A thalamocortical substrate for integrated information via critical synchronous bursting.

Understanding the neurobiological mechanisms underlying consciousness remains a significant challenge. Recent evidence suggests that the coupling between distal-apical and basal-somatic dendrites in thick-tufted layer 5 pyramidal neurons (L5PN), regulated by the nonspecific-projecting thalamus, is crucial for consciousness. Yet, it is uncertain whether this thalamocortical mechanism can support emergent signatures of consciousness, such as integrated information. To address this question, we constructed a biophysical network of dual-compartment thick-tufted L5PN, with dendrosomatic coupling controlled by thalamic inputs. Our findings demonstrate that integrated information is maximized when nonspecific thalamic inputs drive the system into a regime of time-varying synchronous bursting. Here, the system exhibits variable spiking dynamics with broad pairwise correlations, supporting the enhanced integrated information. Further, the observed peak in integrated information aligns with criticality signatures and empirically observed layer 5 pyramidal bursting rates. These results suggest that the thalamocortical core of the mammalian brain may be evolutionarily configured to optimize effective information processing, providing a potential neuronal mechanism that integrates microscale theories with macroscale signatures of consciousness.

Local Adaptive Image Filtering Based on Recursive Dilation Segmentation.

This paper introduces a simple but effective image filtering method, namely, local adaptive image filtering (LAIF), based on an image segmentation method, i.e., recursive dilation segmentation (RDS). The algorithm is motivated by the observation that for the pixel to be smoothed, only the similar pixels nearby are utilized to obtain the filtering result. Relying on this observation, similar pixels are partitioned by RDS before applying a locally adaptive filter to smooth the image. More specifically, by directly taking the spatial information between adjacent pixels into consideration in a recursive dilation way, RDS is firstly proposed to partition the guided image into several regions, so that the pixels belonging to the same segmentation region share a similar property. Then, guided by the iterative segmented results, the input image can be easily filtered via a local adaptive filtering technique, which smooths each pixel by selectively averaging its local similar pixels. It is worth mentioning that RDS makes full use of multiple integrated information including pixel intensity, hue information, and especially spatial adjacent information, leading to more robust filtering results. In addition, the application of LAIF in the remote sensing field has achieved outstanding results, specifically in areas such as image dehazing, denoising, enhancement, and edge preservation, among others. Experimental results show that the proposed LAIF can be successfully applied to various filtering-based tasks with favorable performance against state-of-the-art methods.

System Integrated Information.

Integrated information theory (IIT) starts from consciousness itself and identifies a set of properties (axioms) that are true of every conceivable experience. The axioms are translated into a set of postulates about the substrate of consciousness (called a complex), which are then used to formulate a mathematical framework for assessing both the quality and quantity of experience. The explanatory identity proposed by IIT is that an experience is identical to the cause-effect structure unfolded from a maximally irreducible substrate (a Φ-structure). In this work we introduce a definition for the integrated information of a system (φs) that is based on the existence, intrinsicality, information, and integration postulates of IIT. We explore how notions of determinism, degeneracy, and fault lines in the connectivity impact system-integrated information. We then demonstrate how the proposed measure identifies complexes as systems, the φs of which is greater than the φs of any overlapping candidate systems.

The Fundamental Tension in Integrated Information Theory 4.0's Realist Idealism.

Integrated Information Theory (IIT) is currently one of the most influential scientific theories of consciousness. Here, we focus specifically on a metaphysical aspect of the theory's most recent version (IIT 4.0), what we may call its idealistic ontology, and its tension with a kind of realism about the external world that IIT also endorses. IIT 4.0 openly rejects the mainstream view that consciousness is generated by the brain, positing instead that consciousness is ontologically primary while the physical domain is just "operational". However, this philosophical position is presently underdeveloped and is not rigorously formulated in IIT, potentially leading to many misinterpretations and undermining its overall explanatory power. In the present paper we aim to address this issue. We argue that IIT's idealistic ontology should be understood as a specific combination of phenomenal primitivism, reductionism regarding Φ-structures and complexes, and eliminativism about non-conscious physical entities. Having clarified this, we then focus on the problematic tension between IIT's idealistic ontology and its simultaneous endorsement of realism, according to which there is some kind of external reality independent of our minds. After refuting three potential solutions to this theoretical tension, we propose the most plausible alternative: understanding IIT's realism as an assertion of the existence of other experiences beyond one's own, what we call a non-solipsistic idealist realism. We end with concluding remarks and future research avenues.

Comment on Albantakis et al. Computing the Integrated Information of a Quantum Mechanism. Entropy 2023, 25, 449.

Integrated information theory (IIT) is a powerful tool that provides a framework for evaluating consciousness, whether in the human brain or in other systems. In Computing the Integrated Information of a Quantum Mechanism, the authors extend IIT from digital gates to a quantum CNOT logic gate, and while they explicitly distinguish the analysis from quantum theories of consciousness, they nonetheless provide an analytical road map for extending IIT not only to other quantum mechanisms but also to hybrid computing structures like the brain. This comment provides additional information relating to an adiabatic quantum mechanical energy routing mechanism that is part of a hybrid biological computer that provides an action selection mechanism, which has been hypothesized to exist in the human brain and for which predicted evidence has been subsequently observed, and it hopes to motivate the further evaluation and extension of IIT not only to that hypothesized mechanism but also to other hybrid biological computers.

Metastability, fractal scaling, and synergistic information processing: What phase relationships reveal about intrinsic brain activity.

Dynamic functional connectivity (dFC) in resting-state fMRI holds promise to deliver candidate biomarkers for clinical applications. However, the reliability and interpretability of dFC metrics remain contested. Despite a myriad of methodologies and resulting measures, few studies have combined metrics derived from different conceptualizations of brain functioning within the same analysis - perhaps missing an opportunity for improved interpretability. Using a complexity-science approach, we assessed the reliability and interrelationships of a battery of phase-based dFC metrics including tools originating from dynamical systems, stochastic processes, and information dynamics approaches. Our analysis revealed novel relationships between these metrics, which allowed us to build a predictive model for integrated information using metrics from dynamical systems and information theory. Furthermore, global metastability - a metric reflecting simultaneous tendencies for coupling and decoupling - was found to be the most representative and stable metric in brain parcellations that included cerebellar regions. Additionally, spatiotemporal patterns of phase-locking were found to change in a slow, non-random, continuous manner over time. Taken together, our findings show that the majority of characteristics of resting-state fMRI dynamics reflect an interrelated dynamical and informational complexity profile, which is unique to each acquisition. This finding challenges the interpretation of results from cross-sectional designs for brain neuromarker discovery, suggesting that individual life-trajectories may be more informative than sample means.

A framework for preparing a stochastic nonlinear integrate-and-fire model for integrated information theory.

This paper presents a framework for spiking neural networks to be prepared for the Integrated Information Theory (IIT) analysis, using a stochastic nonlinear integrate-and-fire model. The model includes the crucial dynamics of the all-or-none law and after-spike refractoriness. The noise is modelled as an additive term in the system's equations. By preparing the model for the IIT analysis, it is meant to determine the length of the analysis time-window and the transition probability distributions required for the IIT 3.0. To this end, a system of differential equations is proposed to estimate the time evolution of the system's mean and covariance. Assuming the binary Fired/Silent activity as the possible states of each neuron, an algorithm is proposed to calculate the required probability distributions. As long as the Fired/Silent probabilities are only concerned, the Gaussian density assumption with the estimated moments is a reasonable estimate. The synaptic inputs are treated as random variables with low variances to avoid the costs of conditioning on the system's past activities. The Monte-Carlo simulation is used to validate the estimation methods. To increase the reliability of the inductive inference behind the Monte-Carlo method, various stimulation protocols are applied to evoke the dynamics of the equations.

Consciousness and complexity: Neurobiological naturalism and integrated information theory.

In this paper we take a meta-theoretical stance and compare and assess two conceptual frameworks that endeavor to explain phenomenal experience. In particular, we compare Feinberg & Mallatt's Neurobiological Naturalism (NN) and Tononi's and colleagues Integrated Information Theory (IIT), given that the former pointed out some similarities between the two theories (Feinberg & Mallatt 2016c-d). To probe their similarity, we first give a general introduction into both frameworks. Next, we expound a ground-plan for carrying out our analysis. We move on to articulate a philosophical profile of NN and IIT, addressing their ontological commitments and epistemological foundations. Finally, we compare the two point-by-point, also discussing how they stand on the issue of artificial consciousness.

Filled/non-filled pairs: An empirical challenge to the integrated information theory of consciousness.

Perceptual filling-in for vision is the insertion of visual properties (e.g., color, contour, luminance, or motion) into one's visual field, when those properties have no corresponding retinal input. This paper introduces and provides preliminary empirical support for filled/non-filled pairs, pairs of images that appear identical, yet differ by amount of filling-in. It is argued that such image pairs are important to the experimental testing of theories of consciousness. We review recent experimental research and conclude that filling-in involves brain activity with relatively high integrated information (Φ) compared to veridical visual perceptions. We then present filled/non-filled pairs as an empirical challenge to the integrated information theory of consciousness, which predicts that phenomenologically identical experiences depend on brain processes with identical Φ.

Integrated Information Coefficient Estimated from Neuronal Activity in Hippocampus-Amygdala Complex of Rats as a Measure of Learning Success.

BACKGROUND: The goal of the brain is to provide right on time a suitable earlier-acquired model for the future behavior. How a complex structure of neuronal activity underlying a suitable model is selected or fixated is not well understood. Here we propose the integrated information Φ as a possible metric for such complexity of neuronal groups. It quantifies the degree of information integration between different parts of the brain and is lowered when there is a lack of connectivity between different subsets in a system. METHODS: We calculated integrated information coefficient (Φ) for activity of hippocampal and amygdala neurons in rats during acquisition of two tasks: spatial task followed by spatial aversive task. An Autoregressive Φ algorithm was used for time-series spike data. RESULTS: We showed that integrated information coefficient Φ is positively correlated with a metric of learning success (a relative number of rewards). Φ for hippocampal neurons was positively correlated with Φ for amygdalar neurons during the learning requiring the cooperative work of hippocampus and amygdala. CONCLUSIONS: This result suggests that integrated information coefficient Φ may be used as a prediction tool for the suitable level of complexity of neuronal activity and the future success in learning and adaptation and a tool for estimation of interactions between different brain regions during learning.

Non-Separability of Physical Systems as a Foundation of Consciousness.

A hypothesis is presented that non-separability of degrees of freedom is the fundamental property underlying consciousness in physical systems. The amount of consciousness in a system is determined by the extent of non-separability and the number of degrees of freedom involved. Non-interacting and feedforward systems have zero consciousness, whereas most systems of interacting particles appear to have low non-separability and consciousness. By contrast, brain circuits exhibit high complexity and weak but tightly coordinated interactions, which appear to support high non-separability and therefore high amount of consciousness. The hypothesis applies to both classical and quantum cases, and we highlight the formalism employing the Wigner function (which in the classical limit becomes the Liouville density function) as a potentially fruitful framework for characterizing non-separability and, thus, the amount of consciousness in a system. The hypothesis appears to be consistent with both the Integrated Information Theory and the Orchestrated Objective Reduction Theory and may help reconcile the two. It offers a natural explanation for the physical properties underlying the amount of consciousness and points to methods of estimating the amount of non-separability as promising ways of characterizing the amount of consciousness.

Emergence of Integrated Information at Macro Timescales in Real Neural Recordings.

How a system generates conscious experience remains an elusive question. One approach towards answering this is to consider the information available in the system from the perspective of the system itself. Integrated information theory (IIT) proposes a measure to capture this integrated information (Φ). While Φ can be computed at any spatiotemporal scale, IIT posits that it be applied at the scale at which the measure is maximised. Importantly, Φ in conscious systems should emerge to be maximal not at the smallest spatiotemporal scale, but at some macro scale where system elements or timesteps are grouped into larger elements or timesteps. Emergence in this sense has been demonstrated in simple example systems composed of logic gates, but it remains unclear whether it occurs in real neural recordings which are generally continuous and noisy. Here we first utilise a computational model to confirm that Φ becomes maximal at the temporal scales underlying its generative mechanisms. Second, we search for emergence in local field potentials from the fly brain recorded during wakefulness and anaesthesia, finding that normalised Φ (wake/anaesthesia), but not raw Φ values, peaks at 5 ms. Lastly, we extend our model to investigate why raw Φ values themselves did not peak. This work extends the application of Φ to simple artificial systems consisting of logic gates towards searching for emergence of a macro spatiotemporal scale in real neural systems.

From Shorter to Longer Timescales: Converging Integrated Information Theory (IIT) with the Temporo-Spatial Theory of Consciousness (TTC).

Time is a key element of consciousness as it includes multiple timescales from shorter to longer ones. This is reflected in our experience of various short-term phenomenal contents at discrete points in time as part of an ongoing, more continuous, and long-term 'stream of consciousness'. Can Integrated Information Theory (IIT) account for this multitude of timescales of consciousness? According to the theory, the relevant spatiotemporal scale for consciousness is the one in which the system reaches the maximum cause-effect power; IIT currently predicts that experience occurs on the order of short timescales, namely, between 100 and 300 ms (theta and alpha frequency range). This can well account for the integration of single inputs into a particular phenomenal content. However, such short timescales leave open the temporal relation of specific phenomenal contents to others during the course of the ongoing time, that is, the stream of consciousness. For that purpose, we converge the IIT with the Temporo-spatial Theory of Consciousness (TTC), which, assuming a multitude of different timescales, can take into view the temporal integration of specific phenomenal contents with other phenomenal contents over time. On the neuronal side, this is detailed by considering those neuronal mechanisms driving the non-additive interaction of pre-stimulus activity with the input resulting in stimulus-related activity. Due to their non-additive interaction, the single input is not only integrated with others in the short-term timescales of 100-300 ms (alpha and theta frequencies) (as predicted by IIT) but, at the same time, also virtually expanded in its temporal (and spatial) features; this is related to the longer timescales (delta and slower frequencies) that are carried over from pre-stimulus to stimulus-related activity. Such a non-additive pre-stimulus-input interaction amounts to temporo-spatial expansion as a key mechanism of TTC for the constitution of phenomenal contents including their embedding or nesting within the ongoing temporal dynamic, i.e., the stream of consciousness. In conclusion, we propose converging the short-term integration of inputs postulated in IIT (100-300 ms as in the alpha and theta frequency range) with the longer timescales (in delta and slower frequencies) of temporo-spatial expansion in TTC.

NLM and the IAIMS initiative: Cross-institutional academic/advanced systems contributing to the evolution of networked information and resources.

The Integrated Academic/Advanced Information Systems (IAIMS) program began in 1983 and was based on a study by the Association of American Medical Colleges (AAMC). Donald A.B. Lindberg M.D. was a member of the AAMC Advisory Committee. The U.S. National Library of Medicine (NLM) grants for IAIMS were initiated in 1984 the same year Dr. Lindberg became Director of the NLM. This chapter presents an overview of IAIMS and its progression through three stages with Dr. Lindberg's leadership.

Integrated information theory does not make plant consciousness more convincing.

It has been proposed by some plant scientists that plants are cognitive and conscious organisms, although this is a minority view. Here we present a brief summary of some of the arguments against this view, followed by a critique of an article in this same issue of Biochemical and Biophysical Research Communications by Calvo, Baluska, and Trewavas (2020) that cites Integrated Information Theory (IIT) as providing additional support for plant consciousness. The authors base their argument on the assumptions that all cells are conscious and that consciousness is confined to life. However, IIT allows for consciousness in various nonliving systems, and thus does not restrict consciousness to living organisms. Therefore, IIT cannot be used to prove plant consciousness, for which there is neither empirical evidence nor support from other, neuron-based, theories of consciousness.

Integrated information as a possible basis for plant consciousness.

It is commonly assumed that plants do not possess consciousness. Since the criterion for this assumption is usually human consciousness this assumption represents a top down attitude. It is obvious that plants are not animals and using animal criteria of consciousness will lead to its rejection in plants. However using a bottom up evolutionary approach and a leading theory of consciousness, Integrated Information Theory, we report that we find evidence that indicates that plant meristems act in a conscious fashion although probably at the level of minimal consciousness. Since many plants contain multiple meristems these observations highlight a very different evolutionary approach to consciousness in biological organisms.

IIT's Scientific Counter-Revolution: A Neuroscientific Theory's Physical and Metaphysical Implications.

IIT includes commitments about the very nature of physical reality, a fact both highly unusual for an empirical theory within neuroscience, and surprisingly underappreciated within the literature. These commitments are intimately tied to the theory; they are not incidental. This paper demonstrates as much by raising certain objections in a "naive" way, and then exposing how the principled IIT responses would rely upon metaphysical positions. Along the way we draw on the IIT literature for support for these interpretations, but also point to a need for elaboration and clarification. Section 1 applies the Placement Argument in a way that leads to problem involving zombies, treated in Section 2. Section 3 frames the zombie problem as an apparent dilemma, and addresses that dilemma by drawing on claims in the IIT literature concerning physical reality. Section 4 raises a related dilemma and treats it in a way that dovetails with the treatment in Section 3 of physical reality. All of this underscores not just the breadth of IIT, but the relevance of this breadth to a full consideration of IIT's merits.

Quantifying the Autonomy of Structurally Diverse Automata: A Comparison of Candidate Measures.

Should the internal structure of a system matter when it comes to autonomy? While there is still no consensus on a rigorous, quantifiable definition of autonomy, multiple candidate measures and related quantities have been proposed across various disciplines, including graph-theory, information-theory, and complex system science. Here, I review and compare a range of measures related to autonomy and intelligent behavior. To that end, I analyzed the structural, information-theoretical, causal, and dynamical properties of simple artificial agents evolved to solve a spatial navigation task, with or without a need for associative memory. By contrast to standard artificial neural networks with fixed architectures and node functions, here, independent evolution simulations produced successful agents with diverse neural architectures and functions. This makes it possible to distinguish quantities that characterize task demands and input-output behavior, from those that capture intrinsic differences between substrates, which may help to determine more stringent requisites for autonomous behavior and the means to measure it.

On the Operational Utility of Measures of Multichannel EEGs.

Multichannel EEGs were obtained from healthy participants in the eyes-closed no-task condition and in the eyes-open condition (where the alpha component is typically abolished). EEG dynamics in the two conditions were quantified with two related binary Lempel-Ziv measures of the first principal component, and with three measures of integrated information, including the more recently proposed integrated synergy. Both integrated information and integrated synergy with model order p=1 had greater values in the eyes-closed condition. When the model order of integrated synergy was determined with the Bayesian Information Criterion, this pattern was reversed, and in line with the other measures, integrated synergy was greater in the eyes-open condition. Eyes-open versus eyes-closed separation was quantified by calculating the between-condition effect size. The Lempel-Ziv complexity of the first principal component showed greater separation than the measures of integrated information.

What Is Consciousness? Integrated Information vs. Inference.

Any successful naturalistic account of consciousness must state what consciousness is, in terms that are compatible with the rest of our naturalistic descriptions of the world. Integrated Information Theory represents a pioneering attempt to do just this. This theory accounts for the core features of consciousness by holding that there is an equivalence between the phenomenal experience associated with a system and its intrinsic causal power. The proposal, however, fails to provide insight into the qualitative character of consciousness and, as a result of its proposed equivalence between consciousness and purely internal dynamics, into the intentional character of conscious perception. In recent years, an alternate group of theories has been proposed that claims consciousness to be equivalent to certain forms of inference. One such theory is the Living Mirror theory, which holds consciousness to be a form of inference performed by all living systems. The proposal of consciousness as inference overcomes the shortcomings of Integrated Information Theory, particularly in the case of conscious perception. A synthesis of these two perspectives can be reached by appreciating that conscious living systems are self-organising in nature. This mode of organization requires them to have a high level of integration. From this perspective, we can understand consciousness as being dependent on a system possessing non-trivial amounts of integrated information while holding that the process of inference performed by the system is the fact of consciousness itself.