# On Implementing Technomorph Biology for Inefficient Computing
## 1 Introduction  
Concerning computing performance, efficiency, and power, we have a high standard, a desired goal, and an existing prototype: \"The human brain is...the most powerful and energy-efficient computer known to humankind\" [1]. However, when attempting to imitate its operations, the fundamental difficulty is that \"engineers want to design systems; neuroscientists want to analyze an existing one they little understand\" [2]. Despite the impressive results of grandiose projects [1, 3], \"we so far lack principles to understand rigorously how computation is done in living, or active, matter\" [4]. Even within biology, the too-close parallels with the electric operation are at least questioned [5] (given that the model is called \"realistic\"): \"Is realistic neuronal modeling realistic?\" Engineers have grasped a few aspects of the active matter's operation and implemented those aspects without their context within the frames of their possibilities. Implementing \"lacking the principles of understanding how computation is done in living matter\" in the different contexts of technical computing is dangerous. It leads to solutions that are neither biological nor technical. A typical example of misunderstanding the role of the most evident phenomenon in neural systems is spiking. Many publications and technical solutions use the idea of\"spiking neural networks\". As we discuss in Section 4, spiking is just a technical tool nature uses to transmit information. Although it is well known from communication theory [6] that a single spike cannot deliver information, so-called neural information theory extensively uses the fallacy [7; 8; 9] that firing rate delivers information. Answering the fundamental question is abandoned: how the information is represented in neurons, in their networks, and in the transmission signal called spike. Despite that, the relevance of the \"coding metaphor\" is at least questionable for biology [10]; different coding systems are created based on hundreds of communication models [7; 11]; not related to [PERSON]'s model, but referring to its theory. \"What makes the brain a remarkable information processing organ is not the complexity of its neurons but the fact that it has many elements interconnected in a variety of complex ways\" [12], p. 37. Similarly, definitions such as \"Multiple neuromorphic systems use Spiking Neural Network (SNN) to perform computation in a way that is inspired by concepts learned about the human brain\" [13] and \"SNNs are artificial networks made up of neurons that fire a pulse, or spike, once the accumulated value of the inputs to the neuron exceeds a threshold\" [13] are misleading. That review \"looks at how each of these systems solved the challenges of forming packets with spiking information and how these packets are routed within the system\". In other words, the technically needed \"servo\" mechanism, a well-observable symptom of neural operation, inspired the \"idea of spiking networks\". The idea lacks knowing neurons' information handling, replaces the (in space and time) distributed information content by well-located timestamps in packets, the continuous analog signal transfer by discrete packet delivering, the dedicated wiring of neural systems by bus-centered transfer with arbitration and packet routing, and the continuous biological operating time with a mixture of digital quanta of computer processing plus transferring time. Even the order of timestamps conveyed in spikes (not to mention the information they deliver) is not resemblant to biology; furthermore, due to the vast need for communication, the overwhelming majority of the generated events must be killed [14] without delivering and processing them to provide a reasonable operating time and to avoid the collapse [15] of the communication system. In the rest of the aspects, the system may provide an excellent imitation of the brain.