Gidon Albert, Aru Jaan, Larkum Matthew E
Institute of Biology, Humboldt University of Berlin, Berlin, Germany.
Institute of Computer Science, University of Tartu, Tartu, Estonia.
Front Neurosci. 2025 May 23;19:1511972. doi: 10.3389/fnins.2025.1511972. eCollection 2025.
Artificial neural networks are becoming more advanced and human-like in detail and behavior. The notion that machines mimicking human brain computations might be conscious has recently caused growing unease. Here, we explored a common computational functionalist view, which holds that consciousness emerges when the right computations occur-whether in a machine or a biological brain. To test this view, we simulated a simple computation in an artificial subject's "brain" and recorded each neuron's activity when the subject was presented with a visual stimulus. We then replayed these recorded signals back into the same neurons, degrading the computation by effectively eliminating all alternative activity patterns that otherwise might have occurred (i.e., the counterfactuals). We identified a special case in which the replay did nothing to the subject's ongoing brain activity-allowing it to evolve naturally in response to a stimulus-but still degraded the computation by erasing the counterfactuals. This paradoxical outcome points to a disconnect between ongoing neural activity and the underlying computational structure, which challenges the notion that consciousness arises from computation in artificial or biological brains.
人工神经网络在细节和行为上正变得越来越先进且类人。机器模仿人类大脑计算可能具有意识这一观念最近引发了越来越多的不安。在此,我们探讨了一种常见的计算功能主义观点,该观点认为当正确的计算发生时意识就会出现——无论这是在机器中还是在生物大脑中。为了检验这一观点,我们在一个人工主体的“大脑”中模拟了一个简单计算,并在向该主体呈现视觉刺激时记录每个神经元的活动。然后我们将这些记录的信号重新播放回相同的神经元,通过有效消除所有原本可能出现的替代活动模式(即反事实情况)来破坏计算。我们发现了一种特殊情况,即重放对主体正在进行的大脑活动没有任何影响——使其能够自然地对刺激做出反应——但仍然通过消除反事实情况破坏了计算。这一矛盾的结果表明正在进行的神经活动与潜在的计算结构之间存在脱节,这对意识源于人工或生物大脑中的计算这一观念提出了挑战。