Gabriele Scheler, Johann M. Schumann

We present a model of pattern memory and retrieval with novel, technically useful and biologically realistic properties. Specifically, we enter n variations of k pattern classes (n*k patterns) onto a cortex-like balanced inhibitory-excitatory network with heterogeneous neurons, and let the pattern spread within the recurrent network. We show that we can identify high mutual-information (MI) neurons as major information-bearing elements within each pattern representation. We employ a simple one-shot adaptive (learning) process focusing on high MI neurons and inhibition. Such ‘localist plasticity’ has high efficiency, because it requires only few adaptations for each pattern. Specifically, we store k=10 patterns of size s=400 in a 1000/1200 neuron network. We stimulate high MI neurons and in this way recall patterns, such that the whole network represents this pattern. We assess the quality of the representation (a) before learning, when entering the pattern into a naive network and (b) after learning, on the adapted network, during recall. The recalled patterns could be easily recognized by a trained classifier. The pattern ‘unfolds’ over the recurrent network with high similarity, albeit compressed, with respect to the original input pattern. We discuss the distribution of neuron properties in the network, and find that an initial Gaussian or uniform distribution changes into a more heavy-tailed, lognormal distribution during the adaptation process. The remarkable result is that we are able to achieve reliable pattern recall by stimulating only high information neurons. This work has interesting technical applications, and provides a biologically-inspired model of cortical memory.

Read the full article at: www.biorxiv.org

Olaf Witkowski, Eric Schwitzgebel

Artificial Life (2024) 30 (2): 193–215.

The field of Artificial Life studies the nature of the living state by modeling and synthesizing living systems. Such systems, under certain conditions, may come to deserve moral consideration similar to that given to nonhuman vertebrates or even human beings. The fact that these systems are nonhuman and evolve in a potentially radically different substrate should not be seen as an insurmountable obstacle to their potentially having rights, if they are sufficiently sophisticated in other respects. Nor should the fact that they owe their existence to us be seen as reducing their status as targets of moral concern. On the contrary, creators of Artificial Life may have special obligations to their creations, resembling those of an owner to their pet or a parent to their child. For a field that aims to create artificial life-forms with increasing levels of sophistication, it is crucial to consider the possible ethical implications of our activities, with an eye toward assessing potential moral obligations for which we should be prepared. If Artificial Life is larger than life, then the ethics of artificial beings should be larger than human ethics.

Read the full article at: direct.mit.edu

Joanna K. Garner, Karen R. Harris

INTERNATIONAL JOURNAL OF COMPLEXITY IN EDUCATION Vol 5, No 1 (2024)

A primary goal of educational research is to improve understanding of the systems in
which students learn and educators teach. However, for much of the 20th and early 21st
century, researchers have relied upon models of individual, school, and district level change
that characterize educational processes and outcomes using linear, input-process-output
frameworks (Opfer & Pedder, 2011). These approaches conceal the complexity of
educational systems by using research designs and data collection methods that simplify
and decontextualize phenomena and the relations among them, and do not consider
influences across levels of the system (Kaplan & Garner, 2020). They have also not yielded
guidance for researchers and practitioners who work in contexts where multiple programs
and interventions overlap and interact with one another. In addition, our field faces
epistemological divisions that reflect varying emphases on context and the foregrounding
of different units of analysis. Some scholars advocate for large-scale, randomized control
trials as a “gold standard” for evaluating the implementation and outcome of interventions
(Lortie-Forgues & Inglis, 2019; Maxwell, 2004), while others value design-based, iterative
approaches aimed at addressing progressions of dilemmas of practice (Sandoval & Bell,
2004). Perhaps most importantly, many current approaches to research overlook the ways
in which teaching and learning are inherently interconnected with other sociocultural,
political, and historical phenomena such as economic development, mass migration, and
technological advances that manifest in individuals, families, and communities, and that
support or disrupt education (Harris, 2018).

Read the full article at: ejournals.lib.auth.gr

We demonstrate that the assembly pathway method underlying assembly theory (AT) is an encoding scheme widely used by popular statistical compression algorithms. We show that in all cases (synthetic or natural) AT performs similarly to other simple coding schemes and underperforms compared to system-related indexes based upon algorithmic probability that take into account statistical repetitions but also the likelihood of other computable patterns. Our results imply that the assembly index does not offer substantial improvements over existing methods, including traditional statistical ones, and imply that the separation between living and non-living compounds following these methods has been reported before.

Abicumaran Uthamacumaran, Felipe S. Abrahão, Narsis A. Kiani & Hector Zenil
npj Systems Biology and Applications volume 10, Article number: 82 (2024)

Read the full article at: www.nature.com

Andy Clark

Entropy 2024, 26(8), 677

According to active inference, constantly running prediction engines in our brain play a large role in delivering all human experience. These predictions help deliver everything we see, hear, touch, and feel. In this paper, I pursue one apparent consequence of this increasingly well-supported view. Given the constant influence of hidden predictions on human experience, can we leverage the power of prediction in the service of human flourishing? Can we learn to hack our own predictive regimes in ways that better serve our needs and purposes? Asking this question rapidly reveals a landscape that is at once familiar and new. It is also challenging, suggesting important questions about scope and dangers while casting further doubt (as if any was needed) on old assumptions about a firm mind/body divide. I review a range of possible hacks, starting with the careful use of placebos, moving on to look at chronic pain and functional disorders, and ending with some speculations concerning the complex role of genetic influences on the predictive brain.

Read the full article at: www.mdpi.com