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.

dynamic task switching

In this paper, we discuss the motivation, approach, and status of a new NSF ITR project in which an adaptive working memory is investigated for robot control and learning. There is much evidence for the existence of such a memory structure in primates. Such memory is closely tied to the learning and execution of tasks, as it contributes to decision-making capabilities by focusing on essential task information and discarding distractions. We will integrate the adaptive working memory structure into a robot to explore the issues of task learning in a physical embodiment. This leads to a complex but realistic system involving perceptual systems, actuators, reasoning, and short-term and long-term memory structures. In the paper, we discuss also planned experiments intended to evaluate the utility of the adaptive working memory.

This work focuses on a particular neurocomputational account of working memory function that has been used to explain a wide range of working memory phenomena in terms of interactions between the prefrontal cortex and the mesolimbic dopamine system. Using the mechanisms prescribed by this theory, we have constructed a software toolkit for creating working memory modules for use in robotic control systems. The challenges faced by embodied robots are similar to those experienced by humans in everyday living, making this domain useful for testing the utility and scalability of this computational theory of working memory. We report the results of a feasibility study, involving a robotic version of the delayed saccade task, and we discuss future plans to test our working memory model in the context of robot control and learning.

People perceive and conceive of activity in terms of discrete events. Here the authors propose a theory according to which the perception of boundaries between events arises from ongoing perceptual processing and regulates attention and memory. Perceptual systems continuously make predictions about what will happen next. When transient errors in predictions arise, an event boundary is perceived. According to the theory, the perception of events depends on both sensory cues and knowledge structures that represent previously learned information about event parts and inferences about actors ’ goals and plans. Neurological and neurophysiological data suggest that representations of events may be implemented by structures in the lateral prefrontal cortex and that perceptual prediction error is calculated and evaluated by a processing pathway, including the anterior cingulate cortex and subcortical neuromodulatory systems.

www.wjh.harvard.edu/~cfc Chapter 19 in Roberts, M. J. (Ed.) (2007). Integrating the mind: Domain general versus domain specific processes in higher cognition (pp. 449–491). Hove, UK: Psychology Press.

An understanding of attention is arguably one of the most important goals of the cognitive sciences and yet also has proven to be one of the most elusive. Most attention researchers will agree that a major problem has been agreeing on a definition of the term and the scope of the phenomena to which it applies. There are no doubt as many explanations for this state of affairs as there are those who consider themselves “attention researchers. ” However, most will probably agree that, in large measure, this is because attention is not a unitary phenomenon—at least not in the sense that it reflects the operation of a single mechanism, or a single function of one or a set of mechanisms. Rather, attention is the emergent property of the cognitive system that allows it to successfully process some sources of information to the exclusion of others, in the service of achieving some goals to the exclusion of others. This begs an important question: If attention is so varied a phenomenon, how can we make progress in understanding it? There are two simple answers to this question: Be precise about the specific (aspects of the) phenomena to be studied, and be precise about the mechanisms thought to explain them. In this chapter, we address a particular type of attentional phenomenon—that associated

Abstract – The ability to teach a robot new skills and tasks without explicit programming is an important goal in robotics. Such capability tends to imply the ability to learn from experience, much like many biological creatures. Evidence suggests that working memory plays a pivotal role in this learning process, in part by focusing attention on the most relevant data. We describe ongoing research to study the utility of computational neuroscience models of working memory within robotic systems. A system comprised of working memory, short term memory, long term memory, spatial reasoning and perception modules is proposed. The paper focuses on the perceptual module and its interaction with the working memory. Results are given to show the current progress. Index Terms – Working memory, robot vision, computer vision, learning, perception..

Standard techniques from pharmacology and current theories on the biochemistry of dopamine (DA) are used to derive a new model of how DA modulates cortical activation. The model assumes that DA potentiates the glutamate response through the NMDA receptor and depresses the glutamate response through non-NMDA receptors. A static version of the model is used to examine the neurobiological plausibility of the Servan-Schreiber, Printz, and Cohen model [Science 249 (1990) 892]. A dynamic version can be used to model many behaviors that are outside the domain of the static [Science 249 (1990) 892] model, including single-cell recording data. As a preliminary test of the model, we show that it can account for some single-cell recording data that examined the effects of DA on the firing rate of glutamatergic cortical cells.

The unsupervised categorization of sensory stimuli is typically attributed to feedforward processing in a hierarchy of cortical areas. This purely sensory-driven view of cortical processing, however, ignores any internal modulation, e.g., by top-down attentional signals or neuromodulator release. To isolate the role of internal signaling on category formation, we consider an unbroken continuum of stimuli without intrinsic category boundaries. We show that a competitive network, shaped by recurrent inhibition and endowed with Hebbian and homeostatic synaptic plasticity, can enforce stimulus categorization. The degree of competition is internally controlled by the neuronal gain and the strength of inhibition. Strong competition leads to the formation of many attracting network states, each being evoked by a distinct subset of stimuli and representing a category. Weak competition allows more neurons to be co-active, resulting in fewer but larger categories. We conclude that the granularity of cortical category formation, i.e., the number and size of emerging categories, is not simply determined by the richness of the stimulus environment, but rather by some global internal signal modulating the network dynamics. The model also explains the salient non-additivity of visual object representation observed in the monkey inferotemporal (IT) cortex. Furthermore, it offers an explanation of a previously observed, demand-dependent modulation of IT activity on a stimulus categorization task and of categorization-related cognitive defi cits in schizophrenic patients.