The brain is not a prediction machine, it does not make controlled hallucinations, best guesses, neither does predictive coding nor predictive processing explain its function. Deep learning and computer models may be great in making predictions, but the most advanced artificial intelligence anywhere till date is a good memory system, where inferences are smartly made based on data, but what it means to feel-like or have feelings, a major component of natural intelligence exceeds its capability. The computer that can win games, predict protein structures, drive itself and much else can do nothing when struck by some object. It does not have actual feelings, which could also have been picked up, to feel-like before the situation. For example -- to feel fear, while approaching a situation outside its training data.

A new research article, THE THERMODYNAMIC ANALYSIS OF NEURAL COMPUTATION, examines the energy-information cycle of sensory processing in the brain. However, the work has relevance for artificial intelligence, especially deep learning. The cortical brain is an evolutionary marvel which interacts with the outside world via self-regulation, based on its resting or ground state. The resting state, which is disturbed during stimulus and sensory processing, is recovered by automatic operations. The brain's energy need multiplies in the complex brain of warm-blooded animals.

We introduce total wire length as salient complexity measure for an analysis of the circuit complexity of sensory processing in biological neural systems and neuromorphic engineering. This new complexity measure is applied to a set of basic computational problems that apparently need to be solved by circuits for translation-and scale-invariant sensory processing. We exhibit new circuit design strategies for these new benchmark functions that can be implemented within realistic complexity bounds, in particular with linear or almost linear total wire length. 1 Introduction Circuit complexity theory is a classical area of theoretical computer science, that provides estimates for the complexity of circuits for computing specific benchmark functions, such as binary addition, multiplication and sorting (see, e.g.

We introduce total wire length as salient complexity measure for an analysis of the circuit complexity of sensory processing in biological neural systems and neuromorphic engineering. This new complexity measure is applied to a set of basic computational problems that apparently need to be solved by circuits for translation-and scale-invariant sensory processing. We exhibit new circuit design strategies for these new benchmark functions that can be implemented within realistic complexity bounds, in particular with linear or almost linear total wire length. 1 Introduction Circuit complexity theory is a classical area of theoretical computer science, that provides estimates for the complexity of circuits for computing specific benchmark functions, such as binary addition, multiplication and sorting (see, e.g.

We introduce total wire length as salient complexity measure for an analysis ofthe circuit complexity of sensory processing in biological neural systems and neuromorphic engineering. This new complexity measure is applied to a set of basic computational problems that apparently need to be solved by circuits for translation-and scale-invariant sensory processing. Weexhibit new circuit design strategies for these new benchmark functions that can be implemented within realistic complexity bounds, in particular with linear or almost linear total wire length. 1 Introduction Circuit complexity theory is a classical area of theoretical computer science, that provides estimates for the complexity of circuits for computing specific benchmark functions, such as binary addition, multiplication and sorting (see, e.g.