Consciousness and Information Theory

2023-02-15T16:34:20+00:00

This week on Journal Club session Shabnam Kadir will talk about Consciousness and Information Theory based on selection of 3 papers. For more information, please see the list of papers and an abstract below.

This paper presents Integrated Information Theory (IIT) of consciousness 3.0, which incorporates several advances over previous formulations. IIT starts from phenomenological axioms: information says that each experience is specific - it is what it is by how it differs from alternative experiences; integration says that it is unified - irreducible to non-interdependent components; exclusion says that it has unique borders and a particular spatio-temporal grain. These axioms are formalized into postulates that prescribe how physical mechanisms, such as neurons or logic gates, must be configured to generate experience (phenomenology). The postulates are used to define intrinsic information as "differences that make a difference" within a system, and integrated information as information specified by a whole that cannot be reduced to that specified by its parts. By applying the postulates both at the level of individual mechanisms and at the level of systems of mechanisms, IIT arrives at an identity: an experience is a maximally irreducible conceptual structure (MICS, a constellation of concepts in qualia space), and the set of elements that generates it constitutes a complex. According to IIT, a MICS specifies the quality of an experience and integrated information Φ_Max its quantity. From the theory follow several results, including: a system of mechanisms may condense into a major complex and non-overlapping minor complexes; the concepts that specify the quality of an experience are always about the complex itself and relate only indirectly to the external environment; anatomical connectivity influences complexes and associated MICS; a complex can generate a MICS even if its elements are inactive; simple systems can be minimally conscious; complicated systems can be unconscious; there can be true "zombies" - unconscious feed-forward systems that are functionally equivalent to conscious complexes.

Papers:

L. Albantakis, L. Barbosa, G. Findlay, M. Grasso, A. Haun, W. Marshall, W. Mayner, A. Zaeemzadeh, M. Boly, B. Juel, S. Sasai, K. Fujii, I. David, J. Hendren, J. Lang, G. Tononi, "Integrated Information Theory (IIT) 4.0: Formulating the Properties of Phenomenal Existence in Physical Terms", 2022, arXiv,
J. Jost, "Information Theory and Consciousness", 2021, Frontiers in Applied Mathematics and Statistics, 7,
M. Oizumi, L. Albantakis, G. Tononi, "From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0", 2014, PLOS Computational Biology, 10, e1003588

Date: 2023/02/17
Time: 14:00
Location: online

Learning Compositional Sequences with Multiple Time Scales through a Hierarchical Network of Spiking Neurons

2021-04-28T16:53:00+01:00

This week on Journal Club session Muhammad Yaqoob will talk about a paper "Learning Compositional Sequences with Multiple Time Scales through a Hierarchical Network of Spiking Neurons".

Sequential behaviour is often compositional and organised across multiple time scales: a set of individual elements developing on short time scales (motifs) are combined to form longer functional sequences (syntax). Such organisation leads to a natural hierarchy that can be used advantageously for learning, since the motifs and the syntax can be acquired independently. Despite mounting experimental evidence for hierarchical structures in neuroscience, models for temporal learning based on neuronal networks have mostly focused on serial methods. Here, we introduce a network model of spiking neurons with a hierarchical organisation aimed at sequence learning on multiple time scales. Using biophysically motivated neuron dynamics and local plasticity rules, the model can learn motifs and syntax independently. Furthermore, the model can relearn sequences efficiently and store multiple sequences. Compared to serial learning, the hierarchical model displays faster learning, more flexible relearning, increased capacity, and higher robustness to perturbations. The hierarchical model redistributes the variability: it achieves high motif fidelity at the cost of higher variability in the between-motif timings.

Papers:

A. Maes, M. Barahona, C. Clopath, "Learning Compositional Sequences with Multiple Time Scales through a Hierarchical Network of Spiking Neurons", 2021, PLOS Computational Biology, 17, e1008866

Date: 2021/04/30
Time: 14:00
Location: online

UH Biocomputation Group - Synapses

Consciousness and Information Theory

Learning Compositional Sequences with Multiple Time Scales through a Hierarchical Network of Spiking Neurons