Ongoing Spontaneous Activity Cont...
Ongoing Spontaneous Activity Controls Access to Consciousness: A Neuronal Model for Inattentional Blindness Stanislas Dehaene1*, Jean-Pierre Changeux2 1 INSERM-CEA Unit 562, Cognitive Neuroimaging, Service Hospitalier Frederic �� �� Joliot, Orsay, France, 2 CNRS URA2182 Recepteurs �� and Cognition, Institut Pasteur, Paris, France Even in the absence of sensory inputs, cortical and thalamic neurons can show structured patterns of ongoing spontaneous activity, whose origins and functional significance are not well understood. We use computer simulations to explore the conditions under which spontaneous activity emerges from a simplified model of multiple interconnected thalamocortical columns linked by long-range, top-down excitatory axons, and to examine its interactions with stimulus-induced activation. Simulations help characterize two main states of activity. First, spontaneous gamma-band oscillations emerge at a precise threshold controlled by ascending neuromodulator systems. Second, within a spontaneously active network, we observe the sudden ������ignition������ of one out of many possible coherent states of high-level activity amidst cortical neurons with long-distance projections. During such an ignited state, spontaneous activity can block external sensory processing. We relate those properties to experimental observations on the neural bases of endogenous states of consciousness, and particularly the blocking of access to consciousness that occurs in the psychophysical phenomenon of ������inattentional blindness,������ in which normal subjects intensely engaged in mental activity fail to notice salient but irrelevant sensory stimuli. Although highly simplified, the generic properties of a minimal network may help clarify some of the basic cerebral phenomena underlying the autonomy of consciousness. Citation: Dehaene S, Changeux JP (2005) Ongoing spontaneous activity controls access to consciousness: A neuronal model for inattentional blindness. PLoS Biol 3(5): e141. Introduction Ongoing spontaneous activity is present throughout the nervous system , but its function remains enigmatic. In the embryo, spontaneous movements  and waves of endoge- nous retinal activity [3,4] are thought to play an important role in the epigenesis of neural networks through selective synapse stabilization [5,6]. Ongoing spontaneous activity is also present in the adult brain, where it is responsible for the highly variable patterns of the electroencephalogram (EEG). Thalamocortical networks generate a variety of oscillations whose rhythms change across the sleep-wake cycle [7,8,9]. Optical imaging methods in anesthetized animals also reveal fast spontaneous states of neuronal activity that, far from being random, exhibit patterns that resemble those evoked by external stimuli [10,11]. In parallel, functional neuroimaging studies in humans have shown a globally elevated brain metabolism at rest, with localized patterns suggesting that particular cortical regions are maintained in a high, although variable, state of activity [12,13,14,15,16]. At present, the functional roles of this spontaneous activity in the adult brain at rest remains to be elucidated. In previous neuronal modeling studies and computer simulations, we illustrated the possible contribution of spontaneous activity to tasks that involve a random search, such as the learning of a temporal sequence , the search for and selection of the correct rule in the delayed response and Wisconsin card-sorting tests , or the discovery of a multistep solution in the Tower of London test . More recently, generalizing from this early work, we proposed a broader framework of a formal architecture of thalamocort- ical areas, in which top-down activity generated in hierarchi- cally higher cortical areas plays a key role in what we referred to as ������access to consciousness������ in an effortful task [20,21,22,23]. Like several previous proposals, our model of a conscious neuronal workspace distinguishes lower autom- atized systems from increasingly higher and more autono- mous supervisory systems . It also builds upon Baars��� cognitive theory of consciousness, which distinguishes a vast array of unconscious specialized processors running in parallel, and a single limited-capacity serial ������workspace������ that allows them to exchange information . The proposed neuronal architecture separates, in a first minimal description, two computational spaces, each char- acterized by a distinct pattern of connectivity. Subcortical networks and most of the cortex can be viewed as a collection of specialized and automatized processors, each attuned to the processing of a particular type of information via a limited number of local or medium-range connections that bring to each processor the ������encapsulated������ inputs necessary to its function. On top of this automatic level, we postulate a Received October 4, 2004 Accepted February 16, 2005 Published April 12, 2005 DOI: 10.1371/journal.pbio.0030141 Copyright: �� 2005 Dehaene et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abbreviations: EEG, electroencephalogram LFP, local field potential Academic Editor: Larry Abbott, Brandeis University, United States of America *To whom correspondence should be addressed. E-mail: firstname.lastname@example.org PLoS Biology | www.plosbiology.org May 2005 | Volume 3 | Issue 5 | e141 0910 Open access, freely available online PLoS BIOLOGY
distinct set of cortical ������workspace������ neurons characterized by their ability to send and receive projections to many distant areas through long-range excitatory axons, thus allowing many different processors to exchange information. Our previous simulations demonstrated how this architec- ture could account for a psychological phenomenon, the ������attentional blink.������ Because of its long-distance, brain-scale connectivity, the global workspace establishes a central processing bottleneck such that, in the presence of two competing stimuli, processing of the first temporarily blocks high-level processing of the second . While this work simulated only sensory processing, a key hypothesis of the workspace model is that the neurons of the higher level, the workspace neurons, are the seat of a permanent spontaneous activity that creates a succession of active internal states [20,21,23]. The aim of the present paper is to explore in a more extensive and systematic manner the role of this ongoing spontaneous activity in a similar neural network comprising several nested levels of neuronal architecture. We propose a specific network architecture and perform explicit computer simulations that offer plausible explanations for the origins and function of structured spontaneous activity in adult thalamocortical circuits, and in particular its critical role in allowing or blocking access by sensory stimuli. The observed dynamic properties of the network lead us to distinguish two main transitions in activation. First, a neuromodulatory substance is assumed to control the level of network activation as its input increases continuously, the network exhibits a sudden surge in spontaneous activation and switches to a state of thalamocortical resonance characterized by temporary bouts of synchronized gamma- band oscillations of increasing amplitude. This state of activity leads to a facilitation of sensory processing, and is proposed to correspond to the state of vigilance or being awake. When the simulated areas are reciprocally connected by long-distance excitatory connections, a second state tran- sition can occur. A subset of areas may suddenly show a strong temporary increase in synchronized firing and form a coherent state of activity (������ignition������). The transition to this state of high correlated activity is fast and characterized by an amplification of local neural activation and the subsequent ignition of multiple distant areas. This state of activity competes with, rather than facilitates, sensory processing, and thus leads to an extinction of sensory processing. We propose that this blocking may account for the ������inattentional blind- ness������ phenomenon, in which normal subjects intensely engaged in mental activity fail to notice salient but task- irrelevant sensory stimuli [26,27,28]. Results We used computer simulations to characterize spontaneous and evoked activity in a complex nested architecture comprising multiple neurons, columns, and areas (Figure 1). To facilitate comprehension, we organize the results section as a progression from local to more global states of activity. We start by describing the spontaneous and evoked activity in the building blocks of the model, namely the single neuron and an isolated thalamocortical column. We then consider the extent to which those properties are affected when multiple thalamocortical columns are interconnected by long-distance, bottom-up and top-down connections. Spontaneous Oscillatory Behavior in a Single Neuron and a Thalamocortical Column We first simulated a single neuron using the ������cellular oscillator������ model derived from Wang . The results appear in Figure 2A. In the absence of any depolarizing current, the resting membrane potential is stable at V ��� 63 mV. Injection of an increasing depolarizing current Ineuromodul leads to the sudden emergence, at a rather precise value (Ineuromodul��� 1.1 lA/cm2), of oscillations in membrane potential in the gamma range. Two features characterize this transition as a supercritical Hopf bifurcation according to dynamical systems theory . First, a discontinuous tran- sition is observed in the frequency domain, with the oscillation emerging suddenly at a characteristic frequency of 30���35 Hz, and changing only slowly with increasing current (up to 40���45 Hz). Second, a continuous transition is observed in the amplitude domain, with oscillation amplitude increas- ing continuously from zero as the square root of the amount of deviation from the threshold current (and therefore power increases linearly, as shown in Figure 2A). Around Ineuromodul ��� 1.7 lA/cm2, a second threshold is observed: When oscillation amplitude reaches the voltage threshold for spiking, a spike is generated. Firing rate increases essentially linearly above this threshold. Overall, those properties of the model are similar to the gamma-band (������40 Hz������) subthreshold membrane oscillations observed in intracellular recordings of thalamic and cortical neurons [7,31,32,33]. In both our simulations and these experiments, oscillations emerge at a precise depolarization threshold, with a sudden well-defined frequency and a continuously increasing amplitude. Similar properties continued to be observed when 120 such oscillatory neurons, with randomized membrane parameters, were interconnected in a model thalamocortical column (as described in Materials and Methods). Figure 2B shows the temporal evolution of the average local field potential (LFP) emitted by the cortical excitatory neurons in response to variable levels of injected current Ineuromodul. There is still a threshold, now lowered to Ineuromodul ��� 0.8 lA/cm2, at which gamma-band oscillations emerge with a fixed frequency (initially 30���35 Hz) and with continuously increasing ampli- tude. The lowering of the threshold is due to random variability between neurons in the conductance of the Na�� and K�� channels responsible for generating membrane oscillations. Some neurons begin to oscillate at a lower value of the injected current, thus smoothing out the sharp transition observed within each single neuron. For the same reason, the spiking threshold is also lowered in this single- column simulation, with the number of emitted spikes increasing smoothly starting around Ineuromodul ��� 0.9 lA/ cm2. In the absence of spikes, the membrane oscillations of different neurons are independent of each other. Spikes, however, introduce coupling and result in transient periods of synchrony, which appear as occasional increases changes in firing rate accompanied by high-amplitude LFPs and a strong synchronization of the thalamocortical column. Continuous plotting of the LFP reveals a spontaneous, semirandom waxing and waning of bouts of coherent gamma-band PLoS Biology | www.plosbiology.org May 2005 | Volume 3 | Issue 5 | e141 0911 Spontaneous Activity and Access to Consciousness
oscillations with typical durations of 100���150 ms (Figures 1B and 2B). The temporal evolution of the LFP appears largely chaotic and unpredictable, although the underlying simula- tion is strictly deterministic. Altogether, these properties are comparable to the synchronized, depolarization-dependent, high-frequency tha- lamocortical oscillations that have been observed, for instance, in the cat thalamus and cortex [34,35]. For simplicity, we did not include mechanisms for sleep-related, low-frequency oscillations, which are not the focus of the present model but have been simulated by others [9,36]. Thalamocortical Resonance without Intrinsic Oscillators To evaluate the role of intrinsic cellular oscillators in generating the above thalamocortical oscillations, we reiter- ated the simulations using the ������random spikes������ model, with simplified neurons devoid of the Na�� and K�� channels, but with a random (Gaussian) moment-to-moment variability in spike initiation threshold. We verified that a single such passive, single-compartment, integrate-and-fire neuron, faced with a constant input current, is incapable of generating membrane oscillations below the spiking threshold (p. 163, ). In a single neuron, oscillations appear only once the injected neuromodulatory current is sufficient to depolarize the neuron beyond the spiking threshold. Even then, they do not exhibit a fixed central frequency, but cover a broad spectrum that progressively increases and broadens starting at 0 Hz, and stays mostly below 30 Hz with the present parameters (Figure 2C). Despite those major differences at the single-unit level, when 120 such neurons were connected into a thalamocort- ical column architecture, we observed structured sponta- neous activity and phase transitions analogous to those of the cellular oscillator model (Figure 2D compare with Figure 2B). Once a sufficient number of neurons were depolarized above the spiking threshold, the local field potential began to wax and wane within a range of gamma-band frequencies. Although this band was initially rather broad, it quickly narrowed to a predominant band at 40���45 Hz for higher values of the injected current. As shown in Figure 2D, the critical properties of continuously increasing oscillation amplitude with a well-characterized frequency range re- mained, due no longer to intrinsic membrane properties, but to the temporal filtering properties of the several connection loops present in the thalamocortical column. Those loops have the effect of filtering random spiking activity, thus biasing neurons toward generating spikes at recurrent, rather randomly organized times. For instance, a major excitatory loop circles from the thalamus to the layer IV, supragranular, and infragranular cortical neurons, and finally back to the thalamus. In the simulation, the total length of synaptic delays along this loop was 15 ms, which, combined with membrane integration times, resulted in a total of approximately 25 ms/ cycle, thus biasing the system toward 40-Hz oscillations. Because intrinsic membrane oscillations have been re- ported, particularly in thalamic neurons , we also simulated a third type of model in which only a small subset of neurons were intrinsic cellular oscillators (the excitatory thalamic neurons, or 16.6% of the simulated cells), and all other neurons were of the ������random spikes������ type. The results, which appear on Figure 2E, indicate the presence of waxing-and- waning LFP oscillations within a much narrower band of the gamma range than in the model with nonoscillating integrate-and-fire neurons. Thus, a small proportion of intrinsic oscillators, in resonance with the delays associated with recurrent thalamocortical connectivity, suffices to generate spontaneous activity with precise characteristics. Facilitation of External Inputs by Spontaneous Activity We then examined how spontaneous activity affects activation caused by external stimuli. To this end, we measured the number of spikes evoked during stimulation by a 500-ms depolarizing current pulse of variable intensity, while orthogonally varying the amount of ascending neuro- Figure 1. Simulation Components and Resulting Spontaneous Activity Shown are the constituents of the simulation (upper diagrams) and typical patterns of spontaneous activity that they can produce (lower tracings). We simulated a nested architecture in which spiking neurons (A) are incorporated within thalamocortical columns (B), which are themselves interconnected hierarchically by local and long-distance cortical connections (C) (see Materials and Methods for details). While single neurons may generate sustained oscillations of membrane potentials (A), only the column and network levels generate complex waxing-and- waning EEG-like oscillations (B) and metastable global states of sustained firing (C). DOI: 10.1371/journal.pbio.0030141.g001 PLoS Biology | www.plosbiology.org May 2005 | Volume 3 | Issue 5 | e141 0912 Spontaneous Activity and Access to Consciousness