Consciousness-Take 2: Animal Consciousness-A Systems Biology Perspective & Its Implications for Artificial Intelligence

#conciousness #neuroscience #neuropsychiatry #systemsbiology #animalscience #brainscience #nature #biology #environment

Animal consciousness has long been a topic of scientific inquiry and debate. I will remind us of the current evidence for animal consciousness. Still, as is my form, it will be informed by a systems biology perspective, citing experimental and observational data in the scientific literature.

Please review my last article on "Consciousness...You Think?". In that piece, I discuss a concept of Consciousness Functional Units (TM), whereby an 'incremental' amount of these building block 'units of consciousness' builds to provide an ever-increasing 'amount' of perceivably-different levels of consciousness (ie. a pet parrot vs my puppies Cookie-&-Minnie vs a dolphin vs my 5-year old grand-daughter vs my 7 yr old grandson vs my 10 yr old grandson vs my 27-yr old 4yr-medical student son).

Recently a great post by Debbie Hampton brought to our attention an article from the Guardian discussing the extraordinary findings pointing to increasing awareness of animal consciousness (particularly in the article in question, to no surprise to most --bees). I had the privilege (although I didn't see it as such at the time) to work as a 'functional' vet assistant for a small farm while in medical school. Pigs, chickens, cows, horses... and bees were the animals I got to pretend to know what I was doing with. Anyone who has observed bees closely will tell you of the marvelous and mesmerizing social interactions and the legendary industriousness of the hive in general but of any of the multitudinous particular individuals that make up the community that is The Hive.

We will explore how understanding and studying animal consciousness can lead to better designs of artificial intelligence and consciousnesses. By recognizing the various levels of consciousness in animals that at least approximate human consciousness, we can gain valuable insights into the nature of consciousness itself and its potential applications in understanding physiopathological mechanisms and possibly the development of artificial intelligences.

Consciousness is a complex and multidimensional phenomenon encompassing subjective experiences of awareness, perception, and thought (Battleday, R.M., et al, 2021). In recent years, increasing interest has been in understanding animal consciousness from a systems biology perspective. Systems biology examines biological systems as a whole, investigating how individual components interact to produce emergent properties. This approach can provide valuable insights into the mechanisms underlying the very thing you are interested in understanding -- in this case, consciousness in animals and humans, with implications for understanding disordered thought and consciousness and possibly contributing to the development of artificial intelligence and consciousnesses.

Evidence for Animal Consciousness

1.1. Neuroanatomical Similarities

Research has revealed striking similarities between human and animal brains regarding their histological structure, with neural networks and subunits conserved across species (Battleday, R.M., et al, 2021). For example, the presence of Cajal cells and Golgi apparatuses in human and animal nervous systems highlights the evolutionary conservation of these structures.

1.2. Behavioral & Cognitive Indicators

Animals display a range of behaviors and cognitive capacities that suggest consciousness. These include problem-solving, decision-making, and social interaction (Bekoff, Allen, & Burghardt, 2002). Moreover, many animals exhibit empathy, self-awareness, and theory of mind, suggesting they possess complex emotional and cognitive experiences (de Waal, 2016). Again, see those pictures of my gorgeous babies (my puppies, not my grandchildren). They know when I'm not myself, or busy or angry (at the babies? Never!). Is this simple, instinctual behavior of them deferring to or regarding the alpha and picking up cues and clues of different behavior, or is this something more?

1.3. Neurophysiological correlates

Neuroimaging studies have shown that consciousness is associated with specific patterns of brain activity in various cortical regions (Marijuan, P.C., et al, 2021). Similar patterns have been observed in animals, suggesting the presence of conscious experiences. For example, studies on non-human primates have demonstrated that they exhibit neural activity consistent with subjective experiences of perception and awareness (Logothetis, 2003).

Understanding Animal Consciousness: Implications for Artificial Intelligence

2.1. Insights into the emergence of consciousness

Studying animal consciousness can provide insights into the emergence of conscious experiences from the coordinated activity of neuronal pathways and subunits in the cerebral cortex (Battleday, R.M., et al, 2021). This understanding can inform the development of computational models that mimic the dynamics of neuronal ensembles in the brain, with the potential to simulate the processing of sensory information and the generation of conscious experiences through artificial neural networks (linking things like LLMs -ChatGPT- with neuronally-structured-computational-units like those designed by Numenta).

2.2. The role of subcellular physical properties

Research into animal consciousness has highlighted the importance of subcellular physical properties, such as ion channels and synaptic connectivity, in the emergence of consciousness (Fields, 2018). Glial cells, for example, have been shown to play a role in modulating synaptic plasticity and regulating neurotransmitter concentrations, potentially contributing to the generation of consciousness (Poskanzer & Yuste, 2016). This knowledge can be applied to developing artificial intelligence that incorporates biologically inspired mechanisms for the emergence of consciousness.

2.3. Information integration and differentiation

Information theory has been used to study consciousness, suggesting that it can be understood as a process of information integration and differentiation (Shannon, 1948). By examining how animal neural strategies differentiate between sensory inputs and generate coherent subjective experiences, we can develop artificial intelligence capable of similar experiences (Tononi, 2004). This approach can inform the design of artificial neural networks that integrate and differentiate information in a manner analogous to conscious biological systems and possibly inform when these systems are found NOT to function this way in correlation to neuropsychiatric pathological states, thereby illuminating a path to the possible understanding of the underlying disorder.

The Potential of Artificial Consciousness

3.1. The Turing Test and limitations of current AI models

The Turing Test has been proposed to evaluate whether an artificial intelligence possesses consciousness (Turing, 1950). However, recent chatbots like Google's LaMDA and OpenAI's ChatGPT have demonstrated that while natural language processing models can convincingly simulate conversation, they may still fall short of true consciousness (see multiple posts by Martin Ciupa ). By understanding the mechanisms underlying animal consciousness, we can refine our models and criteria for evaluating artificial consciousness.

3.2. Designing AI with ethical considerations

Ethical considerations become increasingly relevant as we develop artificial intelligence with consciousness-like properties. By studying animal consciousness and recognizing its similarities to human consciousness, we can better appreciate the ethical implications of creating conscious artificial intelligence and ensure its development is guided by ethical principles (Bostrom, 2014).

3.3. Towards a deeper understanding of consciousness

The study of animal consciousness from a systems biology perspective can contribute to a deeper understanding of consciousness as a whole. By examining the intricacies of animal consciousness, we can better comprehend the fundamental principles underlying conscious experiences, potentially leading to groundbreaking advances in artificial intelligence and our understanding of consciousness itself (Koch, Massimini, Boly, & Tononi, 2016).

The evidence for animal consciousness from a systems biology perspective is compelling, to say the least. By examining neuroanatomical similarities, behavioral and cognitive indicators, and neurophysiological correlates, we can appreciate the complexity of conscious experiences in animals. Understanding animal consciousness has ethical implications and provides valuable insights for the development of artificial intelligence and consciousnesses. As we continue to study animal consciousness and its underlying mechanisms, we can refine our pathophysiology and artificial intelligence models while deepening our understanding of consciousness as a whole.

Desiderio Pina, MD, MPH

TheMindAndBodyDoc-Physician/Neuroscientist —?@mindandbodydoc

I provide compassionate care for children (5 years & older), adolescents, adults & families struggling with nutritional, drug, & neuropsychiatric problems.

Teaching is always a privilege, and I’ve been afforded such privilege to teach at various medical schools (MD & DO), residency programs (Psychiatry, Neurology, Family Practice, and Internal Medicine), and universities; I have participated in clinical and basic science research in the past, and am currently on staff at a few hospitals, but primarily care for patients via telemedicine.

I generally talk & write about things that catch my fancy in the news and from the recent medical literature.?

These include, but are not limited to:?#wellness,?#neurosciences,?#neuropsychiatry,?#culturalpsychiatry,?#ethnobotony,?#mycology,?#mycologicalmedicine,?#digitalhealthcare,?#healthcaremanagement, and?#psychoneuroendocrineimmunology

References:

Battleday, R.M., Peterson, J.C. and Griffiths, T.L. (2021), From convolutional neural networks to models of higher-level cognition (and back again). Ann. N.Y. Acad. Sci., 1505: 55-78.?https://doi.org/10.1111/nyas.14593

Bekoff, M., Allen, C., & Burghardt, G. M. (Eds.). (2002). The cognitive animal: Empirical and theoretical perspectives on animal cognition. MIT press.

Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press.

de Waal, F. B. (2016). Are we smart enough to know how smart animals are? WW Norton & Company.

Fields, R. D. (2018). Glial cell regulation of neuronal activity and behavior. Neuroscience Letters, 689, 11-15.

Koch, C., Massimini, M., Boly, M., & Tononi, G. (2016). Neural correlates of consciousness: progress and problems. Nature Reviews Neuroscience, 17(5), 307-321.

Logothetis, N. K. (2003). The underpinnings of the BOLD functional magnetic resonance imaging signal. The Journal of Neuroscience, 23(10), 3963-3971.

Marijuan, P.C. (2001). CAJAL AND CONSCIOUSNESS: Scientific Approaches to Consciousness on the Centennial of Ramon y Cajal's Textura Volume 929,??Issue 1 Pages:?1-257, April 2001, Edited by Pedro C. Marijuán (Universidad de Zaragoza, Spain).

Poskanzer, K. E., & Yuste, R. (2016). Astrocytes regulate cortical state switching in vivo. Proceedings of the National Academy of Sciences, 113(19), E2675-E2684.

Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379-423.

Tononi, G. (2004). An information integration theory of consciousness. BMC Neuroscience, 5 (1), 42. doi:10.1186/1471-2202-5-42

Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433-460.

Uttal, W. R. (2001). The new phrenology: The limits of localizing cognitive processes in the brain. MIT press.

van Swinderen, B. (2005). The remote roots of consciousness in fruit-fly selective attention? BioEssays, 27(3), 321-330.

Watanabe, S., & Yamamoto, M. (2015). Neural mechanisms of social dominance. Frontiers in Neuroscience, 9, 154. doi:10.3389/fnins.2015.00154

Zylberberg, A., & Strowbridge, B. W. (2017). Mechanisms of persistent activity in cortical circuits: possible neural substrates for working memory. Annual Review of Neuroscience, 40, 603-627.