PLoS Computational Biology
June 12, 2008
David Balduzzi, Giulio Tononi
433 citations
A new time- and state-dependent measure of integrated information, phi, quantifies how much information is generated when a system enters a particular state through causal interactions among its elements, beyond what its parts generate independently. This captures two key properties of consciousness: a large repertoire of experiences that rule out others when one occurs, and the integration of that information into a whole that cannot be decomposed. Applied to discrete networks, phi varies with the state entered, being higher when active and inactive elements are balanced and lower when the network is inactive or hyperactive.
PLoS Computational Biology
August 13, 2009
David Balduzzi, Giulio Tononi
272 citations
Integrated information theory proposes that consciousness corresponds to the amount of integrated information generated by a system of elements, and the quality of an experience is determined by the informational relationships within that system. This paper introduces qualia space (Q), where each possible state of a system is an axis, and submechanisms specify repertoires of states. Arrows between these repertoires define informational relationships that together form a shape—a quale—that uniquely characterizes a conscious experience. The quantity of consciousness is the height of this shape (phi). Entanglement measures how irreducible these relationships are. The framework implies that the same activity pattern can yield different qualia in different systems, and vice versa. Experience cannot be reduced to local mechanisms but requires the entire quale.
PLoS Computational Biology
January 20, 2011
Adam B. Barrett, Anil K. Seth
243 citations
Two new measures of integrated information, Φ(E) and Φ(AR), overcome limitations of the earlier Φ(DM) measure, which could rarely be applied to biological systems because it required discrete Markov dynamics. The new measures are easy to apply to time-series data, as demonstrated through simulations. They offer new opportunities for studying information integration in real and model systems and have implications for understanding consciousness and other neurocognitive processes. However, the findings also challenge theories that assign physical meaning to these measured quantities.
PLoS Computational Biology
November 7, 2007
Marco Loh, Edmund T. Rolls, Gustavo Deco
172 citations
Reduced depth in the basins of attraction of cortical attractor states destabilizes neural activity at the network level due to constant statistical fluctuations from stochastic spiking of neurons. In integrate-and-fire network simulations, decreasing NMDA receptor conductances reduces attractor basin depth, destabilizes short-term memory states, and increases distractibility. Cognitive symptoms of schizophrenia—distractibility, working memory deficits, poor attention—could stem from this instability in prefrontal cortical networks. Lower firing rates in orbitofrontal and anterior cingulate cortex may account for negative symptoms like reduced emotions. Decreasing both GABA and NMDA conductances causes switches between attractor states and jumps from spontaneous activity into attractors, linked to positive symptoms such as delusions, paranoia, and hallucinations from shallow basins in temporal lobe semantic memory networks.
PLoS Computational Biology
April 14, 2015
Joon-Young Moon, UnCheol Lee, Stefanie Blain‐moraes et al.
143 citations
Efficient brain networks balance global integration with functional specialization, but how global topology, local node dynamics, and information flow relate has been unclear. Using analytical solutions of oscillator models, computational simulations on model and anatomical brain networks, and high-density electroencephalography from conscious and anesthetized humans, the authors demonstrate that network nodes with more connections (higher degree) have larger amplitudes and are directional targets (phase lag) rather than sources (phase lead). This degree–directionality relationship appears to be a fundamental network property with direct applicability to brain function. Changes in directionality patterns across states of human consciousness are driven by alterations in brain network topology.
PLoS Computational Biology
August 30, 2018
Hyoungkyu Kim, Joon-Young Moon, George A. Mashour et al.
79 citations
Hysteresis—the difference between the forward and reverse paths of state transitions—occurs as people lose and regain consciousness. Analyzing high-density EEG from healthy volunteers given sevoflurane or ketamine, the authors found that functional brain networks exhibit hysteresis during these transitions. The principle of explosive synchronization, which governs abrupt state shifts in many complex networks, also explains hysteresis in the brain. More potent anesthetics produce larger hysteresis; a broader range of EEG frequencies hastens the loss of consciousness but delays its return; connectivity shows greater hysteresis than EEG power; and network structure and strength reconfigure differently during loss versus recovery. These results indicate that hysteresis in conscious state transitions is a generic network feature, potentially allowing prediction and modulation of such transitions.
PLoS Computational Biology
August 2, 2023
Alexander Tscshantz, Beren Millidge, Anil K. Seth et al.
56 citations
Predictive coding theory holds that the brain perceives by minimizing prediction errors through cycles of neural activity. However, some visual perception, including complex object recognition, happens too quickly for such cycles. This paper proposes that the initial fast 'feedforward sweep' performs amortized inference, using a learned function to map data directly to beliefs, while slower recurrent processing performs iterative inference, sequentially updating beliefs for greater accuracy. A hybrid predictive coding network combining both methods is introduced, implemented in a biologically plausible neural architecture using local Hebbian rules. The hybrid model achieves rapid perception for familiar data while retaining context-sensitivity and sample efficiency for novel situations, and adaptively balances both inference modes based on uncertainty.
PLoS Computational Biology
August 18, 2021
Indie C. Garwood, S. Chakravarty, Jacob Donoghue et al.
32 citations
Ketamine, an anesthetic that blocks NMDA receptors, produces alternating bursts of gamma (25-50 Hz) and slow-delta (0.1-4 Hz) brain oscillations. A hidden Markov model fitted to local field potentials from two non-human primates and electroencephalograms from nine humans quantified these dynamics. Gamma activity lasted on average 2.2 seconds in one primate, 1.2 in the other, and 2.5 in humans; slow-delta lasted 1.6, 1.0, and 1.8 seconds respectively. Five sub-states with regular sequential transitions were identified. These findings provide quantitative constraints for models of rhythm generation underlying ketamine-induced altered arousal.
PLoS Computational Biology
August 24, 2017
Peter J. Hellyer, Claudia Clopath, Angie A. Kehagia et al.
21 citations
A simple computational model of spontaneous neural dynamics controlling an agent in a virtual environment shows that brain-environment feedback can rapidly destabilize neural and behavioral dynamics, requiring homeostatic mechanisms. Local homeostatic plasticity, where inhibition adjusts to balance excitation, and global mechanisms, where regional task-negative activity compensates for task-positive sensory input in another region, both stabilize behavior. The results suggest complementary functional roles for local and macroscale homeostatic processes and propose a novel function for macroscopic task-negative activity patterns, such as the default mode network, in maintaining stable neural and behavioral dynamics.
PLoS Computational Biology
October 28, 2025
Fernando Lehue, Carlos Coronel‐Oliveros, Vicente Medel et al.
1 citation
During sleep, brain dynamics shift from wakefulness through NREM stages N1, N2, and N3, driven partly by decreases in the neuromodulators acetylcholine (ACh) and noradrenaline (NA). Analyzing fMRI data from healthy individuals and using a whole-brain model, the study shows that functional connectivity (FC) changes distinctly: locus coeruleus connectivity with the cortex decreases during N2 and N3, while basal forebrain connectivity with the cortex decreases during N3. Compared to wakefulness, the brain becomes more integrated in N1 and more segregated in N3. Region-specific neurotransmitter effects are key to explaining these FC changes, advancing understanding of how neurochemistry modulates sleep stages and consciousness transitions.
PLoS Computational Biology
December 15, 2025
Lingyu Zhang, Weiyang Shi, Ziyang Zhao et al.
LSD increases global brain synchrony and dynamic complexity by stabilizing a globally synchronized, functionally non-modular brain state that acts as an attractor, recruiting transitions from cognitive control networks. This enhanced synchrony arises from a convergence of excitatory/inhibitory balance across cortical hierarchies, driven by suppression in sensorimotor cortices and potentiation in transmodal regions. Sensorimotor cortices emerge as potential regulatory hubs for this rebalancing. The resulting brain state shows weakened sensory anchoring and enhanced cognitive flexibility, blurring the line between concrete perception and abstract cognition. This neurophysiological remodeling may underlie LSD's hallucinatory effects and its therapeutic potential for mental disorders with rigid thought patterns.