Introduction When we return home from work we sometimes find ourselves deeply engaged in thinking about an unsolved problem. Yet, at the same time we manage to find our way home and avoid accidents. This indicates that apparently we are able to automatically control our behavior without needing conscious attention. If suddenly an obstacle appears, however, we are also able to switch attention in a split second. Given the right cues, we may also remember that in the morning we planned to do some shopping on the way home. That too would put solving the problem to a halt and bring us to figure out the best route to the shop. This shows that control may be taken over by retrieving a plan, and that in order to carry it out we need access to a large database, including a route planner. If later on we resume thinking about the problem, we may figure out a solution by combining known elements in a novel configuration, and then start thinking how to use it in the future. The description given above is an example of the coordinated and goal-directed thoughts and actions that are generated in our brains all the time. Clearly, such coordinated behavior requires a versatile system of cognitive control for which as yet there does not seem to exist an adequate concept. Some of the contours of such a control system, however, are discernable in the example given above, like the ability to maintain representations, to direct attention and to combine previously unrelated information. 1974 2003 1986 1996 2001 1999 2003 2001 1997 1986 2001 1999 2002 More specifically, we suggested that the development of PFC created the possibility to maintain physically absent information in an active state by recurrent connections (loops) between PFC and the rest of the cortex. The recurrent activity in these loops may affect subsequent perceptual and motor processing, i.e., it can redirect attention and control actions by activating or inhibiting particular motor programs. By assuming interactions and integration between loops, more complex forms of representation and control may develop. For instance, recurrent connections with memory systems would allow access to all stored information, and mechanisms for combining information in the loops would allow the formation and updating of future goal states, and ways to achieve them. A taxonomy of working memory functions maintenance attentional control integration 2001 1999 In this paper we will first discuss the neuroanatomical correlates of these hypothesized interdependent working memory functions. Then we will present a neural model for binding and segregation in working memory to simulate the maintenance function. This model is based on recent ideas about synchronization of activation patterns in networks and it is able to explain capacity limitations of maintenance in WM. Next, extensions of this model will be discussed to realize the attentional control mechanism. It is suggested that modulatory effects within PFC may selectively enhance or suppress representations in posterior cortex by recurrent connections. Finally, we will present some ideas on how to realize integration and manipulation functions, given that large-scale multi-modal integration is necessary to explain coherence and coordination of behavior. Neuroanatomical correlates of working memory in PFC 2001 2003 2001 2003 2001 2000 2004 2005 2001 2000 2001 2006 1999 2000 1998 2000 1999 1999 2001 2001 2002 2003 2000 2000 2001 2003 2004 Learning and working memory 1999 1999 2001 1991 The suggestion that PFC implements WM functions, but is not itself involved in associative learning, would fit the requirement that it is maximally flexible in the service of generating novel combinations of representations that are stored somewhere else. There are no indications, however, that the neural architecture and processes of the PFC differ fundamentally from those in posterior cortex which intrinsically generate a learning capacity. Therefore, some learning (and representational) capacity in PFC cannot be ruled out a priori. 1995 2006 2003 2005 2000 2001 1999 2006 1999 2005 2001 It remains to be seen whether the supposed PFC learning mechanism is viable. Over brief periods of time, learning probably does not play a major role, but a role of learning over extensive periods cannot be ruled out. Evidence for three interdependent working memory functions in PFC Maintenance 1971 1996 1995 1999 2003 1995 2003 1999 1985 1993 1999 1998 2003 2000 2002 2001 2006 2005 2006 1993 2002 2002 2000 1999 Although a dopamine-gating mechanism is an interesting possibility, we believe that re-entry of activation through recurrent neuronal circuits is the main mechanism for the maintenance function of WM. It is likely that there are many of such recurrent loops for different types of information. These loops link the perceptual, memory and motor representational areas in posterior cortex to PFC. They feed information into PFC and, in turn, are activated by the recurrent activation from PFC. Modulating activity in these loops by other PFC sources would modulate the top-down recurrency. We also suggest and that these loops form a hierarchy, at the lowest level maintaining simple stimuli or features and at higher levels maintaining increasingly complex stimulus relations and rules. We will show how this idea can be implemented and explore the feasibility of such an implementation. We agree, however, that especially for the highest and most global or integrative levels of maintaining information, a dopamine gating mechanism for auto-associative maintenance cannot be ruled out. Attentional control 1995 2001 2000 1993 1995 2000 2000 2005 1995 1999 1999 2003 1998 1998 2003 2006 1998 2001 2003 Integration 2000 2001 2003 2001 2004 2000 2000 2000 2002 2001 2003 2002 2002 1999 2002 2003 2002 2001 2002 2004b 2006 2002 2005 2004a 1 2004 2003 2006 Fig. 1 Double arrowheads aPFC dlPFC vlPFC oPFC mPFC pm cortex  A neurocomputational model of maintenance, control and integration 2001 In the model WM was assumed to be based on recurrent connections between IT cortex containing representations of objects or features, and corresponding neurons in PFC. The IT representations were modelled as strongly associated neural assemblies that generate synchronized firing patterns when activated by external input. The simultaneous activation of independent assemblies in IT causes competition via inhibitory interneurons. Due to the neuron characteristics, this leads to desynchronization among the activation patterns of competing assemblies resulting in a sustained phase-locked activation of multiple assemblies over time. 2001 1997 1998 1999 1992 1995 2002 2001 2 Fig. 2 IPSPs  In the IT module, strong connections are implemented within and weak connections between assemblies coding different ‘stimuli’. We also implemented a global inhibition (competition) mechanism between IT assemblies. This circuitry, in which a given assembly is inhibited by the firing of the other assemblies in the IT module, mediates an active desynchronization mechanism. 2 1993a b 1992 2001 The model, of course, is very simplified with respect to the complexity of the real cortical networks. Feedback from the vlPFC module does no more than maintaining the oscillatory state of IT assemblies after the stimulus offset. More realistic network versions might include “closed” reverberatory circuits within prefrontal areas, making prefrontal assemblies independent from the IT assemblies in maintaining the delay activity. This would allow modality shifts by activating other prefrontal assemblies, which in turn would trigger reverberatory circuitries in lower cortical areas, in the absence of actual physical stimulation. Moreover, we used a simplified one-to-one matching between IT and vlPFC assemblies, whereas it seems more realistic to assume that prefrontal assemblies are relatively non-specific, thus being connected to multiple sets of neurons in posterior areas. Instead of the one-to-one matching of assemblies, dlPFC neurons might simply send back activation to all vlPFC neurons, and these in turn to all IT neurons from which they receive activation. However, the present simple architecture is sufficient for demonstrating the functional principles we have specified earlier. Simulations Simulating maintenance in working memory 2001 3 2001 1997 3 3 3 2001 3 Fig. 3 a c e b d f a b c d e f  2001 1956 Simulating attentional control in working memory 1998 2000 selection maintenance supervisory control 2003 1995 4 4 Fig. 4 a b a b  2003 2003 1 \documentclass[12pt]{minimal}
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$$ {\text{Net}}_{i} = {\text{VI}}_{i} {\left( {1 + {\text{VD}}_{i} ^{ * } } \right)} $$\end{document} 2 \documentclass[12pt]{minimal}
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$$ {\text{VD}}_{i} ^{ * } = {\text{VD}}_{i} {\left( {u_{i} - {\text{VT}}} \right)}\quad {\text{if}}\;u_{i} \ge {\text{VT}}\quad {\text{else}}\;{\text{VD}}_{i} ^{ * } = 0 $$\end{document} 1992 u i 4 1991 1992 A series of follow-up simulations showed that the probability of item retention depends on the relative biasing top-down input for a given item. A strong attentional input to one or two items may ‘narrow’ visual working memory capacity to one or two highly focused elements, due to the higher firing rate (e.g. bursting) of the neurons coding for the focused items. Mechanisms for integration in working memory binding problem 2006 2001 2001 5 1999 Fig. 5 a c e b d f a b c d e f  1999 2000 2000 5 5 Control simulations with the same model and learning parameters, showed that a relatively high number of simultaneous presentations of initially independent elements, may slowly give rise to novel chunks with a variable degree of stability, even in the absence of feedback from dlPFC to vlPFC. This slow chunk-learning process may be related to an implicit learning process, rather than the more rapid process of controlled chunk formation by feedback from dlPFC. Discussion 2001 1999 2002 2003 2003 We do not imply that control is always hierarchical. Because the PFC is assumed to be a modulatory system, simple tasks may be executed automatically without PFC involvement. Only controlled processing requires modulation of automatic processes by PFC feedback. Also here, however, control does not need to be completely hierarchical. Depending on the type and complexity of a task, control may be executed by specialized modules at subordinate levels. Automatic processing and controlled processing under the guidance of subordinate levels in PFC, is a requirement to free higher integrative areas to engage in the kind of simulated actions and perceptions, and using anticipated outcomes, that are the contents of conscious thought and planning. So there are things we can do automatically, using previously established associations between perceptual events and effective responses, and there are things we can do in a controlled way, using the possibility of PFC to modulate and thus control perception and action. The possibility of PFC to work off-line, to use and integrate all present and past knowledge for creating virtual worlds and for making plans and carry them out if conditions are appropriate, has generated a tremendously flexible potential for control. The model we propose is vastly insufficient to simulate such cognitive feats that we engage in daily. We believe, however, that the principles for a controlling system we have suggested may be a first step towards a better understanding of what is until now a cognitive Terra Incognita.