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Proposed are the new types of fast training, scalable analog and digital artificial neural networks (p-networks) based on the new model of formal neuron, described in [1]. The p-network includes synapses with a plurality of weights, and devices of weight selection based on the intensity of the incoming signal. Versions of the p-networks are present...
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Darwin’s theory of evolution with focus on the origin of new species was formulated in an era in which the principles of genetics, biochemistry, physiology, communication etc. were not an issue. Numerous revolutionary new insights have since been gained. 1. The ‘sender-receiver communicating compartment”, a classical but still valuable concept, is better suited than ‘the cell’ to serve the role of universal unit of structure and function, ‘the cell’ being the smallest such unit; 2. Not only genetic, but non-genetic mechanisms as well contribute to variability that can be passed onto the next generation; 3. Natural selection, the almost unanimously accepted universal driving force of evolution, is itself the result of preceding problem-solving activity enabled by the principles of communication; 4. A logically deduced, unambiguous definition of ‘Life’ has been published so that now the key question can shift from Darwin’s formulation towards “How does ‘Life’, with its many aspects, change in the course of time”? Communication activity represents the very heart of being alive, thus of ‘Life itself’. In digital-era wording, living entities are hardware-software double continua. This paper advances an easily teachable change in paradigm, namely that evolution concerns ever changing complexes of signalling pathways, chemical and other, that occasionally yield both new species and additional (at least 16) levels of communication. This approach complements the genetic basis of the New Synthesis with several as yet undervalued mechanisms from physiology and development. In particular, ‘the universal self-generated electrical dimension of cells’ and Lamarckism deserve an upgrade.
In this paper, a new model of formal neuron, analog mechanisms of neuron training, and a new model of biological feedback are proposed. The statement is supported by the neurobiological data published by other authors and through our experiments in silico. Key qualitative and quantitative differences of the proposed neural network model from the concept accepted today are discussed. A new concept reflects the mechanisms of memory formation. The model bridges the gap between the micro-level of the molecular processes in a neuron and the macro-level of information processing and storage in brain. Thus, an opportunity appears of modeling the processes occurring in brain, as well as developing the artificial neural networks, which are trained in a real-time mode, and are not limited in their structure and complexity of connections. The proposed model is easily implemented, both as virtual emulation and by means of digital and analog artificial neural networks.
