The search for the neural correlates of consciousness is in need of a systematic, principled foundation that can endow putative neural correlates with greater predictive and explanatory value. Here, we propose the predictive processing framework for brain function as a promising candidate for providing this systematic foundation. The proposal is motivated by that framework’s ability to address three general challenges to identifying the neural correlates of consciousness, and to satisfy two constraints common to many theories of consciousness. Implementing the search (...) for neural correlates of consciousness through the lens of predictive processing delivers strong potential for predictive and explanatory value through detailed, systematic mappings between neural substrates and phenomenological structure. We conclude that the predictive processing framework, precisely because it at the outset is not itself a theory of consciousness, has significant potential for advancing the neuroscience of consciousness. (shrink)

The approach used by physics is based on the identification and study of ideal objects, which is also the basis of biophysics, in combination with von Neumann heuristic modeling and functional fractionation according to R.Rosen is discussed as a tool for studying the properties of consciousness. The object of the study is a kind of line of analog systems: the human brain, the vertebrate brain, the invertebrate brain and artificial neural networks capable of reflection, which is a key property characteristic (...) of consciousness. Reflection in the broad sense of the word, understood as an internal representation of the external world, is characteristic of a wide range of animals, and some of them (bumblebees, fish) even demonstrate reflection in the narrow sense of the word, understood as an inner self-representation. This complex behavior is realized by miniature brains of ~1 million neurons. The use of simple recurrent neural networks (RNNs) to obtain answers to general questions is illustrated. For example, it has been shown a small RNS is able to pass delayed matching to sample (DMTS) test, forming an individual dynamic representation of the received stimulus, allowing decoding by a special external neural detector.. It has been demonstrated in the reflexive game “even-odd”, the RNS has a huge advantage over a multi-layered neural network, with the same and a larger number of neurons – reflection defeats regression. It was found that the asymmetry of outcomes in the odd-even game, which was explained by various causes, including psychological ones – “it’s easier to catch up than to run away”, is reproduced in the game of two RNNs. Obviously, there are no psychological causes here and the advantage of the player playing for “even” is explained by the more complex strategy of the “odd” player – he needs to predict the opponent’s move and choose the opposite one. (shrink)

A central problem in the cognitive sciences is identifying the link between consciousness and neural computation. The key features of consciousness—including the emergence of representative information content and the initiation of volitional action—are correlated with neural activity in the cerebral cortex, but not computational processes in spinal reflex circuits or classical computing architecture. To take a new approach toward considering the problem of consciousness, it may be worth re‐examining some outstanding puzzles in neuroscience, focusing on differences between the cerebral cortex (...) and spinal reflex circuits. First, the mammalian cerebral cortex exhibits exascale computational power, a feature that is not strictly correlated with the number of binary computational units; second, individual computational units engage in noisy coding, allowing random electrical events to gate signaling outcomes; third, this noisy coding results in the synchronous firing of statistically random populations of cells across the neural network, at a range of nested frequencies; fourth, the system grows into a more ordered state over time, as it encodes the predictive value gained through observation; and finally, the cerebral cortex is extraordinarily energy efficient, with very little free energy lost to entropy during the work of information processing. Here, I argue that each of these five key features suggest the mammalian brain engages in probabilistic computation. Indeed, by modeling the physical mechanisms of probabilistic computation, we may find a better way to explain the unique emergent features arising from cortical neural networks. (shrink)