Sustained attention and apical dendrite activity in recurrent circuits

Brain Research Reviews 50 (2005) 86 – 99 www.elsevier.com/locate/brainresrev

Review

Sustained attention and apical dendrite activity in recurrent circuits David LaBerge Simon’s Rock College of Bard, 84 Alford Road, Great Barrington, MA 01230, USA Accepted 21 April 2005 Available online 25 May 2005

Abstract Recurrent neural activity is a pervasive mode of cortical operations and is believed to underlie cognitive functions of working memory, attention, and the generation of spontaneous activity during sleep [90]. It is proposed here that activity in corticothalamic recurrent circuits underlies the sustaining of attention, and that extended durations of attention are made possible by the stabilizing effects of electrical activity in long apical dendrites of pyramidal neurons. Using the cue – target delay task as a framework, the present paper describes sustained attention during the cue – target delay as activity in recurrent circuits involving layer 5/6 pyramidal neurons. At target onset, persistent activity in apical dendrites of layer 2/3 pyramidal neurons (projected from the recurrent circuits) can enhance the processing of incoming pulse trains at basal dendrites. Apical dendrite activity is assumed to modulate the soma processing of layer 2/3 and layer 5/6 pyramidal neurons at subthreshold voltage levels. The variability of successive soma depolarizations from the apical dendrite strongly influences the stability of activity in the corticothalamic recurrent circuit. Lower variability promotes higher stability. According to the present model of apical dendrite function, soma depolarizations can be reduced in variability and maintained within subthreshold levels by increasing the distance that EPSPs propagate along the apical dendrite. The close relationship between sustained attention and the electrical field potentials produced by repeated EPSP propagations in apical dendrites is supported in a brief review of sustained attention experiments that have employed measures of EEG, ERS/ERD, ERP, and LFP. D 2005 Elsevier B.V. All rights reserved. Theme: Neural basis of behavior Topic: Cognition Keywords: Attention; Recurrent circuit; Apical dendrite

Contents 1. 2. 3. 4. 5. 6. 7.

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The recurrent circuit mechanism of sustained attentional activity. . . . . . . . . . . . . . . . . . . . . . . . . . The stability of recurrent circuit activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modeling the effect of apical dendrite length on stability of the recurrent circuit activity . . . . . . . . . . . . . Membrane depolarizations and conductances of the in vivo apical dendrite . . . . . . . . . . . . . . . . . . . . An anatomical comparison of apical dendrites across mammalian species . . . . . . . . . . . . . . . . . . . . . Evidence of apical dendrite activity during sustained attention from studies that use EEG, ERS/ERD, ERP, and LFP measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1. EEG evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2. ERS/ERD evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3. ERP evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4. LFP evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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D. LaBerge / Brain Research Reviews 50 (2005) 86 – 99

1. Introduction The pyramidal neuron, with its relatively long apical dendrite, is the most numerous type of neuron found in the mammalian cortex. A review of studies that counted neurons in the cerebral cortex of mammals [67] concludes that approximately 67% of cerebral neurons are pyramidal cells (this may be a conservative assessment, in view of the estimate of 70 –80% given by [26]). Given the involvement of layer 2/3 pyramidal neurons in corticocortical processing, the apparently close anatomical organization of the apical dendrites of layer 2/3 pyramidal neurons around clusters of apical dendrites of layer 5 pyramidal neurons [76] suggests that apical dendrite activity has the potential to influence corticocortical processing anywhere in the neocortex. While some apical dendrite activity exhibits action potentials [21,22,39,90], the repeated propagation of stable levels of EPSPs to the soma could be involved in the sustaining of cognitive states that exhibit durations of seconds or more, in particular focal states of prolonged attention and ambient states of consciousness. The average length of the apical dendrite varies considerably across the mammalian species. The variation in apical dendrite length is particularly noticeable in the parietal cortex, where visual attention to location is believed to operate on sensory inputs and in the primary visual cortex from which the parietal cortex receives much of its input. Fig. 1 shows camera lucida drawings, obtained from several neuroscience laboratories, that show pyramidal neurons

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whose somas are located in layer 5 and layer 3 for adults of five mammalian species: mouse, rat, cat, monkey, and human. The lengths of the apical dendrites in the living mammal are actually somewhat longer than the lengths shown here, owing to the linear shrinkage of neural tissue during the preparation of the cortical sections for microscopic observation (estimated to be approximately 18% [67]). The average length of the apical dendrite of pyramids increases across mammalian phyla from mouse to human, while the average length of the basal dendrites of pyramids exhibits a somewhat irregular trend. The drawings in Fig. 1 were taken from the primary visual area, except for the pair of human pyramids. After over a year of searching, this author was unable to find drawings of adult human pyramids from layers 3 and 5 of area 17 in the literature or in the files of many neuroscience laboratories. Therefore, for the present purpose of comparing apical dendrite lengths from mouse to human, the pair of pyramid drawings for the human is based on drawings of pyramids from the motor area of the newborn human made by Cajal [19]. These drawings were then scaled to fit the lengths of corresponding adult human pyramids in area 17 that were estimated from the thicknesses of the layers of human area 17 given by [84]. Lengths of apical dendrites of pyramids whose somas lie in layers 2, 3, 5, and 6 can be estimated from the thicknesses of cortical layers. The thicknesses of each layer of area 17 and area 7 for five mammalian species are given by [84] and are shown in Fig. 2 (uncorrected for linear shrinkage of

Fig. 1. Camera lucida drawings of cortical pyramidal neurons of five mammalian species. The pyramidal neurons are located in layers 3 and 5 of cortical area 17. Mouse and Rat. Camera lucida drawings obtained from G. Gorney, Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Alberta, Canada (2002). Cat. Camera lucida drawing of a layer 3 neuron obtained from [30], p. 124, Fig. 6. Camera lucida drawing of a layer 5 neuron obtained from [42], p. 661, Fig. 3. Monkey. Camera lucida drawing of a layer 3 neuron obtained from [99], p. 14, Fig. 7; camera lucida drawing of a layer 5 neuron obtained from [100], p. 244, Fig. 24. Human. Camera lucida drawing of layers 3 and 5 neurons by Cajal, obtained from [19], p. 202, Fig. 90. For the drawings of the pair of human neurons, the motor cortex pyramidal neurons of a 1-month-old child were used as estimates of the appearance of visual cortex pyramidal neurons of the adult. The layer 5 neuron was modified by extending the central part of the apical dendrite so that the total length of the apical dendrite conformed to the length of the visual cortical layer 5 pyramidal neuron of the adult human, using length estimates given by [84] and shown in Fig. 2 of the present paper. The layer 3 pyramidal neuron for the human was not modified.

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Fig. 2. Thicknesses of the layers in cortical area 17 (left) and area 7 (right) of the mouse, rat, cat, monkey, and human [84].

approximately 18%). The observed thicknesses of area 17 cortical layers in Fig. 2 provide an independent confirmation of the lengths of apical dendrites for the mouse, rat, cat, and monkey that are shown in Fig. 1. The lengths of the apical dendrite of layer 6 pyramids in the primary visual and parietal areas across these five species can also be estimated from the thicknesses of cortical layers shown in Fig. 2, noting that the apical dendrites of layer 6 pyramids typically terminate in layer 4. The aim of this paper is to examine the way that apical dendrite activity can operate within a recurrent circuit to sustain attention. The sustaining of attention typically occurs in two classes of stimulus situations: during cue – target delays, in which a cue induces attention in preparation for the upcoming stimulus target, and during situations in which attention to an object is simply maintained for its own sake (e.g., for the pleasure it produces) [51]. In contrast, brief durations of attention typically occur when object locations are rapidly selected for subsequent processing, as in reading words in text and in searching for an object in a cluttered visual field. The sustaining of neural activity during cognitive processing has been extensively studied in laboratory tasks of working memory. Since the early 1970’s, a substantial amount of research has consistently supported the claim that

persistent neural activity in the frontal cortex can store object information for many seconds, enabling the animal to respond successfully to a delayed appearance of the target stimulus [29,31,50]. The neural mechanism by which the frontal cortex sustains the activity of working memory is generally believed to be a recurrent circuit [13,64,104]. Recently, it has been suggested that working memory does more than simply store the information that is provided by a stimulus cue until the target stimulus appears [55,72,75]. In the case of spatial working memory, the storage of cued location information may be accompanied by attention to that location throughout the delay period between the cue and target [4,44]. During the cue– target delay period, executive operations in the frontal cortex [5] may provide top – down control preparatory attention to a particular stimulus location. Control of attention from frontal areas may be distinguished from the expression of attention in posterior cortical areas. The expression of attention refers to operations performed on incoming sensory signals that select particular signals for further processing while ignoring or suppressing other sensory signals. Top– down signals are presumed to control which of the many available incoming signals, indexed as the location or appearances of objects, will be selected for additional processing. Although abrupt onsets of sensory

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signals can transiently activate (and interrupt) both the expression and control of attention, it is assumed that the frontal areas of control determine whether or not attentional operations will be sustained for a period of time, for example, during an extended period of time during which a particular stimulus is anticipated. The role of frontal attentional control is underlined by two recent studies [3,93] which used a test of sustained attention developed by the present author and his colleagues [54]. The test of preparatory attention was given to adult patients with frontal lobe lesions and child patients with frontal lobe epilepsy who showed deficits in resisting effects of distracters during extended delays between a stimulus that cued the location of an upcoming target stimulus and the onset of the target stimulus. Many studies have suggested that frontal operations of executive control are directed at the parietal areas during attentional preparation for the location of a target object by a frontal-parietal network [2,16,17,41,47,48,57,62,74,92]; for a review, see [15]. Thus, during periods of prolonged preparatory attention to an object’s location, activity may persist both in frontal areas of attentional control and in parietal areas of attentional expression, where activity can influence the early processing of the location information in an upcoming target stimulus.

2. The recurrent circuit mechanism of sustained attentional activity While recurrent circuit activity in frontal cortex appears to be crucial for sustaining information in working memory, recurrent circuit activity may also be crucial for sustaining

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attention to the location and appearances of stimulus objects. Information is presumed to be sustained in working memory as pulses circulating in recurrent circuits [13,64, 104], but attention is assumed to be sustained as apical dendrite activity that produces an ongoing modulatory state in pyramidal neurons. Stable elevated levels of subthreshold voltage at the soma of particular pyramidal neurons are assumed to be produced by the arrival of repeated EPSPs from apical dendrites within recurrent circuits that include thalamic neurons. According the triangular circuit theory of attention [51], two recurrent circuits are active during sustained attention to a sensory object or attribute: a global recurrent circuit and a local recurrent circuit. Fig. 3 shows, in a diagrammatic form, a global recurrent circuit that connects frontal and parietal cortical neurons, and a local circuit that connects cortical and thalamic neurons in a nested arrangement within the global recurrent circuit. The local recurrent circuit that expresses attention in the parietal cortex contains both cortical and thalamic neurons in a ‘‘vertical’’ organization, which contrasts with the ‘‘horizontal’’ organization of cortical cells believed to serve circuits of working memory [104]. The present paper is concerned primarily with the operations within the proposed local recurrent circuit of sustained attention. Thalamic involvement in sustained attention tasks is indicated by several studies. Cellular recordings have shown that both parietal area 7a and the pulvinar were active while monkeys waited attentively for a central fixation point to dim [8]. Implanted electrodes in the cortex and thalamus showed elevated coherence in oscillatory activity in cats watching a hole in a wall where the head of a mouse occasionally appeared [86]. PET measures of humans

Fig. 3. Global and local recurrent circuits assumed to be active during sustained attention, according to the triangular circuit theory [51]. The global recurrent circuit includes thalamic neurons from the nuclei that serve the frontal and posterior cortical areas, and the local recurrent circuit (shown here with thicker lines) includes thalamic neurons from the (pulvinar) nucleus that serves the posterior cortex.

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indicated that the pulvinar nucleus of the thalamus was active while humans sustained attention to a visual location [52,58]. Attention-related studies have shown activation of the thalamus while participants were attending to sounds while ignoring visual stimuli [28] and when participants observed increases in salience of an emotionally expressive face [65]. An analysis of nine PET studies of visual processing, including tasks of discrimination, search, language, memory, and imagery [91], showed consistent activation in the thalamus (and cerebellum) while showing no common cortical regions of increased activation (outside of area V1). Simulations of activity in the thalamocortical circuit [53] during selective attention of a target in a cluttered scene suggest that the pulvinar may enhance persistent activity in cortical columns that code the target while suppressing activity in neighboring columns that code distractors. The local cortical circuits that participate in the sustaining of attention during a cue– target delay and participate in the use of that attention in the processing of the target stimulus are defined here within the cortical minicolumn. The minicolumn has been proposed as the basic unit of neural organization of the neocortex [66], based on studies of the somatosensory area; and the neuronal structure of the minicolumn, as revealed by staining studies, was described as a vertically oriented module, in which pyramidal cells surround a centrally located cluster of layer 5 apical dendrites [76]. Clusters of minicolumns are organized into columns, possibly by being common targets of a thalamic relay cell, so that there is some redundancy in activity across minicolumns within a column. This redundancy of activity levels within a column may underlie the distributed coding scheme by which sensory attributes, such as the orientations of a bar, are registered in cortical areas. In order to assist the present description of the proposed corticothalamic circuit of attentional expression, the cortical minicolumn will be partitioned into three parts. Fig. 4 shows a diagram of the minicolumn from [76], which is based on photographs of stained dendrites of monkey striate cortex. In this diagram, the apical dendrites of layer 5 pyramidal neurons appear to form a vertical axis of the minicolumn that extends from layer 5 to layer 1. Arranged in a shell-like pattern around the upper region of this axis are the layer 2 and layer 3 pyramidal neurons (‘‘pyramids’’). This axis-shell structure of the layer 5 and layer 2/3 pyramids is shown diagrammatically in Fig. 5 by dashed lines. The distribution of layer 6 pyramids apparently is not organized within the minicolumn but instead is organized at the column level, which is a cluster of approximately 100 minicolumns [67]. The recurrent circuit of sustained attention, shown diagrammatically on the left side of Fig. 5, contains two interconnected corticothalamic loops, each containing a different kind of thalamic relay neuron. Thalamic relay cells have been separated by staining into two types: the core relay cell, which receives inputs from the local column to which it

Fig. 4. Diagram from [76] of excitatory neurons in a minicolumn in area 17 of the monkey showing pyramidal neurons whose somas are located in four layers: layers 2, 3, 5, and 6. Neurons not showing apical dendrites are midlayer stellate neurons.

projects, and the matrix relay cell, which receives inputs from remote columns [46]. Core projections and their feedback are topographically and reciprocally arranged. Axons from local cortical columns to the core cell appear to terminate mainly on distal dendritic regions where they may serve mainly to modulate activity in the region of the soma [63]. Axons from remote cortical columns to the matrix relay cell appear to terminate mainly on proximal dendritic regions [94], where they may more directly influence the pattern of output activity of the soma. The matrix relay neuron projects to the distal

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neurons are strong. When synaptic connections are weak, input – output processing is presumed to require attentional modulation by apical dendrite activity in the layer 2/3 apical dendrites, in the manner shown in Fig. 6. Fig. 6 shows the connections between the axis and shell circuits of a typical minicolumn in the posterior cortex. Ongoing activity in the recurrent corticothalamic circuit is projected to the apical dendrites of layer 2/3 pyramids, and the continuous activity there is presumed to facilitate the input – output processing of corticocortical inputs to the minicolumn at the time of target onset. Apical dendrite activity influences input –output processing of a layer 2/3 pyramid by modulating the level of subthreshold activity at the soma. It is assumed that activity at the soma of a layer 2/3 pyramid during the cue – target delay normally is not sufficiently strong to produce trains of output pulses. However, at the time of target onset, the ongoing subthreshold activity in layer 2/3 pyramids will enhance the processing of the incoming target input pulses from earlier sensory cortical areas (e.g., area V1) at basal dendrites so that a train of output pulses is produced in the axon. Support for this assumption is given by cellular recordings in the cat visual cortex [101 – 103]. In these studies, the spike count

Fig. 5. Diagram of recurrent thalamocortical circuits and cortical circuits within a single minicolumn of extrastriate cortex (adapted from [46]). For clarity, the inhibitory neurons of the reticular nucleus and the dorsal thalamus have been omitted. The axis-shell organization of the minicolumn, shown by dashed lines, is based on the apparent separation of recurrent circuits involving the thalamus and input – output circuits involving only the cortex. The vertical scale is greatly reduced to accommodate the diagram within a page of text. Left: Matrix and core relay cells of the thalamus project to apical dendrites of layer 5 pyramidal cells at distal and midlayer laminar locations, respectively; and core cells also project to layer 6 cells at midlayer laminar locations. Core cells receive activation from the cortical area to which they project, while matrix cells receive additional activation from other cortical areas. Right: The circuits within the minicolumn that serve input – output processing between cortical columns. Signals from sensory sources (via minicolumns and columns of area V1) initiate activity in stellate cells of layer 4, which project activity to the basal dendrites of layer 2/3 pyramidal cells (as well as to inhibitory cells that are not shown here). The output signals from this processing within the shell of the minicolumn are then sent via axon fibers from layer 2/3 pyramidal cells to the minicolumn (and columns) of other cortical areas.

sector of apical dendrites in layer 5 pyramids, whose locations are diffusely spread across adjacent cortical areas. The core relay neuron projects to the midlayer sector of apical dendrites in layer 5 and layer 6 pyramids, whose cortical locations are more focused, perhaps within the cortical column that organizes layer 6 pyramids. On the right side of Fig. 5 is a simplified diagram of minicolumn circuitry that processes incoming signals from minicolumns and columns in other cortical areas into output signals to minicolumns and columns in other areas. The processing in this shell part of the minicolumn is presumed to take place independently of attention (i.e., automatically) only if the synaptic connections between participating

Fig. 6. Circuit diagrams showing the way recurrent circuit activity in the axis of the minicolumn can influence input – output corticocortical activity in the shell of the minicolumn.

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produced in response to the same test stimulus was higher when the gamma (20 – 70 Hz) power of membrane oscillations was higher. Although the animals in these studies were anesthetized, which presumably reduced or eliminated attentional activity in top – down global pathways (see Fig. 3), the bottom – up sensory pathways of the lateral geniculate nucleus produce activity of both the membrane potential and spiking of pyramidal neurons during visual stimulation [70]. Thus, the relationship between the strength of the membrane potential and the spiking rate could be assessed within a single cortical neuron in the anesthetized animal. In view of these considerations, it is proposed here that sustained attention in the cortex is expressed in two different sets of circuits within the cortical minicolumn: (1) the corticothalamic recurrent circuits involving layer 5 and 6 neurons, which sustain attentional activity that column during a cue – target delay period, and (2) the circuit containing layer 2/3 neurons, which employs this activity in the processing of the forthcoming target. The present distinction between sustaining attention during a delay and the use of that activity at the time of target onset is supported by an fMRI study [41] of spatial attention in which superior frontal control areas apparently modulated activity in extrastriate cortex during the cue –target delay and at the time the target appeared. The partitioning of the expression of attention into sustainment and use within a column of the posterior cortex parallels the separation of working memory operations of the frontal cortex into storage and processing [55,72,75]. The diagram shown in Fig. 5 suggests the generalization that the sustaining of attention is communicated from column to column across cortical areas along an indirect route through the thalamus that is somewhat separable from the direct column-to-column route that specializes in processing signal information. For convenience, the direct column-to-column pathway involving the cortical shells may be labeled the Fupper tier_, and the indirect column-tothalamus-to-column pathway may be labeled the Flower tier_. Thus, the axis-shell diagram of Fig. 5 implies that the global dorsal and ventral pathways that traverse the cortex (and code for object location and appearance, respectively) each contain an upper and a lower tier that serve different cognitive functions. In the upper tier pathway, the use function may be controlled in a brief manner by direct connections from frontal columns that excite (via stellate cells) layer 2/3 neurons of parietal columns (see Fig. 3). This top – down route of attentional control, via the upper tier pathway, is presumed to operate somewhat independently of sustained attentional activity currently existing in the lower tier recurrent circuits. An important example of a brief attentional operation is the rapid shifting of selective attention from one location to another within a stimulus display, which is a prominent operation in the rapid selective reading of fluent text and the rapid selective scanning of a display of

objects in search tasks. The time between shifts of attention to object locations in a search display has been estimated to be on the order of 80 ms [98]. Since part of this time interval is allotted to the shift operation itself, the ‘‘dwell’’ time devoted to processing the information at the selected location is estimated to be less than 80 ms. The local circuit operations involved in briefly attending to an object’s location may exhibit strong lateral inhibition that momentarily suppresses activity in columns that code for locations of neighboring objects. Attending to a target location is expressed as a greater activity at the target site than at surrounding sites, which may be produced by enhancing activity at the target site, by suppressing activity at the surrounding sites, or by both of these operations [51]. Suppression of activity at distracter sites may dominate the selective operations of attention when the existing attentional activity to alternative locations of a display is near ceiling levels. Ceiling (or near-ceiling) levels of activation can be produced by initially attending to the whole display on a trial, or by residual effects of having attended to these locations on recent trials. In consequence, expressing attention to a particular location (by more activity at the target site) will require the inhibition of neighboring locations. The roles of excitation and suppression in selective attention have been addressed by several theories [20,53,97]. Other network models of visuospatial attention are described in a review [89].

3. The stability of recurrent circuit activity To be effective in serving processes of working memory, the recurrent circuit should possess several properties, in particular, stability, content-specific selection, and the ability to be rapidly turned on and off [96,104]. Given the ambient background noise in most cortical circuitry, the issue of stability presents a serious problem for the prolonging of activity without interruption or distortion throughout the duration of a delay period, particularly when the duration is relatively long. The stability of recurrent activity is evidenced by rhythmically regular, synchronous activity of the neurons that make up the recurrent circuit (a description of the role of neurons of the thalamocortical circuit in producing synchronous high frequency oscillations is given by [46]). Synchronous activity in the apical dendrite is presumed to produce a steady ongoing level of subthreshold depolarization, in which the depth of temporally produced ‘‘ripples’’ in the depolarization level is reduced as the frequency of arriving EPSPs is increased. Asynchronous activity is presumed to produce uneven ripples that preclude a steady state of modulation at the soma and therefore preclude a steady state of sustained attention. The activity level in a recurrent circuit is subject to fluctuations, owing to the intrusion of excitatory and inhibitory noise from sources inside as well as outside the

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particular recurrent circuit. If the fluctuating activity within the layer 5 or layer 6 pyramid momentarily reaches zero, the activity in the recurrent circuit of which it is a part will shut down. If the fluctuation of activity reaches too high a level, it can produce axon outputs that perturb the synchrony of the recurrent activity. Momentary fluctuations of recurrent activity that are too high in layer 2/3 pyramids during a cue –target delay can trigger axon outputs prematurely. An overt response produced in this manner before the target stimulus occurs is commonly called a ‘‘false positive’’ or ‘‘jumping the gun.’’ Therefore, a relatively high level of stability is required in the recurrent circuits containing layer 5/6 pyramids and in layer 2/3 pyramids to insure the steady persistence of attention during a delay and to insure the effective use of this attentional activity for the processing of the target stimulus at the end of the delay. To maintain a high level of stability during a cue– target interval, the participating pyramids must have some means of constraining the variance of their activity levels. To illustrate the impact of variability in depolarization level on stability, hypothetical distributions of depolarization variability are shown in Fig. 7. The distributions of depolarization are produced by successive incoming EPSPs from the apical dendrite of a pyramid. When the variability of soma depolarizations is sufficiently small, the voltages remain within a subthreshold range, and soma activity is highly stable. When these depolarizations are added to the concurrent subthreshold depolarization provided by basal dendrites in layer 5/6 pyramids, the summed depolarizations exceed the threshold for soma firing, and oscillating depolarization levels produced by the apical dendrites may effectively drive the frequency of pulse outputs in the axon. These pulse outputs are sent into the recurrent corticothalamic circuit, and from there, to apical dendrites of layer 2/3 pyramids (see Fig. 6). During a cue– target delay, apical dendrites of layer 2/3 pyramids also produce highly stable levels of subthreshold soma depolarizations when their variability is sufficiently small; and when these subthreshold depolarizations are added to concurrent depolarizations provided by basal dendrites, the summed depolarizations in layer 2/3 pyramids remain subthreshold. When a target

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stimulus appears at the end of the cue –target interval, incoming pulses to the basal dendrites of the layer 2/3 pyramids produce strong depolarizations that sum with the existing subthreshold soma depolarization level and exceed the firing threshold, producing pulse outputs. As noted before, higher subthreshold depolarizations in layer 2/3 pyramids can enable incoming corticocortical pulses at the basal dendrites of these pyramids to produce axonal outputs more quickly and more accurately. These pulse outputs are then sent on to neurons in the shells of other cortical columns for further processing.

4. Modeling the effect of apical dendrite length on stability of the recurrent circuit activity The soma depolarization level that is produced by apical dendrite activity is assumed to decrease as a function of two main variables: the distance the initiating input current propagates along the apical dendrite, and the membrane conductances along the apical dendrite between the site of the initiating current and the soma. As a first approximation, the equation for the soma (incremental) voltage level within a single compartment model of the apical dendrite is assumed to be Vd ¼ Vo ekd ; where Vo is the voltage increment at the site of current initiation, d is the distance the current has propagated, V d is the voltage increment at distance d from the site of initiation, and k is a constant that represents an average of local conductances within the dendritic compartment. This exponential equation, which describes the local change in voltage increment along the apical dendrite, is identical to the cable equation developed by Rall [81,82] for representing the passive decay of voltage along a dendrite. In the cable equation, k represents average membrane conductance, for which the potassium leak current is prominent when the dendrite is assumed to be in a passive state. When the dendrite is in the active state, conductances could conceivably take on values that represent the boosting of

Fig. 7. Hypothetical distributions of soma depolarizations showing three levels of variability-based stability.

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EPSPs as well as their attenuation [83]. However, modeling of in vivo dendritic activity generally assumes an active membrane that is dominated by inhibitory conductances, with an inhibition/excitation ratio that may be large as 5-to-1 [22]. Fig. 8 shows a family of four voltage decay curves that vary in initial voltage level, and each initial voltage undergoes a constant rate of attenuation, k, with distance along the dendritic shaft (voltages shown here are voltage increments from resting potential). It is assumed here that the dendritic membrane is in an active state, so that the value of k is relatively large, representing an average conductance that is dominated strongly by inhibition. If the average inhibitory conductance was decreased, the value of k would be less and the slope of the curves in Fig. 8 would be more shallow, resulting in a high proportion of suprathreshold depolarizations at the soma of the longer pyramidal neuron shown at the bottom of the figure. Although the exponential decay curves shown in Fig. 8 were proposed for a passive dendrite, decay curves in the literature for models of high conductance states of active dendrites (e.g., [22]) have the same general form even though the underlying equations are not exactly the same. Because the decay of voltage in all of these curves is steep at first and gradually becomes more shallow as it approaches an asymptote of zero or near-zero voltage, the variability of voltage decreases with propagation distance. In Fig. 8, the group of four voltage values at any given distance between the initiating site and the soma indicates the voltage variability at that distance. A vertically oriented unimodal distribution of voltages can be visualized by letting the values on the highest and lowest curves

Fig. 8. A family of theoretical exponential functions corresponding to the decay of voltage with distance along a single-compartment model of the apical dendrite. Four different levels of voltage are shown at the initiating synapse, and the distance to the soma is represented by d. The decay constant, k, is 0.70. As distance increases, the variability of the voltage decreases. (All voltage values are increments in voltage from baseline level.)

correspond to voltages near the lower and upper ranges of the distribution, and by letting the values on the two intermediate curves correspond to voltages near the mean or median of the distribution. For a Gaussian distribution, the two outside values could correspond to T2.00 SD from the mean, and the two inside values could correspond to T0.67 SD from the mean (which would separate adjacent values by a constant SD of 1.33). Given that the magnitude of the variance is indicated by the vertical separations of the voltage values, it is clear that the convergence of the curves with distance from the initiating synaptic site indicates a reduction in the variance of their voltage levels. Thus, according to the present model of apical dendrite function, an increase in the length of the apical dendrite makes possible a greater reduction in the variability of the soma depolarization level at the same time that it allows the depolarization levels to reach subthreshold values.

5. Membrane depolarizations and conductances of the in vivo apical dendrite A cortical pyramidal neuron is dotted with upwards of 10,000 synapses [18], activated mostly from other cortical neurons [11]. For decades, dendrites were believed to be passive conductors of postsynaptic electrical activity; but it is now firmly established that dendrites express active conductances and possess a rich repertoire of intrinsic electric and chemical properties [45,59,83]. Background activity at the thousands of synapses along the apical dendrite is responsible for about 80% of the input conductance [21,74], and although background activity sometimes involves highly fluctuating membrane potentials at individual synapses [14,71,74], it maintains average membrane depolarization at subthreshold levels, owing to the dominance of inhibitory conductances [1,9,22,38]. Analyses of these membrane conditions on signal integration and transmission in the dendrite have been carried out with theoretical and experimental methods in several recent studies [22,36,39,87,88,90,105]. In the present paper, these active membrane conditions are regarded primarily in terms of their average local effects on voltage changes in the thalamic-generated EPSP as it propagates along the dendritic shaft. Owing to the large number of conductance sites along the apical dendrite, fluctuations in background conductance at any particular local site will have a minor affect on the average voltage on the EPSP as it propagates toward the soma. Thus, the active conducting state of the apical dendrite shaft produces a greater rate of decrement in the propagating EPSP than would be produced by a passive state of conduction. Together with an appropriately long propagation distance, the high decrement rate of the active dendrite helps to insure that a high EPSP at the initiating synapse is reduced to a subthreshold level of depolarization at the soma.

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6. An anatomical comparison of apical dendrites across mammalian species The increase in length of the layer 5 apical dendrite in the parietal area from mouse to human (Fig. 2) suggests that preparatory attention to a specific location could be sustained for longer periods in mammals that possess longer apical dendrites. Part of the conjectured increase in ability to sustain spatial attention could be attributed to the increase in frontal areas of attentional control across these species. But the stability-related duration of attentional activity that is expressed in the circuitry of posterior cortical areas would still seem to be crucial for the modulation of early processing of incoming signals from a target stimulus that lead to faster and more accurate outputs. Unfortunately, cross-species comparisons of performance on delayedresponse tasks, encouraged by Walter Hunter’s early findings [43], have since been shown to be disadvantaged methodologically because the tasks employed in published animal experiments have changed with the species tested. Additional research is needed to test the present conjecture that the potential duration of sustained attention increases with apical dendrite length.

7. Evidence of apical dendrite activity during sustained attention from studies that use EEG, ERS/ERD, ERP, and LFP measures The perspective on apical dendrite function presented here is that cognitive states of sustained attention involve repeated rhythmic EPSP propagations in the apical dendrites of cortical pyramidal cells that form part of the thalamocortical recurrent circuit. Thus, the state of the apical dendrite membrane during sustained attention is presumed to be highly active, owing not only to the ‘‘background’’ subthreshold bombardment at the thousands of synapses that dot the long dendritic shaft, but also owing to the ‘‘foreground’’ activity of the repeated propagations of EPSPs along the apical dendrite as part of the corticothalamic recurrent circuit activity. It is proposed here that sustained attention to a specific cognitive content (e.g., to a shape, color, or location of an object) is expressed by synchronous activity in the many layer 5 apical dendrite clusters within cortical columns that code for that particular content. This activity is presumed to be relayed to layer 2/3 pyramidal cells within these columns, where it can be used to modulate the input – output processing of corticocortical signals. Fast synchronous activity in the apical dendrites of layer 2/3 pyramidal cells is crucial to maintaining relatively constant subthreshold levels of soma depolarization in layer 2/3 pyramidal cells during the time interval between a stimulus cue and stimulus target. Slow waves, e.g., in the alpha band, produce a series of soma depolarizations with relatively long inter-arrival times, resulting in widely spaced peaks in soma thresholds.

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The resulting large fluctuations in subthreshold depolarizations at the soma reduce the probability of effectively modulating a short train of corticocortical signal information that passes to the soma from the basal dendrites of layer 2/3 pyramidal cells. Hence, faster oscillatory activity, e.g., in the beta and gamma bands, is presumably required to maintain a relatively smooth level of soma subthreshold depolarization during the time when the appearance of a target produces incoming signal trains from other cortical columns. Evidence that fast oscillatory activity in apical dendrites takes place during sustained attention is given by studies that employ EEG, ERS/ERD, ERP, and LFP measures, all of which are based on the field potentials produced by a succession of EPSPs propagating along apical dendrites that are anatomically aligned in parallel. This anatomical arrangement enables synchronous electrical activity in thousands of apical dendrites to sum by superposition and produce an electrical dipole of appreciable strength. As a result, oscillations of the dipole electric field can reach the scalp and be recorded as EEGs [60,67]. 7.1. EEG evidence Watching a moving object and waiting for an object to appear both involve sustaining attention to a particular location in the visual field. Rougeul-Buser and her colleagues [10,86] recorded EEGs while cats were watching a mouse move within a transparent box and found coherent 40-Hz activity in the posterior parietal area as well as in the frontal area. Enhanced activity recorded in the posterior thalamus correlated with the appearance of these 40-Hz oscillations in the parietal area. Gamma band power has been shown to increase significantly when human subjects attend the location of a visual stimulus as compared to when the same stimulus is ignored [32]. In a display of multiple rectangles having varying shapes and colors, an arrow cue indicated the hemifield in which a single brown rectangle target would subsequently appear. When the attended hemifield began to rotate slowly, average spectral power in the 35– 51 Hz range at contralateral parieto-occipital and frontal regions was augmented from the average level observed when the rotating hemifield was ignored. In particular, after the onset of the arrow, the cortical source of the 35– 51 Hz activity shifted from a broad area of posterior cortex to a relatively narrow parieto-occipital site contralateral to the cued hemifield, where an increase in spectral power was also observed. A related study from the same laboratory [49] evaluated the stability of the gamma band activity and its topography in a selective attention task across four weekly sessions, and found high temporal stability of the nonphased-locked gamma band activity occurring in the time range of 200 – 500 ms following stimulus onset. In another study with human participants, central arrow cues were used to direct spatial attention to left or right locations of upcoming visual targets; and the results showed

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The presence of fast EEG oscillations can be inferred when slower oscillations show desynchronization. Named event-related desynchronization (ERD) by Pfurtscheller and Aranibar [78], this indicator of synchronous activity has been paired with event-related synchronization (ERS) in spectral power analyses of EEG recordings. In a simple finger movement task, ERDs of 10 Hz were found [79] along with ERSs of 40 Hz in time periods just prior to finger movements. For humans preparing to make simple movements (finger, toe, and tongue movements), brief periods of 40-Hz oscillations occurred approximately 300 ms prior to movement onsets, which were subsequently suppressed when the movement occurred [77]. In a self-paced movement task, patients with epilepsy showed a significant beta ERD with an embedded gamma ERS [80]. A study of visual preparatory attention [6,7] inferred synchronous activity in the visual cortex from ERDs observed in humans while they waited for a visual feedback display following a response that estimated a 20-s time interval.

directed to a cognitive content and sustained for a period of time in preparation for the target stimulus that was cued. When a stimulus that exhibits the attended attribute occurs in the cued location, apical dendrites of cortical columns that code for the attended attribute and those that code for the attended location presumably show a transient increase in dipole intensity. This hypothesis is supported by a study [73] of auditory responses in humans, in which the transient EEG response to an auditory stimulus showed a strong transient increase in amplitude for four or more cycles of 40 Hz oscillation. These oscillations were locked in phase with stimulus onset at latencies of 20 to 130 ms (recordings of these transient responses by EEG and MEG were virtually identical in form). These findings suggest the onset of the stimulus boosts the ongoing 40-Hz oscillations so that they stand out from other EEG activity in the background. When a target stimulus shares attributes with the nontarget stimulus, a transient increase in dipole intensity also will occur when the non-target stimulus appears, even in a non-attended location, owing to their shared cortical columns. However, the increase in dipole intensity will be somewhat less than the intensity produced by the target, because the activated target columns contain not only columns shared with the similar non-target but also additional columns that distinguish the target from the non-target. Many ERP studies of spatial attention in humans have found early (50 –250 ms post-stimulus) attention-related modulations of ERPs in several cortical areas, including the parietal cortex [23,24,34,35,37,40,61,107,108]. For example, early visual ERP enhancements were found in dorsal and ventral extrastriate areas as well as in parietal cortex [23], and the auditory ERP enhancements that had been reported in a previous study [37] were localized to the superior temporal auditory cortex [106]. A recent study [56] of attentional anticipation of a visual stimulus in the monkey recorded EEGs at occipital and temporal sites and found that the latencies and amplitudes of two ERP waves (one that peaked between 75 and 95 ms, and the other that peaked between 100 and 140 ms) were highly correlated with prefrontal network strength (peak power and coherence) measured by LFPs.

7.3. ERP evidence

7.4. LFP evidence

In the typical visual ERP task, attention is directed to a location by a visual cue, and the cued attention is sustained during the delay period leading up to a target. In the typical auditory ERP task, attention is directed to one ear and to a particular attribute of the auditory target. This attentional state is sustained while a series of brief ‘‘beeps’’ is presented. The subject is instructed to count a particular kind of ‘‘beep’’ (e.g., one with lower amplitude than the other ‘‘beeps’’), which usually has a low frequency of occurrence. Thus, when a target appears in either of these visual or auditory ERP tasks, attention has already been

The synchronization of field potentials in the gamma range was observed with implanted electrodes in parietal and motor sites of cats for both novel and expected stimuli during a visuomotor coordination task [85]. In monkeys, gamma synchronization of field potentials recorded in area V4 increased when the monkey attended to an object [27,57], and the increase in gamma synchronization was accompanied by a reduction in low frequency synchronization, which is similar to the ERS/ ERD effects found in studies described earlier. The implications of these and other related studies for top –

persistent EEG negativity, which increased at the centrally located electrode during the last half of a 2-s delay between cue and target [40]. A steady increase in EEG negativity was also found in the parietal area during the last half of the 2-s delay between a response and visual feedback [12]. One interpretation of the observed persistent negativity during periods of anticipating a stimulus, consistent with the present theoretical proposal, is that states of anticipation involve apical dendrite activity in columns that code for the anticipated content, and further increases in the intensity of anticipation recruit the active participation of more apical dendrites. Another interpretation is that the intensity of activity within each participating apical dendrite is increased, which raises the level of soma depolarization and produces pulse outputs in bursts. A third possibility is the combination of both of these kinds of changes. Reviews and discussions of the role of high-frequency cortical EEGs during non-spatial attention and other cognitive activities have appeared in several recent articles [33,68,69,95]. 7.2. ERS/ERD evidence

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down control of anticipation-related synchrony in posterior cortical areas are reviewed in [25].

8. Conclusion The overall hypothesis of this study is that apical dendrite activity, operating within a recurrent cortico – thalamo – cortical circuit, can provide stable levels of modulatory activity at the somas of cortical pyramidal neurons that can persist over extended intervals of time. The modulatory activity at the soma is assumed to be at subthreshold levels, where it can be integrated with input depolarizations arriving via basal dendrites. In particular, subthreshold depolarizations arising from ongoing apical dendrite activity can be sustained during periods of time when stimuli are not yet present but are anticipated. A high degree of stability in recurrent circuit activity may be accomplished by narrowing the variability of EPSPs within subthreshold levels at the soma. According to the present model of somatic modulation by decremental conduction, two main properties of the apical dendrite produce a strong reduction in the mean and variability of EPSPs at the soma: the dominance of inhibitory conductances along the apical dendrite shaft, and the distance the EPSP propagates between the synaptic site of initiation and the soma. It is suggested that the stability of modulatory activity at the soma of pyramidal neurons makes possible the sustaining of attention to the location and appearance of objects for prolonged periods of time. References [1] J.S. Anderson, M. Carandini, D. Forster, Orientation tuning of input conductance, excitation and inhibition in cat primary visual cortex, J. Neurophysiol. 84 (2000) 909 – 926. [2] S.V. Astafiev, G.L. Shulman, C.M. Stanley, A.Z. Snyder, D.C. Van Essen, M. Corbetta, Functional organization of human intraparietal and frontal cortex for attending, looking, and pointing, J. Neurosci. 23 (2003) 4689 – 4699. [3] L. Auclair, I. Jambaque, D. Olivier, D. LaBerge, E. Sieroff, Deficit of preparatory attention in children with frontal lobe epilepsy, Neuropsychologia (in press). [4] E. Awh, L. Anllo-Vento, S.A. Hillyard, The role of spatial selective attention in working memory for locations: evidence from eventrelated potentials, J. Cogn. Neurosci. 12 (2000) 840 – 847. [5] A. Baddeley, Working memory: looking back and looking forward, Nat. Rev., Neurosci. 4 (2003) 829 – 839. [6] M.C. Bastiaansen, C.H.M. Brunia, Anticipatory attention: an eventrelated desynchronization approach, Int. J. Psychophysiol. 43 (2001) 1 – 107. [7] M.C. Bastiaansen, K.B. Bocker, C.H.M. Brunia, J.C. de Muck, H. Spekreijse, Event-related desynchronization during anticipatory attention for an upcoming stimulus: a comparative EEG/MEG study, Clin. Neurophysiol. 112 (2001) 393 – 403. [8] D.B. Bender, M. Youakim, Effect of attentive fixation in macaque thalamus and cortex, J. Neurophysiol. 85 (2000) 219 – 234. [9] L.J. Borg-Graham, C. Monier, Y. Fregnac, Visual input invokes transient and strong shunting inhibition in visual cortical neurons, Nature 393 (1998) 369 – 373.

97

[10] J.J. Bouyer, M.F. Montaron, A. Rougeul, Fast fronto-parietal rhythms during combined focused attentive behavior and immobility in cat: cortical and thalamic localizations, Electroencephalogr. Clin. Neurophysiol. 51 (1981) 244 – 252. [11] V. Braitenberg, A. Schuz, Cortex: Statistics and Geometry of Neuronal Connectivity, 2nd edR, Springer, Berlin, 1998. [12] C.H.M. Brunia, E.J.P. Damen, Distribution of slow potentials related to motor preparation and stimulus anticipation in a time estimation task, Electroencephalogr. Clin. Neurophysiol. 69 (1988) 234 – 243. [13] A. Compte, N. Brunel, P.S. Goldman-Rakic, X.-J. Wang, Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model, Cereb. Cortex 10 (2000) 910 – 923. [14] D. Contreras, I. Timofeev, M. Steriade, Mechanisms of long lasting hyperpolarizations underlying slow sleep oscillations in cat corticothalamic networks, J. Physiol. 494 (1996) 251 – 264. [15] M. Corbetta, G.L. Shulman, Control of goal-directed and stimulusdriven attention in the brain, Nat. Rev., Neurosci. 3 (2002) 201 – 215. [16] M. Corbetta, J.M. Kincade, G.L. Shulman, Neural systems for visual orienting and their relationships to spatial working memory, J. Cogn. Neurosci. 14 (2002) 508 – 523. [17] J.T. Coull, C.D. Frith, R.S. Frackowiak, P.M. Grasby, A frontoparietal network for rapid visual information processing: a PET study of sustained attention and working memory, Neuropsychologia 34 (1996) 1085 – 1095. [18] J. DeFelipe, I. Farinas, The pyramidal neuron of the cerebral cortex: morphological and chemical characteristics of the synaptic inputs, Prog. Neurobiol. 3 (1992) 563 – 607. [19] J. DeFelipe, E.G. Jones, Cajal on the Cerebral Cortex: An Annotated Translation of the Complete Writings, Oxford Univ. Press, New York, 1988. [20] R. Desimone, M. Wessinger, L. Thomas, W. Schneider, Attentional control of visual perception: cortical and subcortical mechanisms, Cold Spring Harbor Symp. Quant. Biol. 55 (1990) 963 – 971. [21] A. Destexhe, D. Pare, Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo, J. Neurophysiol. 81 (1999) 1531 – 1547. [22] A. Destexhe, M. Rudolph, D. Pare, The high-conductance state of neocortical neurons in vivo, Nat. Rev., Neurosci. 4 (2003) 739 – 751. [23] F. DiRusso, A. Martinez, S.A. Hillyard, Source analysis of eventrelated cortical activity during visuo-spatial attention, Cereb. Cortex 13 (2003) 486 – 499. [24] M. Eimer, Spatial cueing, sensory gating and selective response preparation: an ERP study on visuo-spatial orienting, Electroencephalogr. Clin. Neurophysiol. 88 (1993) 408 – 420. [25] A.K. Engel, P. Fries, W. Singer, Dynamic predictions: oscillations and synchrony in top – down processing, Nat. Rev., Neurosci. 2 (2001) 704 – 716. [26] M.L. Feldman, Morphology of the neocortical pyramidal neuron, in: A. Peters, E.G. Jones (Eds.), Cellular Components of the Cerebral Cortex, Cereb. Cortex, vol. l, Plenum, New York, 1984, pp. 123 – 200. [27] P. Fries, J.H. Reynolds, A.E. Rorie, Desimone, Modulation of oscillatory neuronal synchronization by selective visual attention, Science 291 (2001) 1560 – 1563. [28] C.D. Frith, K.J. Friston, The role of the thalamus in ‘‘top – down’’ modulation of attention to sound, NeuroImage 4 (1996) 210 – 215. [29] J.M. Fuster, G.E. Alexander, Neuron activity related to short-term memory, Science 173 (1971) 652 – 654. [30] C.D. Gilbert, T.N. Wiesel, Clustered intrinsic connections in cat visual cortex, J. Neurosci. 3 (1983) 1116 – 1133. [31] P.S. Goldman-Rakic, Circuitry of primate prefrontal cortex and regulation of behavior by representational memory, in: F. Plum (Ed.), Handbook of Physiology, The Nervous System, Higher Functions of the Brain. Section 1, vol. V, American Physiological Society, Bathesda, MD, 1987, pp. 373 – 417, Part 1. Chap. 9. [32] T. Gruber, M.M. Muller, A. Keil, T. Elbert, Selective visual-spatial

98

[33]

[34]

[35]

[36]

[37]

[38]

[39]

[40]

[41]

[42]

[43] [44]

[45]

[46] [47]

[48]

[49]

[50]

[51] [52]

[53]

[54] [55]

D. LaBerge / Brain Research Reviews 50 (2005) 86 – 99 attention alters induced gamma band response in the human EEG, Clin. Neurophysiol. 110 (1999) 2074 – 2085. T. Gruber, P. Malinowski, M.M. Muller, Modulation of oscillatory brain activity and evoked potentials in a repetition priming task in the human EEG, Eur. J. Neurosci. 19 (2004) 1073 – 1082. M.R. Harter, L. Anllo-Vento, Visual-spatial attention: preparation and selection in children and adults, in: C.H.M. Brunia, M.N. Verbaten (Eds.), Event-Related Brain Research, Elsevier, Amsterdam, 1991, pp. 183 – 194. M.R. Harter, S.L. Miller, N.J. Price, M.E. LaLonde, A.L. Keyes, Neural processes involved in directing attention, J. Cogn. Neurosci. 1 (1989) 223 – 237. U. Hillenbrand, Subthreshold dynamics of the neural membrane potential driven by stochastic synaptic input, Phys. Rev. 66 (2002) 1 – 16. S.A. Hillyard, R.F. Hink, V.L. Schwent, T.W. Picton, Electrical signs of selective attention in the human brain, Science 182 (1973) 177 – 180. J.A. Hirsch, J.M. Alonso, R.C. Reid, L.M. Martinez, Synaptic integration in striate cortical simple cells, J. Neurosci. 18 (1998) 9517 – 9528. N. Ho, A. Destexhe, Synaptic background activity enhances the responsiveness of neocortical pyramidal neurons, J. Physiol. 84 (2000) 1488 – 1496. J.-M. Hopf, G.R. Mangun, Shifting visual attention in space: an electrophysiological analysis using high spatial resolution mapping, Clin. Neurophysiol. 111 (2000) 1241 – 1257. J.B. Hopfinger, M.H. Buonocore, G.R. Mangun, The neural mechanisms of top – down attentional control, Nat. Neurosci. 3 (2000) 284 – 291. M. Hubener, C. Schwarz, J. Bolz, Morphological types of projection neurons in layer 5 of cat visual cortex, J. Comp. Neurol. 301 (1990) 655 – 674. W.S. Hunter, The delayed reaction in animals, Behav. Mono. 2 (1912) 1 – 85. A.P. Jha, Tracking the time-course of attentional involvement in spatial working memory: an event-related potential investigation, Brain Res. Cogn. Brain Res. 15 (2002) 61 – 69. D. Johnston, J.C. Magee, C.M. Colbert, B.R. Christie, Active properties of neuronal dendrites, Annu. Rev. Neurosci. 19 (1996) 165 – 186. E.G. Jones, Thalamic circuitry and thalamocortical synchrony, Philos. Trans. R. Soc. Lond., B Biol. Sci. 357 (2002) 1659 – 1673. J. Jonides, E.E. Smith, R.A. Koeppe, E. Awh, S. Minoshima, M.A. Mintun, Spatial working memory in humans as revealed by PET, Nature 363 (1993) 623 – 625. S. Kastner, M.A. Pinsic, P. DeWeerd, R. Desimone, L. Ungerleider, Increased activity in human visual cortex during directed attention in the absence of visual stimulation, Neuron 22 (1999) 751 – 761. A. Keil, M. Stolarova, S. Heim, T. Gruber, M.M. Muller, Temporal stability of high-frequency brain oscillations in the human EEG, Brain Topogr. 16 (2003) 101 – 110. K. Kubota, H. Niki, Prefrontal cortical unit activity and delayed alternation performance in monkeys, J. Neurophysiol. 34 (1971) 337 – 347. D. LaBerge, Attention, awareness, and the triangular circuit, Cons. Cogn. 6 (1997) 149 – 181. D. LaBerge, M.S. Buchsbaum, Positron emission tomographic measurements of pulvinar activity during an attention task, J. Neurosci. 10 (1990) 613 – 619. D. LaBerge, M. Carter, V. Brown, A network simulation of thalamic circuit operations in selective attention, Neural Comput. 4 (1992) 318 – 331. D. LaBerge, L. Auclair, E. Sieroff, Preparatory attention: experiment and theory, Cons. Cogn. 9 (2000) 396 – 434. H.C. Leung, J.C. Gore, P.S. Goldman-Rakic, Sustained mnemonic

[56]

[57]

[58]

[59]

[60]

[61]

[62]

[63]

[64]

[65]

[66]

[67] [68]

[69]

[70]

[71]

[72]

[73]

[74]

[75]

response in the human middle frontal gyrus during on-line storage of spatial memoranda, J. Cogn. Neurosci. 14 (2002) 659 – 671. H. Liang, S.L. Bressler, M. Ding, W.A. Truccolo, R. Nakamura, Synchronized activity in prefrontal cortex during anticipation of visuomotor processing, NeuroReport 13 (2002) 2011 – 2015. H. Liang, S.L. Bressler, M. Diong, R. Desimone, P. Fries, Temporal dynamics of attention-modulated neuronal synchronization in macaque V4, Neurocomputing 52 (2003) 481 – 487. M. Liotti, P.T. Fox, D. LaBerge, PET measurements of attention to closely spaced visual shapes, Abstr.-Soc. Neurosci. 20 (1994) 354. R.R. Llinas, The intrinsic electrophysiological properties of mammalian neurons: insights in to central nervous system function, Science 242 (1988) 1654 – 1664. F.H. Lopes da Silva, Neural mechanisms underlying brain waves: from neural membranes to networks, Electroencephalogr. Clin. Neurophysiol. 79 (1991) 81 – 93. G.R. Mangun, Orienting attention in the visual fields: an electrophysiological analysis, in: H.J. Heinze, T.F.F. Munte, G.R. Mangun (Eds.), Cogn. Electrophysiol., Birkhauser, Boston, MA, 1994, pp. 81 – 101. G. McCarthy, A. Puce, R.T. Constable, J.H. Krystal, J.C. Gore, P.S. Goldman-Rakic, Activation of human prefrontal cortex during spatial and nonspatial working memory: tasks measured by functional MRI, Cereb. Cortex 6 (1996) 600 – 611. D.A. McCormick, M. von Krosigk, Corticothalamic activation modulates thalamic firing through glutamate ‘‘metabotropic’’ receptors, Proc. Natl. Acad. Sci. U. S. A. 89 (1992) 2774 – 2778. D.A. McCormick, Y. Shu, A. Hasenstaub, M. Sanchez-Vives, M. Badoual, T. Bal, Persistent cortical activity: mechanisms of generation and effects on neuronal excitability, Cereb. Cortex 13 (2003) 1219 – 1231. J.S. Morris, K.J. Friston, R.J. Dolan, Neural responses to salient visual stimuli, Proc. R. Soc. London, Ser. B. Biol Sci. 264 (1997) 769 – 775. V.B. Mountcastle, Modality and topographic properties of single neurons in cat’s somatic sensory cortex, J. Neurophysiol. 20 (1957) 408 – 434. V.B. Mountcastle, The Cerebral Cortex, Harvard Univ. Press, Cambridge, MA, 1998. M.M. Muller, A. Keil, Neuronal synchronization and selective color processing in the human brain, J. Cogn. Neurosci. 16 (2004) 503 – 522. M.M. Muller, T. Gruber, A. Keil, Modulation of induced gamma band activity in the human EEG by attention and visual information processing, Int. J. Psychophysiol. 38 (2000) 283 – 299. S. Neuenschwander, W. Singer, Long-range synchronization of oscillatory light responses in the cat retina and lateral geniculate nucleus, Nature 379 (1996) 728 – 733. L.G. Nowak, M.V. Sanchez-Vives, D.A. McCormick, Influence of low and high frequency inputs on spike timing in visual cortical neurons, Cereb. Cortex 7 (1997) 487 – 501. A.M. Owen, A.C. Evans, M. Petrides, Evidence for a two-stage model of spatial working memory processing within the lateral frontal cortex: a positron emission tomography study, Cereb. Cortex 6 (1996) 31 – 38. C. Pantev, S. Makieg, M. Hoke, R. Galambos, S. Hampson, C. Gallen, Human auditory evoked gamma-band magnetic fields, Proc. Natl. Acad. Sci. U. S. A. 88 (1991) 8996 – 9000. D. Pare, E. Shink, H. Gaudreau, A. Destexhe, E.J. Lang, Impact of spontaneous synaptic activity on the resting properties of cat neocortical pyramidal neurons in vivo, J. Neurophysiol. 79 (1998) 1450 – 1460. R.E. Passingham, J.B. Rowe, Dorsal prefrontal cortex: maintenance in memory or attentional selection? in: D.T. Stuss, R.T. Knight (Eds.), Principles of Frontal Lobe Function, Oxford Univ. Press, New York, 2002, pp. 221 – 232.

D. LaBerge / Brain Research Reviews 50 (2005) 86 – 99 [76] A. Peters, C. Sethares, Organization of pyramidal neurons in area 17 of monkey visual cortex, J. Comp. Neurol. 306 (1991) 1 – 23. [77] G. Pfurtscheller, Event-related desynchronization (ERD) and 40-Hz oscillations in a simple movement task, in: C. Pantev, T. Elben, B. Lutkenhoner (Eds.), Oscillatory Event-related Brain Dynamics, Plenum Press, New York, 1994, pp. 357 – 366. [78] G. Pfurtscheller, A. Aranibar, Event-related cortical desynchronization detected by power measurements of scalp EEG, Electroencephalogr. Clin. Neurophysiol. 42 (1977) 817 – 826. [79] G. Pfurtscheller, C. Neuper, Simultaneous EEG 10 Hz desynchronization and 40 Hz synchronization during finger movements, NeuroReport 3 (1992) 1057 – 1060. [80] G. Pfurtscheller, B. Graimann, J.E. Huggins, S.P. Levine, L.A. Schuh, Spatiotemporal patterns of beta desynchronization and gamma synchronization in corticographic data during self-paced movement, Clin. Neurophysiol. 114 (2003) 1226 – 1236. [81] W. Rall, Core conductor theory and cable properties of neurons, in: E.R. Kandell, T.M. Brookhardt, V.B. Mountcastle (Eds.), The Nervous System: Cellular Biology of Neurons, Handbook of Physiology, vol. 1, Williams and Wilkins, Co., Bethesda, MD, 1977, pp. 139 – 198. [82] W. Rall, Cable theory for dendritic neurons, in: C. Koch, I. Segev (Eds.), Methods in Neuronal Modeling, MIT Press, Cambridge MA, 1989, pp. 9 – 62. [83] A. Reyes, Influence of dendritic conductances on the input – output properties of neurons, Annu. Rev. Neurosci. 24 (2001) 653 – 675. [84] A.J. Rockel, R.W. Hiorns, T.P.S. Powell, The basic uniformity in structure of the neocortex, Brain 103 (1980) 221 – 244. [85] P.R. Roelfsema, A.K. Engel, P. Konig, W. Singer, Visuomotor integration is associated with zero-time lag synchronization among cortical areas, Nature 385 (1997) 157 – 161. [86] A. Rougeul-Buser, P. Buser, Electrocortical rhythms in the attentive cat: phenomenological data and theoretical issues, in: C. Pantev (Ed.), Oscillatory Event-Related Brain Dynamics, Plenum Press, New York, 1994. [87] M. Rudolph, A. Destexhe, A fast-conducting, stochastic integrative mode for neocortical neurons in vivo, J. Neurosci. 23 (2003) 2466 – 2476. [88] M. Rudolph, A. Destexhe, Characterization of subthreshold voltage fluctuations in neuronal membranes, Neural Comput. 15 (2003) 2577 – 2618. [89] C.E. Schroeder, A.D. Mehta, J.J. Foxe, Determinants and mechanisms of attentional modulation of neural processing, Frontiers Biosci. 6 (2001) 672 – 684. [90] Y. Shu, A. Hasenstaub, D.A. McCormick, Turning on and off recurrent balanced cortical activity, Nature 423 (2003) 288 – 293. [91] G.L. Shulman, M. Corbetta, R.L. Buckner, J.A. Fiez, F.M. Miezin, M.E. Raichle, S.E. Petersen, Common blood flow

[92]

[93]

[94] [95]

[96] [97] [98] [99]

[100] [101]

[102]

[103]

[104] [105]

[106]

[107]

[108]

99

changes across visual tasks: I. Increases in subcortical structures and cerebellum but not in nonvisual cortex, J. Cogn. Neurosci. 9 (1997) 624 – 647. G.L. Shulman, M. Corbetta, R.L. Buckner, M.E. Raichle, J.A. Fiez, F.M. Miezin, S.E. Petersen, Top – down modulation of early sensory cortex, Cereb. Cortex 7 (1997) 193 – 206. E. Sieroff, A. Piquard, L. Auclair, L. Lacomblez, C. Derouesne, D. LaBerge, Deficit of preparatory attention in frontal-temporal dementia, Brain Cogn. 55 (2004) 444 – 451. M. Steriade, E.G. Jones, D.A. McCormick, Thalamus, vol. 1, Elsevier, New York, 1997. C. Tallon-Baudry, O. Bertrand, C. Fischer, Oscillatory synchrony between human extrastriate areas during visual short-term memory maintenance, J. Neurosci. 21 (2001) RC177. J. Tegner, A. Compte, X.-J. Wang, The dynamical stability of reverberatory neural circuits, Biol. Cyber. 87 (2002) 471 – 481. A. Treisman, Features and objects: the Fourteenth Bartlett Memorial Lecture, Q. J. Exp. Psychol., A 40 (1988) 201 – 237. A. Treisman, S. Sato, Conjunction search revisited, J. Exp. Psychol. Hum. Percept. Perform. 16 (1990) 459 – 478. F. Valverdi, The organizing principles of the primary visual cortex in the monkey, in: A. Peters, E.G. Jones (Eds.), Cereb. Cortex, vol. 3, Plenum Press, New York, 1985, pp. 207 – 257. F. Valverdi, Intrinsic neocortical organization: some comparative aspects, Neuroscience 18 (1986) 1 – 23. M. Volgushev, M. Chistiakova, W. Singer, Modification of discharge patterns of neocortical neurons by induced oscillations of the membrane potential, Neuroscience 83 (1998) 15 – 25. M. Volgushev, J. Pernberg, U.T. Eysel, A novel mechanism of response selectivity of neurons in cat visual cortex, J. Physiol. (London) 540 (2002) 307 – 320. M. Volgushev, J. Pernberg, U.T. Eysel, Gamma-frequency fluctuations of the membrane potential and response selectivity in visual cortical neurons, Eur. J. Neurosci. 17 (2003) 1768 – 1776. X.-J. Wang, Synaptic reverberation underlying mnemonic persistent activity, Trends Neurosci. 24 (2001) 455 – 463. S.R. Williams, Spatial compartmentalization and functional impact of conductance in pyramidal neurons, Nat. Neurosci. 7 (2004) 961 – 967. M.G. Woldorff, C.C. Gallen, S.A. Hampson, S.A. Hillyard, C. Pantev, D. Sobel, F.E. Bloom, Modulation of early sensory processing in human auditory cortex during auditory selective attention, Proc. Natl. Acad. Sci. U. S. A. 90 (2003) 8722 – 8726. S. Yamaguchi, H. Tsuchiya, S. Kobayashi, Electroencephalographic activity associated with shifts of visuospatial attention, Brain 117 (1994) 553 – 562. S. Yamaguchi, H. Tsuchiya, S. Kobayashi, Electrophysiologic correlates of age effects on visuospatial attention shift, Cogn. Brain Res. 3 (1995) 41 – 49.