Research and publications

The science of intelligence

Research in brain-inspired artificial intelligence, and its implications for how we build, govern and live with intelligent systems.

  1. Brain-inspired AI
  2. Neuromorphic intelligence
  3. Agentic systems
  4. Enterprise and societal implications
A relationship between ideas, not a claim that these technologies are the same or must develop in this order.
1 · Peer-reviewed publications

Published research

Journal articles in brain-inspired AI and spiking neural networks.

  1. 2021
    NeuroSense: Short-Term Emotion Recognition and Understanding Based on Spiking Neural Network Modelling of Spatio-Temporal EEG Patterns ↗

    C. Tan, M. Šarlija, N. Kasabov

    Neurocomputing, vol. 434, pp. 137–148 · DOI 10.1016/j.neucom.2020.12.098

    Recognises short-term emotional states from EEG brain signals by modelling how patterns of activity unfold across space and time in a spiking neural network.

    Peer-reviewed
  2. 2020
    Spiking Neural Networks: Background, Recent Development and the NeuCube Architecture ↗

    C. Tan, M. Šarlija, N. Kasabov

    Neural Processing Letters, vol. 52, no. 2, pp. 1675–1701 · DOI 10.1007/s11063-020-10322-8

    A review of spiking neural networks, from their biological background to recent developments, with a detailed account of the NeuCube brain-inspired architecture.

    Peer-reviewed
  3. 2020
    FusionSense: Emotion Classification Using Feature Fusion of Multimodal Data and Deep Learning in a Brain-Inspired Spiking Neural Network ↗

    C. Tan, G. Ceballos, N. Kasabov, N. Puthanmadam Subramaniyam

    Sensors, vol. 20, no. 18, article 5328 (open access) · DOI 10.3390/s20185328

    Classifies emotion by fusing features from several kinds of data and combining deep learning with a brain-inspired spiking neural network.

    Peer-reviewed

A note on names: earlier peer-reviewed publications appear under the name C. Tan (Clarence Tan), a name Dr. Elias Kairos Chen previously used.

2 · Current research

Working papers and preprints

Recent work that has not yet completed peer review.

  1. 2025
    Beyond Transformers: A Neuro-Symbolic and Quantum Hybrid Architecture Toward Artificial General Intelligence ↗

    E. K. Chen

    TechRxiv preprint · DOI 10.36227/techrxiv.176127375.51816942/v1

    Proposes an architecture that combines neural, symbolic and quantum approaches as a path beyond today's transformer models toward general intelligence.

    Preprint, not peer-reviewed
  2. 2025
    Neuroplasticity and Digital Media: Brain Development Implications for Adolescent Mental Health. A Systematic Review and Meta-Analysis of Neuroimaging Studies ↗

    E. K. Chen, V. Tan, K. Garcia-Tan

    OSF Preprints · DOI 10.31234/osf.io/bjfyr_v1

    Reviews neuroimaging evidence on how digital media use relates to adolescent brain development and mental health.

    Preprint, not peer-reviewed
  3. 2025
    Privacy by Psychology: Developmental Framework for Adolescent Digital Rights and Family Relationships ↗

    E. K. Chen

    Preprint (Europe PMC)

    Proposes a developmental framework for adolescents' digital privacy and rights, set within family relationships.

    Preprint, not peer-reviewed
3 · Technical foundations

A plain-language guide

Accessible descriptions for non-technical readers.

Brain-inspired AI

Most AI today is loosely modelled on the brain. Brain-inspired AI goes further, borrowing how real neurons communicate, learn and adapt over time.

Why it matters for leaders: it points to AI that learns continuously from less data and uses far less energy.

Spiking neural networks

Instead of passing numbers continuously, spiking networks communicate through brief electrical pulses, as biological neurons do. Timing carries information.

Why it matters: they are well suited to signals that change over time, such as sensors, speech, physiological data and markets.

Neuromorphic computing

Hardware and software designed around the brain's architecture, processing information where it is stored rather than shuttling it back and forth.

Why it matters: it could make capable AI run on devices at the edge, with a fraction of today's energy cost.

Intelligent systems

How many specialised AI components, agents and people combine into systems that perceive, decide and act, and where those systems fail.

Why it matters: enterprise AI is becoming a system of agents, so governing the system matters as much as any single model.

4 · Enterprise and strategic applications

From research to practice

How the research informs applied frameworks developed through Kiros Intelligence Group.

  1. 1

    Research and ideas

    Brain-inspired AI, agency and the future of intelligence.

     
  2. 2

    Organisational readiness

    Can the organisation capture value from AI?

    AI Readiness & Value Index ↗
  3. 3

    Financial value creation

    Which lines in the accounts can AI move, at what full cost?

    Agent Value Ledger ↗
  4. 4

    Responsible delegation

    Which decisions may AI make, recommend, or never touch?

    AI Delegation Map ↗
  5. 5

    Measured outcomes

    Results tracked against the baseline, and reported honestly.