Call for papers

 Scope of the special session

This special session aims to provide a venue for the ALife and AI communities to explore how self-organisation can inspire and enrich neural paradigms of intelligence. Submissions to the special session follow the same review process as the main-track, and will also be included in the conference proceedings.

The neural computing paradigm—in its most popular deep learning form—consists of learning networks made of simplified neuron-like units. Despite its simplicity, it has proven to be highly successful at solving complex tasks, and has started to exhibit early signs of emergent intelligence (Wei et al, 2021). On the other hand, self-organisation is essential to how biological neural systems come to be (Hiesinger, 2021), and has been one of the cornerstones of ALife since Ross Ashby work on self-organisation as the basis for adaptive behaviour in neural systems (Ashby, 1952), and von Neumann’s work on universal constructors. Recently, self-organisation is emerging as a promising paradigm in the AI field (Mordvintsev et al., 2020; Ha and Tang, 2021; Risi, 2021, Variengien et al., 2021), bringing ideas from complex systems and ALife onto the neural computing paradigm.

Some questions this special session seeks to address (but not limited to):

• What benefits does self-organisation bring to the neural computing paradigm, and how does it help overcome its limitations?

• How do dynamics, computation, and representation work together to produce neural intelligence? Are some neural substrates better suited for self-organisation? E.g. spike vs rate, synchronous vs. asynchronous activity, critical vs non-critical regimes.

• Can learning be understood as a self-organising process? What role does self-organisation play in lifelong learning and adaptability?

• How can deep reinforcement learning be unified with self-organisation, where rewards emerge spontaneously across multiple levels? How do self-organising systems solve the credit assignment problem? Can self-organisation drive the development of autonomous goal-setting in neural systems?

• How does evolution interact with self-organisation in shaping neural intelligence? Can evolutionary principles facilitate the emergence of self-organising neural structures? 

• Can bio-inspired self-organising principles enhance robustness and fault tolerance in neural systems? 

• What are the challenges and trade-offs of integrating self-organisation into large-scale neural architectures?

• Beyond GPU: Is custom hardware (neuromorphic, FPGAs, etc.) necessary to demonstrate the potential of self-organising neural models?

Submission instructions

Full Papers

Full Papers are up to 8 pages long (not including references) and should report on new, unpublished work. Full Papers will be reviewed as self-contained work, with the same review criteria as a journal paper. Accepted papers will be published by MIT press as open access conference proceedings. Accepted submissions will be assigned an oral or poster presentation.

Summaries

Summaries summarize a previously published work. These are limited to a maximum of 2 pages (not including references). Summaries must report on work that has been peer reviewed and published already, e.g. in another conference or a journal. If the work has only been published as a preprint it should be submitted as a full paper. Summaries will not be included in the proceedings. They will be made publicly available but will not receive DOIs. Summaries will be reviewed for relevance to the conference. Accepted submissions will be assigned an oral or poster presentation, but full papers will be given priority for oral presentations.

To submit, follow the instructions here and select SONI Special Session.