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Purpose – The purpose of this chapter is to be as comprehensive as possible about what is known about mirror neurons at this time.

Design/methodology/approach – This chapter offers a comprehensive critique including Churchland's hesitations about findings on mirror neurons (2011) which are balanced by Ramachandran's conviction that much of the research on mirror neurons is valid (2011). Following this is a summary of the results of the Mirror Neuron Forum (2011) wherein leading mirror neuron researchers exchange their views and conclusions about this subject.

Findings – The few single cells measures that we have show that they are much wider distributed throughout the brain than we have previously imagined. It should be stressed that single measures of mirror neurons have occurred albeit in limited situations. This establishes once and for all their relevance to humans.

Originality/value – The work on mirror neurons is a critical contribution from neuroscience to bringing the social brain into sociology and refining our understandings of intersubjectivity and of our biologically driven connections with others.

In the last half-century, individual sensory neurons have been bestowed with characteristics of the whole human being, such as behavior and its oft-presumed precursor, consciousness. This anthropomorphization is pervasive in the literature. It is also absurd, given what we know about neurons, and it needs to be abolished. This study aims to first understand how it happened, and hence why it persists.

The peer-reviewed sensory-neurophysiology literature extends to hundreds (perhaps thousands) of papers. Here, more than 90 mainstream papers were scrutinized.

Anthropomorphization arose because single neurons were cast as “observers” who “identify”, “categorize”, “recognize”, “distinguish” or “discriminate” the stimuli, using math-based algorithms that reduce (“decode”) the stimulus-evoked spike trains to the particular stimuli inferred to elicit them. Without “decoding”, there is supposedly no perception. However, “decoding” is both unnecessary and unconfirmed. The neuronal “observer” in fact consists of the laboratory staff and the greater society that supports them. In anthropomorphization, the neuron becomes the collective.

Anthropomorphization underlies the widespread application to neurons Information Theory and Signal Detection Theory, making both approaches incorrect.

A great deal of time, money and effort has been wasted on anthropomorphic Reductionist approaches to understanding perception and consciousness. Those resources should be diverted into more-fruitful approaches.

A long-overdue scrutiny of sensory-neuroscience literature reveals that anthropomorphization, a form of Reductionism that involves the presumption of single-neuron consciousness, has run amok in neuroscience. Consciousness is more likely to be an emergent property of the brain.

It is suggested that the left hemispheric neurons and the magnocellular visual system are specialized in tasks requiring a relatively small number of large neurons having a fast reaction time due to a high firing rate or many dendritic synapses of the same neuron which are activated simultaneously. On the other hand the right hemispheric neurons and the neurons of the parvocellular visual system are specialized in tasks requiring a relatively larger number of short term memory (STM) Hebbian engrams (neural networks). This larger number of engrams is achieved by a combination of two strategies. The first is evolving a larger number of neurons, which may be smaller and have a lower firing rate. The second is evolving longer and more branching axons and thus producing more engrams, including engrams comprising neurons located at cortical areas distant from each other. This model explains why verbal functions of the brain are related to the left hemisphere, and the division of semantic tasks between the left hemisphere and the right one. This explanation is extended to other cognitive functions like visual search, ontological cognition, the detection of temporal order, and the dual cognitive interpretation of the perceived physical phenomena.

Presents one possible synthesis of the artificial neural network using linking algebra. The mathematical model given is the basis for such a neural network organization. The problem which appears is a mode of realization of the artificial neuron. Starting with the experience gained by many experiments, the model of a neuron that can be realized in the memory of computer is presented.

The purpose of the paper is to model the relationship between the yield strength of austenitic stainless steel and its chemical composition through the employment of artificial neural network (ANN). The effect of annealing temperature is also taken into consideration.

The influence of network parameters, total number of neurons, number of neurons in a hidden layer, number of hidden layers and the interlayer distribution of neurons with a constant total neuron number, on the achievable training error is studied. Different learning rules available in MATLAB are used to assess the learning efficiencies of various networks.

It is found that increasing neuron number leads to a lowering of achievable training error up to a certain value beyond which training error remains constant. Increasing number of layers at constant total number of neurons causes a rise in the achievable training error. It is noted that if there is a sudden restriction of data flow in an ANN architecture, the achievable training error becomes higher. Interlayer distribution of neurons in ANNs used with different algorithms is found to have significant influence on the predictive performance of the networks.

From the study on metallurgical validation of the output of various ANNs, it appears that mere attainment of a lower training error is not sufficient to achieve better generalization. A convergent network topology is found to be better than a divergent one in respect of effectively describing the input‐output relationship in austenitic stainless steel.

Assuming that all neurons are built from the same molecular materials, as was discussed in a previous paper, we show that the electrical behaviour of each neuron can be characterized by only three parameters. A systematic discussion allows us to define six categories of neurons. Every one is well described by a typical numerical example. For each category, very useful input‐output relations are furnished, including, if required, an adaptive feedback.

Using results explained in previous papers, we show how specific properties of great cortical cells give rise to electro‐encephalographic events. Analogies between EEG rhythms and spontaneous retinal rhythms are considered. The regulations shaping the various EEG waves are described.

Suggests that the arousability theory of intelligence and personality of Robinson (1996) lacks two important factors: the influence of neural transmission errors and of hemisphericity on intelligence and personality. It is considered that at least two factors contribute to intelligence. The first factor is the potential energetic level of Hebb’s engrams, which may be related to arousability. The second factor is the probability of neural transmission errors. It is suggested that the theory of H.J. Eysenck, that a neural message is sent repeatedly until it is accepted identically a certain number of times, which is smaller for more intelligent subjects, is correct.

An estimate is made of the connectivity of a mammalian neuron, i.e., the number of other neurons to or from which an average neuron directly connects. The value derived is about 10. Some functional implications of the value of the connectivity are considered, particularly mental illness, epilepsy, and intelligence. The “length” and “width”, in terms of neurons, of a functional neural channel are discussed.