scholarly journals From Learning to Consciousness: An Example Using Expected Float Entropy Minimisation

Entropy ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 60
Author(s):  
Jonathan Mason

Over recent decades several mathematical theories of consciousness have been put forward including Karl Friston’s Free Energy Principle and Giulio Tononi’s Integrated Information Theory. In this article we further investigate theory based on Expected Float Entropy (EFE) minimisation which has been around since 2012. EFE involves a version of Shannon Entropy parameterised by relationships. It turns out that, for systems with bias due to learning, certain choices for the relationship parameters are isolated since giving much lower EFE values than others and, hence, the system defines relationships. It is proposed that, in the context of all these relationships, a brain state acquires meaning in the form of the relational content of the associated experience. EFE minimisation is itself an association learning process and its effectiveness as such is tested in this article. The theory and results are consistent with the proposition of there being a close connection between association learning processes and the emergence of consciousness. Such a theory may explain how the brain defines the content of consciousness up to relationship isomorphism.

Author(s):  
Takuya Isomura

Mutual information between the brain state and the external world state represents the amount of information stored in the brain that is associated with the external world. On the other hand, surprise of sensory input indicates the unpredictability of the current input. In other words, this is a measure of prediction capability, and an upper bound of surprise is known as free energy. According to the free-energy principle (FEP), the brain continues to minimize free energy to perceive the external world. For animals to survive, prediction capability is considered more important than just memorizing information. In this study, the fact that free energy represents a gap between the amount of information stored in the brain and that available for prediction is established, where the latter will be referred to as predictive information as an analogy with Bialek's predictive information. This concept involves the FEP, the infomax principle, and the predictive information theory, and will be a useful measure to quantify the amount of information available for prediction.


2020 ◽  
Author(s):  
Peter Thestrup Waade ◽  
Christoffer Lundbak Olesen ◽  
Martin Masahito Ito ◽  
Christoph Mathys

The Free Energy Principle (FEP) and Integrated Information Theory (IIT) are two ambitious theoretical frameworks, the first aiming to make a general formal description of self-organization and life-like processes, and the second attempting a mathematical theory of conscious experience based on the intrinsic properties of a system. They are each concerned with complementary aspects of the properties of systems, one with life and behavior the other with meaning and experience, so combining them has potentially great scientific value. In this paper, we take a first step towards this synthesis by first partially replicating the results of the evolutionary simulation study by Albantakis et al. (2014) that show a relationship between IIT-measures and fitness in differing complexities of tasks. We then relate FEP-related information theoretic measures to this result, finding that the surprisal of simulated agents’ system states follows the general increase in fitness over evolutionary time, and that it fluctuates together with IIT-based consciousness measures in within-trial time. This suggests that the consciousness measures of IIT indirectly depend on the relation between the agent and the external world, and that they therefore should be related to concepts directly used in the FEP. Lastly, we suggest a future approach for investigating this relationship empirically.


2020 ◽  
Author(s):  
Adam Safron

In introducing a model of “relaxed beliefs under psychedelics” (REBUS), Carhart-Harris and Friston (2019) have presented a compelling account of the effects of psychedelics on brain and mind. This model is contextualized within the Free Energy Principle (Friston et al., 2006; Friston, 2010), which may represent the first unified paradigm in the mind and life sciences. By this view, mental systems adaptively regulate their actions and interactions with the world via predictive models, whose dynamics are governed by a singular objective of minimizing prediction-error, or “free energy.” According to REBUS, psychedelics flatten the depth of free energy landscapes, or the differential attracting forces associated with various (Bayesian) beliefs, so promoting flexibility in inference and learning. Here, I would like to propose an alternative account of the effects of psychedelics that is in many ways compatible with REBUS, albeit with some important differences. Based on considerations of the distributions of 5-HT2a receptors within cortical laminae and canonical microcircuits for predictive coding, I propose that 5-HT2a agonism may also involve a strengthening of beliefs, particularly at intermediate levels of abstraction associated with conscious experience (Safron, 2020).


2006 ◽  
Vol 100 (1-3) ◽  
pp. 70-87 ◽  
Author(s):  
Karl Friston ◽  
James Kilner ◽  
Lee Harrison

2018 ◽  
Vol 30 (10) ◽  
pp. 2616-2659 ◽  
Author(s):  
Chang Sub Kim

We formulate the computational processes of perception in the framework of the principle of least action by postulating the theoretical action as a time integral of the variational free energy in the neurosciences. The free energy principle is accordingly rephrased, on autopoetic grounds, as follows: all viable organisms attempt to minimize their sensory uncertainty about an unpredictable environment over a temporal horizon. By taking the variation of informational action, we derive neural recognition dynamics (RD), which by construction reduces to the Bayesian filtering of external states from noisy sensory inputs. Consequently, we effectively cast the gradient-descent scheme of minimizing the free energy into Hamiltonian mechanics by addressing only the positions and momenta of the organisms' representations of the causal environment. To demonstrate the utility of our theory, we show how the RD may be implemented in a neuronally based biophysical model at a single-cell level and subsequently in a coarse-grained, hierarchical architecture of the brain. We also present numerical solutions to the RD for a model brain and analyze the perceptual trajectories around attractors in neural state space.


2017 ◽  
Author(s):  
Phillip Alday ◽  
Matthias Schlesewsky ◽  
Ina Bornkessel-Schlesewsky

Sanborn and Chater propose an interesting theory of cognitive and brain function based on Bayesian sampling instead of asymptotic Bayesian inference. Their proposal unifies many current observations and models and, in spite of focusing primarily on cognitive phenomena, their work provides a springboard for unifying several proposed theories of brain function. It has the potential to serve as a bridge between three influential overarching current theories of cognitive and brain function: Bayesian models, Friston's theory of cortical responses based on the free-energy principle, and attractor-basin dynamics. Specifically, their proposal suggests a high-level perspective on Friston's theory, which in turn proposes a sampling procedure including appropriate handling of autocorrelation as well as a plausible neurobiological implementation. In turn, these two theories together link into attractor-basin dynamics at the level of networks (via Friston) as well at the level of behavior (via the relationship between the modes of prior and posterior distributions, as discussed by Sanborn and Chater). We will argue here that, by linking Sanborn and Chater's approach to neurobiological models based on the free-energy principle on the one hand and attractor-basin dynamics on the other, the scope of their proposal can be broadened considerably. Moreover, a unified perspective along these lines provides an elegant solution to several of Sanborn and Chater's Outstanding Questions relating to the neural implementation of sampling.


2021 ◽  
Vol 15 ◽  
Author(s):  
Filippo Cieri ◽  
Xiaowei Zhuang ◽  
Jessica Z. K. Caldwell ◽  
Dietmar Cordes

Neural complexity and brain entropy (BEN) have gained greater interest in recent years. The dynamics of neural signals and their relations with information processing continue to be investigated through different measures in a variety of noteworthy studies. The BEN of spontaneous neural activity decreases during states of reduced consciousness. This evidence has been showed in primary consciousness states, such as psychedelic states, under the name of “the entropic brain hypothesis.” In this manuscript we propose an extension of this hypothesis to physiological and pathological aging. We review this particular facet of the complexity of the brain, mentioning studies that have investigated BEN in primary consciousness states, and extending this view to the field of neuroaging with a focus on resting-state functional Magnetic Resonance Imaging. We first introduce historic and conceptual ideas about entropy and neural complexity, treating the mindbrain as a complex nonlinear dynamic adaptive system, in light of the free energy principle. Then, we review the studies in this field, analyzing the idea that the aim of the neurocognitive system is to maintain a dynamic state of balance between order and chaos, both in terms of dynamics of neural signals and functional connectivity. In our exploration we will review studies both on acute psychedelic states and more chronic psychotic states and traits, such as those in schizophrenia, in order to show the increase of entropy in those states. Then we extend our exploration to physiological and pathological aging, where BEN is reduced. Finally, we propose an interpretation of these results, defining a general trend of BEN in primary states and cognitive aging.


Author(s):  
Daniel Williams

AbstractTwo striking claims are advanced on behalf of the free energy principle (FEP) in cognitive science and philosophy: (i) that it identifies a condition of the possibility of existence for self-organising systems; and (ii) that it has important implications for our understanding of how the brain works, defining a set of process theories—roughly, theories of the structure and functions of neural mechanisms—consistent with the free energy minimising imperative that it derives as a necessary feature of all self-organising systems. I argue that the conjunction of claims (i) and (ii) rests on a fallacy of equivocation. The FEP can be interpreted in two ways: as a claim about how it is possible to redescribe the existence of self-organising systems (the Descriptive FEP), and as a claim about how such systems maintain their existence (the Explanatory FEP). Although the Descriptive FEP plausibly does identify a condition of the possibility of existence for self-organising systems, it has no important implications for our understanding of how the brain works. Although the Explanatory FEP would have such implications if it were true, it does not identify a condition of the possibility of existence for self-organising systems. I consider various ways of responding to this conclusion, and I explore its implications for the role and importance of the FEP in cognitive science and philosophy.


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