Arbib, Érdi, and Szentágothai's book seeks to present a multidisciplinary, multistrategied approach to the study of the mind-brain, encompassing structural, functional, and dynamic perspectives. However, the articulated framework is somewhat underspecified at the cognitive level. The representational level of analysis will need to be fleshed out if the explanatory potential of Arbib et al.'s framework is to be fulfilled.

Arbib, Érdi, and Szentágothai's book should be a required reading for any serious student of the brain. The scope and the accessibility of its presentation of the neurobiological data (especially the functional anatomy of select parts of the central nervous system) more than make up for the peculiarities of the theoretical stance it adopts.

Neural organization describes an approach to analyzing neural function in anatomically defined subsystems in the brain, the hippocampus, cerebellum, sensory systems, thalamus, basal ganglia, and cerebral cortex, combining information on neurocircuitry with mathematical models that link structure with function. It is an up-to-date source on the major schemes and background for neural modeling of the central nervous system and is combined with a Web site that includes tutorials and on-line modeling possibilities.

An impressive review of brain neurophysiology provides the basis for modelling the dynamics of transmission in neural circuits, using appropriate nonlinear mathematics. The coverage is unbalanced, however: the parallel dynamics at the level of behaviour and sensory-cognitive processes are sparsely addressed, so the final chapter fails to indicate the complexity and subtlety of relevant modern work.

A direct equivalence between neural function and neural structure does not provide a fruitful approach to understanding brain functioning. Arbib et al. describe a new and powerful approach to circumvent this problem, which they call schema theory. However, in examples they fall prey to the tradition of finding such equivalences, not doing schema theory justice.

Neural organization contains a wealth of facts from all areas of brain research and provides a useful overview of physiological data for those working outside the immediate field. Furthermore, it gives a good example that the approach of dynamical system theory together with the concepts of cooperative and competitive interaction can be fruitful for an interdisciplinary approach to cognition.

In order to reach a better understanding of brain function, conceptual synergies linking empirical neurobiological studies and neurocomputational studies should be pursued. I describe an example of a potential synergy based on studies of neural network pruning. Simulations demonstrate that selective elimination of connections enhances the computational capacity of networks capable of temporal processing. These findings may shed light on the functional significance of postnatal neuro-developmental pruning of cortical connections that occurs in mammals.

Arbib et al.'s comprehensive review of neural organization, over-relies on modernist concepts and restricts our understanding of brain and behavior. Reliance on terms like coding, transformation, and representation perpetuates a “black-box approach” to the study of the brain. Recognition is due to the authors for attempting to introduce postmodern concepts such as chaos and self-organization to the study of neural organization. However, confusion occurs in the implementation of “biologically rooted” schema theory in which schemas are viewed as computer programs. The inclusion of an additional functional level between structure and dynamics is unnecessary in a postmodernist perspective of brain and behavior.

Neural organization has many facets; multiple descriptive levels and multiple analytical strategies coexist. Although most neuroscientists agree that a multidisciplinary, multistrategic approach is necessary to understand neural organization, diverse individual approaches make it difficult to find the optimal mixture and priority list.

Different sciences approach the brain-behaviour system at various levels, but often apparently share terminology. “Function” is used both ontogenetically and phylogenetically. Within the ontogeny it has various meanings; the one adopted by Arbib et al. is that of mainstream cognitive psychology. This usage is relatively imprecise, and the psychologists who are sceptical about the ability of cognitive psychology to predict behavioural outcomes may have the same reservations about Arbib et al.'s cognitive neuroscience.

This review evaluates pros and cons of the schema theory as a general framework for expressing what Arbib et al. call “systems neuroscience.” We discuss the software/hardware duality of the schema concept and the relative neglect of the mechanical properties of muscles. We propose a computational alternative to the functional decomposition in terms of schemas.

Cognition and behavior are the result of neural processes occurring at multiple levels of organization. Synthetic computational approaches are capable of bridging the gaps between multiple organizational levels and contribute to our understanding of how neural structures give rise to specific dynamical states. Such approaches are indispensable for formulating the theoretical foundations of cognitive neuroscience.

Neuromodelling is one of the techniques of modern neurosciences. The “at a distance” type of triadic synapse is probably the prevailing form of impulse transmission in many parts of the brain. If the genetically controlled cell-to-cell neuronal interconnections are abandoned, self-organisation may be the mechanism of structure formation in the brain. This assumption weakens the position of the reflex arc as the basic functional unit of nervous activities.

The absence of a clear influence of an animal's behavioral responses to Hebbian associative learning in the cerebral cortex requires some changes in the Hebbian learning rules. The participation of the brain monoaminergic systems in Hebbian associative learning is considered.

We clarify the arguments in Neural organization: Structure, function, and dynamics, acknowledge important contributions cited by our critics, and respond to their criticisms by charting directions for further development of our integrated approach to theoretical and empirical studies of neural organization. We first discuss functional organization in general (behavior versus cognitive functioning, the need to study body and brain together, function in ontogeny and phylogeny) and then focus on schema theory (noting that schema theory is not just a top-down theory and discussing the transition from action-oriented perception to cognition). We then turn to dynamical organization, with a focus first on neural modeling and dynamics (clarifying the multiple functions of neurons and brain regions, and looking further at various forms of dynamics) and second on learning, development, and self-organization (looking at monoaminergic systems, reinforcement, self-organization, postnatal development, and disease). We close with a brief philosophical discussion of postmodernism and reductionism.

During human evolutionary history, there were “trade-offs” between expending time and energy on child-rearing and mating, so both men and women evolved conditional mating strategies guided by cues signaling the circumstances. Many short-term matings might be successful for some men; others might try to find and keep a single mate, investing their effort in rearing her offspring. Recent evidence suggests that men with features signaling genetic benefits to offspring should be preferred by women as short-term mates, but there are trade-offs between a mate's genetic fitness and his willingness to help in child-rearing. It is these circumstances and the cues that signal them that underlie the variation in short- and long-term mating strategies between and within the sexes.

Gangestad & Simpson's (G&S's) analysis of strategic pluralism is welcomed as a balance to the current emphasis on between-sex variation. It could have been clarified by acknowledging the extent to which males and females represent fundamentally different mating strategies, since this affects how we view within-sex strategic variation. The distinction between conditional and alternative strategies could also have been highlighted.

Several suggestions are made for revision of Strategic Pluralism Theory (SPT). One revision requires recognition of the impact of individual differences in cognitive and behavioral competence on optimal mating strategy. In addition, SPT may need to incorporate certain self-regulatory processes such as the impact of widespread valuation of mates with one trait on their availability.

Gangestad & Simpson's theory accounts for the existence of male strategic variation admirably well, although it does not specify the precise nature of the adaptation linking male developmental stability to strategic pluralism. In contrast, female strategic variation remains elusive. Empirical data suggest that genetic variation partly underlies such variation, but the causes of female strategic differences largely remain unexplained.

Gangestad & Simpson's arguments may be rendered more substantial and precise by capitalizing on research and theory on choice between reinforced response alternatives. An analysis in terms of feedback functions shows that the effects of individual differences in attractiveness may be understood as constraints on optimality and may be reconciled with the previous research and theory that the authors criticize.