action selection and learning. In this chapter, we help bridge the gap from neuroscience to artificial neural networks (ANN) by incorporating CAA. We do this both directly, by using CAA as a metaphor to consider requirements for ANN, and indirectly, by using CAA research to better understand and model neuroscience. We discuss the strengths and the limitations of these forms of modeling, and propose as future work extensions to CAA inspired by neuroscience.

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This proposal was originally a short paper relating representations of intelligence between three fields: psychology, neuroscience and artificial intelligence (AI). I particularly emphasize the role of modularity in these three areas. To my knowledge, this paper was never published — it was written on commission, but several years ago and I have just done yet another web search to find it. Further,

The authors test a neurocomputational model of dopamine function in cognition by administering to healthy participants low doses of D 2 agents cabergoline and haloperidol. The model suggests that DA dynamically modulates the balance of Go and No-Go basal ganglia pathways during cognitive learning and performance. Cabergoline impaired, while haloperidol enhanced, Go learning from positive reinforcement, consistent with presynaptic drug effects. Cabergoline also caused an overall bias toward Go responding, consistent with postsynaptic action. These same effects extended to working memory and attentional domains, supporting the idea that the basal ganglia/dopamine system modulates the updating of prefrontal representations. Drug effects interacted with baseline working memory span in all tasks. Taken together, the results support a unified account of the role of dopamine in modulating cognitive processes that depend on the basal ganglia.

This paper proposes a brain-inspired cognitive architecture that incorporates approximations to the concepts of consciousness, emotion, and imagination. To emulate the empirically established cognitive efficacy of conscious as opposed to unconscious information processing in the mammalian brain, the architecture adopts a model of information flow from global workspace theory. Cognitive functions such as anticipation and planning are realised through internal simulation of interaction with the environment. Action selection, in both actual and internally simulated interaction with the environment, is mediated by affect. An implementation of the architecture is described which is based on weightless neurons and is used to control a simulated robot. 1