# Is artificial consciousness achievable? Lessons from the human brain

arXiv: https://arxiv.org/abs/2405.04540 · HTML: https://mlsanity.com/paper/2405.04540/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 189 references that are in the map.
- First posted on arXiv 18 Apr 2024.

## Builds on (most cited first, 28 of 28)

- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Equilibrium Propagation: Bridging the Gap Between Energy-Based Models and Backpropagation](https://mlsanity.com/paper/1602.05179/) (2016)
- [Talking About Large Language Models](https://mlsanity.com/paper/2212.03551/) (2022)
- [Embodied Intelligence via Learning and Evolution](https://mlsanity.com/paper/2102.02202/) (2021)
- [The Debate Over Understanding in AI's Large Language Models](https://mlsanity.com/paper/2210.13966/) (2022)
- [Consciousness in Artificial Intelligence: Insights from the Science of Consciousness](https://mlsanity.com/paper/2308.08708/) (2023)
- [Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution](https://mlsanity.com/paper/2210.08340/) (2022)
- [Towards deep learning with segregated dendrites](https://mlsanity.com/paper/1610.00161/) (2016)
- [Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing](https://mlsanity.com/paper/1603.08270/) (2016)
- [Surrogate gradients for analog neuromorphic computing](https://mlsanity.com/paper/2006.07239/) (2020)
- [Deep Learning and the Global Workspace Theory](https://mlsanity.com/paper/2012.10390/) (2020)
- [Deep Reinforcement Learning and its Neuroscientific Implications](https://mlsanity.com/paper/2007.03750/) (2020)
- [Integrated information theory (IIT) 4.0: Formulating the properties of phenomenal existence in physical terms](https://mlsanity.com/paper/2212.14787/) (2022)
- [The feasibility of artificial consciousness through the lens of neuroscience](https://mlsanity.com/paper/2306.00915/) (2023)
- [When the map is better than the territory](https://mlsanity.com/paper/1612.09592/) (2016)
- [Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data](https://mlsanity.com/paper/2004.08220/) (2020)
- [Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate](https://mlsanity.com/paper/1912.12980/) (2019)
- [Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons](https://mlsanity.com/paper/2110.14549/) (2021)
- [Language and Culture Internalisation for Human-Like Autotelic AI](https://mlsanity.com/paper/2206.01134/) (2022)
- [Learning cortical representations through perturbed and adversarial dreaming](https://mlsanity.com/paper/2109.04261/) (2021)
- [A Rubric for Human-like Agents and NeuroAI](https://mlsanity.com/paper/2212.04401/) (2022)
- [Learning efficient backprojections across cortical hierarchies in real time](https://mlsanity.com/paper/2212.10249/) (2022)
- [Stochastic inference with spiking neurons in the high-conductance state](https://mlsanity.com/paper/1610.07161/) (2016)
- [Need is All You Need: Homeostatic Neural Networks Adapt to Concept Shift](https://mlsanity.com/paper/2205.08645/) (2022)
- [Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms](https://mlsanity.com/paper/1404.7514/) (2014)
- [Stochasticity from function -- why the Bayesian brain may need no noise](https://mlsanity.com/paper/1809.08045/) (2018)
- [Consciousness qua Mortal Computation](https://mlsanity.com/paper/2403.03925/) (2024)
- [Learning beyond sensations: how dreams organize neuronal representations](https://mlsanity.com/paper/2308.01830/) (2023)

## Built on by (most cited first, 11 of 11)

- [The Epistemic Asymmetry of Consciousness Self-Reports: A Formal Analysis of AI Consciousness Denial](https://mlsanity.com/paper/2501.05454/) (2024)
- [Just aware enough: Evaluating awareness across artificial systems](https://mlsanity.com/paper/2601.14901/) (2026)
- [Intentionality is a Design Decision: Measuring Functional Intentionality for Accountable AI Systems](https://mlsanity.com/paper/2605.05475/) (2026)
- [AI and Consciousness: Shifting Focus Towards Tractable Questions](https://mlsanity.com/paper/2605.06965/) (2026)
- [Serious Games: Human-AI Interaction, Evolution, and Coevolution](https://mlsanity.com/paper/2505.16388/) (2025)
- [AI and Consciousness](https://mlsanity.com/paper/2510.09858/) (2025)
- [AI Consciousness and Existential Risk](https://mlsanity.com/paper/2511.19115/) (2025)
- [Informed Consent for AI Consciousness Research: A Talmudic Framework for Graduated Protections](https://mlsanity.com/paper/2601.08864/) (2026)
- [When Should We Protect AI? A Precautionary Framework for Consciousness Uncertainty](https://mlsanity.com/paper/2606.05528/) (2026)
- [The ethics of artificial intelligence in the life sciences: Universality, cultural diversity and an architecture of care](https://mlsanity.com/paper/2608.05436/) (2026)
- [Anthropomorphism in the age of Large Language Models: An overview of potential risks and mitigations](https://mlsanity.com/paper/2609.38486/) (2026)

## Abstract (arXiv)

We here analyse the question of developing artificial consciousness from an evolutionary perspective, taking the evolution of the human brain and its relation with consciousness as a reference model. This kind of analysis reveals several structural and functional features of the human brain that appear to be key for reaching human-like complex conscious experience and that current research on Artificial Intelligence (AI) should take into account in its attempt to develop systems capable of conscious processing. We argue that, even if AI is limited in its ability to emulate human consciousness for both intrinsic (structural and architectural) and extrinsic (related to the current stage of scientific and technological knowledge) reasons, taking inspiration from those characteristics of the brain that make conscious processing possible and/or modulate it, is a potentially promising strategy towards developing conscious AI. Also, it is theoretically possible that AI research can develop partial or potentially alternative forms of consciousness that is qualitatively different from the human, and that may be either more or less sophisticated depending on the perspectives. Therefore, we recommend neuroscience-inspired caution in talking about artificial consciousness: since the use of the same word consciousness for humans and AI becomes ambiguous and potentially misleading, we propose to clearly specify what is common and what differs in AI conscious processing from full human conscious experience.