Abstract
Being able to correctly predict the future and to adjust own actions accordingly can offer a great survival advantage. In fact, this could be the main reason why brains evolved. Consciousness, the most mysterious feature of brain activity, also seems to be related to predicting the future and detecting surprise: a mismatch between actual and predicted situation. Similarly at a single neuron level, predicting future activity and adapting synaptic inputs accordingly was shown to be the best strategy to maximize the metabolic energy for a neuron. Following on these ideas, here we examined if surprise minimization by single neurons could be a basis for consciousness. First, we showed in simulations that as a neural network learns a new task, then the surprise within neurons (defined as the difference between actual and expected activity) changes similarly to the consciousness of skills in humans. Moreover, implementing adaptation of neuronal activity to minimize surprise at fast time scales (tens of milliseconds) resulted in improved network performance. This improvement is likely because adapting activity based on the internal predictive model allows each neuron to make a more “educated” response to stimuli. Based on those results, we propose that the neuronal predictive adaptation to minimize surprise could be a basic building block of conscious processing. Such adaptation allows neurons to exchange information about own predictions and thus to build more complex predictive models. To be precise, we provide an equation to quantify consciousness as the amount of surprise minus the size of the adaptation error. Since neuronal adaptation can be studied experimentally, this can allow testing directly our hypothesis. Specifically, we postulate that any substance affecting neuronal adaptation will also affect consciousness. Interestingly, our predictive adaptation hypothesis is consistent with multiple ideas presented previously in diverse theories of consciousness, such as global workspace theory, integrated information, attention schema theory, and predictive processing framework. In summary, we present a theoretical, computational, and experimental support for the hypothesis that neuronal adaptation is a possible biological mechanism of conscious processing, and we discuss how this could provide a step toward a unified theory of consciousness.
Highlights
“How does the brain work? Gather enough philosophers, psychologists, and neuroscientists together, and I guarantee that a group will rapidly form to advocate for one answer in particular: that the brain is a prediction machine” (Seth, 2020)
Predictive processing was suggested to be one of the most promising approaches to understand consciousness (Yufik and Friston, 2016; Hohwy and Seth, 2020). It is still unclear how predictive processing could be implemented in the brain (Lillicrap et al, 2020), as most of the proposed algorithms require a precise network configuration (Rao and Ballard, 2005; Bastos et al, 2012; Whittington and Bogacz, 2017), which could be difficult to achieve, considering variability in neuronal circuits (y Cajal, 1911)
We proposed that single neurons can internally calculate predictions, which eliminates requirement of precise neuronal circuits (Luczak et al, 2022)
Summary
“How does the brain work? Gather enough philosophers, psychologists, and neuroscientists together (ideally with a few mathematicians and clinicians added to the mix), and I guarantee that a group will rapidly form to advocate for one answer in particular: that the brain is a prediction machine” (Seth, 2020). Predictive processing was suggested to be one of the most promising approaches to understand consciousness (Yufik and Friston, 2016; Hohwy and Seth, 2020) It is still unclear how predictive processing could be implemented in the brain (Lillicrap et al, 2020), as most of the proposed algorithms require a precise network configuration (Rao and Ballard, 2005; Bastos et al, 2012; Whittington and Bogacz, 2017), which could be difficult to achieve, considering variability in neuronal circuits (y Cajal, 1911). We found that maximizing future energy balance by a neuron leads to a predictive learning rule, where a neuron adjusts its synaptic weights to minimize surprise [i.e., the difference between actual (xj) and predicted activity (xj)] This derived learning rule was shown to be a generalization of Hebbian-based rules and other biologically inspired learning algorithms, such as predictive coding and temporal difference learning (Luczak et al, 2022). First, we will implement a predictive learning rule in an artificial neural network, and we will use those simulation results together with biological evidence to propose a predictive neuronal adaptation theory of consciousness
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