Brain Processing and Learning in the Face of Reality’s Complexity:

Between Algorithms, Neural Networks, and Active Prediction

Abstract

How does the human brain process and learn from the complex data of reality? This question spans neuroscience, artificial intelligence, and philosophy of mind. Unlike classical algorithms, which rely on explicit rules for solving specific problems, the brain operates dynamically and holistically: distributed, self-referential, and self-organized. Drawing on evidence from neurobiology, computational neuroscience, and phenomenology, this paper examines the physiological and cognitive mechanisms underlying conscious experience. Topics include sensory transduction, hierarchical cortical processing, synaptic plasticity, neural synchronization, and active prediction. The comparison with artificial neural networks highlights both parallels and critical differences, particularly regarding adaptability, multisensory integration, and subjective experience (qualia). We argue that perception is best understood as construction rather than reflection: an adaptive modeling of reality mediated by neural dynamics. The discussion concludes by raising the unresolved challenge of how subjective experience emerges from these processes—a central enigma for contemporary neuroscience and philosophy.

Keywords: brain processing, neural networks, predictive coding, consciousness, complexity, qualia, philosophy of mind

1. Introduction

The problem of how the brain processes and learns from complex reality constitutes one of the most significant interdisciplinary challenges today. While classical computational approaches describe cognition as the execution of algorithms—explicit, linear rules designed to solve specific problems—the brain demonstrates a different operational logic. It functions dynamically and holistically, through distributed, self-referential, and self-organizing networks that resemble artificial neural networks more than linear programming models[^1].

This paper examines the main mechanisms that allow the brain to transform raw sensory data into conscious experience, and it situates these mechanisms within both scientific and philosophical frameworks.

2. Sensory Input and Encoding

Perception begins with sensory transduction: the conversion of physical stimuli (e.g., electromagnetic waves, mechanical vibrations) into neural signals interpretable by the nervous system[^2]. These signals are transmitted to specialized cortical regions, initiating a cascade of hierarchical processing.

In vision, for instance, the primary visual cortex (V1) detects edges and orientations, while higher-order regions integrate these features into object and face recognition. Data are progressively transformed from low-level to high-level representations.

3. Hierarchical Processing and Cortical Distribution

The human brain does not process data sequentially, but in distributed and hierarchically organized networks. From primary cortices to associative areas, multisensory information converges into integrated representations. This architecture resembles the layered structure of deep neural networks in artificial intelligence[^3].

Yet, unlike artificial systems trained on static datasets, the brain confronts a continuous, noisy, and uncertain flow of stimuli. This forces it into holistic and adaptive strategies that cannot be reduced to fixed mappings between input and output.

4. Synaptic Plasticity and Learning

Learning in the brain is enabled by synaptic plasticity—the ability to modify the strength of connections among neurons in response to experience. Hebb’s classic principle, “neurons that fire together wire together”[^4], explains the consolidation of coactivation patterns.

Plasticity includes mechanisms such as long-term potentiation (LTP) and long-term depression (LTD), which strengthen or weaken synapses over time. These processes allow the brain not only to respond to its environment but also to predict its dynamics and adapt behavior accordingly.

5. Neural Synchronization and Global Coherence

Beyond local synaptic changes, the brain integrates information through the synchronization of neural oscillations. Research by Singer (1999) and Varela (2001) demonstrates that coherence within specific frequency bands (e.g., gamma, theta) enables the unification of dispersed sensory signals into coherent percepts[^5].

This synchronization is widely considered a key mechanism for addressing the “binding problem”: how disparate sensory features such as color, motion, and shape coalesce into unified experiences.

6. Active Prediction and Hierarchical Coding

The brain does not merely record stimuli—it actively generates predictions about the world. According to predictive coding and the free-energy principle (Friston, 2010), perception is the outcome of a constant comparison between internal generative models and incoming sensory signals[^6].

Prediction errors—the discrepancies between expected and actual input—serve as drivers for updating models and guiding learning. Thus, the brain emerges as a proactive system of hypothesis-testing rather than a passive receptor.

7. Artificial Neural Networks and Their Limits

Artificial neural networks, the foundation of deep learning, are inspired by these biological principles. They too can detect regularities, adjust weights, and generate abstract representations from raw input.

However, significant differences remain:

• Biological brains integrate multisensory and contextual information, while artificial networks typically process homogeneous datasets.

• Biological plasticity adapts flexibly in real time.

• Most critically, human nervous systems generate subjective phenomenological qualities (qualia), which remain inaccessible to artificial systems[^7].

8. Discussion

The evidence suggests that human cognition is not best understood as the implementation of rigid algorithms, but as an adaptive, self-organizing process. Through synaptic reconfiguration, oscillatory coherence, and predictive coding, the brain transforms complexity into experience.

From a philosophical standpoint, this has profound implications. Reality is never encountered in a “pure” or objective manner but is always mediated by internal neural models (Merleau-Ponty, 1945). Perception is therefore construction, not reflection—a situated act of meaning-making.

9. Conclusion

The brain is a self-referential, self-organizing system of prediction and learning. It operates across multiple levels: from sensory encoding to cortical hierarchies, from local plasticity to global synchronization, and from perception to active prediction.

Unlike classical algorithms, this system does not merely solve predefined problems but constructs flexible, adaptive representations of reality. Such mechanisms underpin both human survival and creativity.

Yet, one central enigma remains unresolved: how do neural dynamics give rise to the subjective quality of conscious experience? Addressing this question stands as a defining challenge for contemporary neuroscience and philosophy of mind.

References

• Dehaene, S. (2014). Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts. Viking.

• Friston, K. (2010). The free-energy principle: a unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138.

• Hebb, D. O. (1949). The Organization of Behavior: A Neuropsychological Theory. Wiley.

• Kandel, E. R., Schwartz, J. H., & Jessell, T. M. (2013). Principles of Neural Science (5th ed.). McGraw-Hill.

• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

• Merleau-Ponty, M. (1945). Phénoménologie de la perception. Gallimard.

• Nagel, T. (1974). What is it like to be a bat? Philosophical Review, 83(4), 435–450.

• Singer, W. (1999). Neuronal synchrony: A versatile code for the definition of relations? Neuron, 24(1), 49–65.

• Varela, F. J., Lachaux, J. P., Rodriguez, E., & Martinerie, J. (2001). The brainweb: Phase synchronization and large-scale integration. Nature Reviews Neuroscience, 2(4), 229–239.

Notes

[^1]: In philosophy of mind, this distinction is tied to the debate between classical computational models and connectionism. See Churchland (1995).

[^2]: See Principles of Neural Science (Kandel et al., 2013) for detailed discussion of sensory transduction.

[^3]: The analogy between deep learning and cortical hierarchies is reviewed by LeCun, Bengio, & Hinton (2015).

[^4]: Hebb’s principle in The Organization of Behavior (1949) marked the foundation of modern neuropsychology.

[^5]: Singer and Varela advanced the synchronization hypothesis as a correlate of consciousness.

[^6]: Friston’s free-energy principle unifies perception, action, and learning within a Bayesian framework.

[^7]: On qualia, see Nagel’s (1974) classic essay.