Constraints of Biological Neural Networks and Their Consideration in AI Applications

Abstract

Biological organisms do not evolve to perfection, but to out compete others in their ecological niche, and therefore survive and reproduce. This paper reviews the constraints imposed on imperfect organisms, particularly on their neural systems and ability to capture and process information accurately. By understanding biological constraints of the physical properties of neurons, simpler and more efficient artificial neural networks can be made (e.g., spiking networks will transmit less information than graded potential networks, spikes only occur in nature due to limitations of carrying electrical charges over large distances). Furthermore, understanding the behavioural and ecological constraints on animals allows an understanding of the limitations of bio-inspired solutions, but also an understanding of why bio-inspired solutions may fail and how to correct these failures.

A Diabetic Diet Suggester and Appointment Scheduler Chatbot using Artificial Intelligence and Cloud

“…Also, showing that it has only an avg of 3.67 manual messages every 2 users asked to the bot. This also concludes that it answers most of the questions as per the result seen in figure [4] and figure [3]…”

“…But using advanced dialog methodology seen in figure [2] system, it allows to develop and makes changes very fast without any complex constraints in order to update the data as soon as possible. Infact combining the api keys to this methodology can give more flexibility in giving a broader approach and better suitable output to a query [3]. To also understand the usage of cloud services which enables a cross platform usage and heavy load balancing access to the particular service helps in a seamless interaction and faster responses/second.…”

“…To create an impact with such technology and look for the most intelligent human like automation, it has to deliver the best accuracy that is spoken like a human and understand like a human [6]. The power of analyzing the human behavior via text messages is to be noticed here as it gives an approximation of 82-85% of human like responses which is satisfied and covered overall messages from various topics by DIABOT seen in figure [3] With a massive 82.3% of Total Coverage text message identification by multiple users.…”

Banknote recognition: investigating processing and cognition framework using competitive neural network

“…Basically, neural networks are learning systems with motivations that are hinged on the biological nervous/cognition processing paradigms (Stafford 2010); they can be considered as simplified computational models which simulate the nervous/brain system in structure and function. These networks have found applications in many important areas where it is highly intricate, tedious or impossible to define mathematical relations associating input parameters with output parameters, neural networks can learn such complex and abstract functions mapping input parameters to output parameters in a phase known as training (Oyedotun and Khashman 2016).…”