Introduction

The function of the brain is a far cry from that of existing computer systems. Our knowledge of the information processing mechanism of the brain is rather limited, but we know that the brain consists of a large number of neurons and that the individual neuron can be regarded as a sort of threshold element. So, we can find some similarities between the neuron systems and the existing computer systems.

The similarity we are especially interested in is that both of them can be considered as aggregates of digital circuits. Any function of the digitial circuit which is independent of the previous state can be easily implemented by a two-level AND-to-OR gate network. In addition the threshold element which substitutes for a neuron performs the function of an AND gate or an OR gate according to its threshold value. Those facts inspired us with some ideas about our AND–OR analog of neuron networks. Our neuron network also has two levels. The first level of the network acts as an analyzer of the input patterns, and the second level acts as a generator of the output patterns. The operations assigned to the two levels correspond to those of the AND plane and the OR plane, respectively.

The function of a standard AND-to-OR gate network is determined by its inherent wiring. However, that of our neuron network is to be formed by the interaction with the given environment. The learning process of the network we assume is based on the biological hypothesis that the creatures which have neuron systems tend to avoid continuous and invariable stimuli, in other words, they are fond of moderate changes of the environment. For instance, the reflex including the avoidant behavior from danger should be explained by this hypothesis.

Introduction

It is quite evident that, while all sane people desire peace, all countries must practically provide for their self-defense. Thus, issues of procurement and implementation of combat systems must be objectively analyzed. When performing such analyses, it is useful and important to distinguish between specific combat components (‘anatomy’) and the Command, Control and Communications (C3) functions (‘physiology’) of these systems. If enormous financial and human resources must indeed be spent on a specific weapons system, then C3 models can offer procurement decision aids to spending these resources more efficiently. However, even before procuring specific weapon components, C3 models should be used to develop battle-management decision aids to help determine if it is feasible to consider building planned large-scale systems at all.

Even without agreement on just what C3 is, there is widespread criticism that we do not spend enough on C3 relative to what we spend on specific weapons systems (Blair, 1985). The Eastport Study Group (Eastport Study Group, 1985) has made this issue its primary concern with regard to the Strategic Defense Initiative (SDI) program. There is also an everpresent problem of weighing the political and military aspects between the hierarchical and distributed designs of C3, the former being politically desirable and appropriate for deterministic or modestly stochastic operations, and the latter being more appropriate for severely stochastic systems (Orr, 1983).

Introduction

The quest or search for the ‘code’ or ‘codes’ involved in short-term memory and information processing in higher mammalian cortex is one of the most exciting and challenging problems in all of science. The recent experimental progress and results from recording in cortex using large arrays of microelectrodes (Krüger & Bach, 1981; Bach & Krüger, 1986) and using optical dye techniques (Blasdel & Salama, 1986; Grinvald et al., 1981) offer great opportunities in the search for the code. The crosscorrelation analyses of these type of data are enormously difficult (Gerstein, Perkel & Dayhoff, 1985; Aertsen, Gerstein & Johannesma, 1986). Thus the close interplay of new theoretical models and experiment will be crucial.

The basis for the tremendous magnitudes of the processing capabilities and the memory storage capacities remain mysteries despite the substantial efforts and results in modeling neural networks; see, e.g., references in Amari & Arbib (1982), Ballard (1986) and Pisco (1984). We believe the Mountcastle (1978) columnar organizing principle for the functioning of neocortex will help provide a basis for these phenomena and we constructed the trion model (Shaw, Silverman & Pearson, 1985; Shaw, Silverman & Pearson, 1986; Silverman, Shaw & Pearson, 1986). Mountcastle proposed that the well-established cortical column (Goldmann–Rakic, 1984), roughly 500 μm in diameter, is the basic network in the cortex and is comprised of small irreducible processing sub-units. The sub-units are connected into the columns or networks having the capability of complex spatial–temporal firing patterns.

Introduction

Computer simulation of the activity of complex neural networks representing substantial portions of the brain is limited by a number of practical considerations, notably the capacity of existing computers and the finite human resources available for analysis of a proliferating output. Whatever the specific model chosen, whether operating in discrete or continuous time, and whether involving the firing states or the firing rates of neurons as the basic dynamical variables, there will arise the possibility that ‘edge effects’ seriously diminish the relevance of the simulation to the behavior of the actual biological system. Such effects may arise, principally, from the fact that the number of neuronal elements in the simulation is too small, or, secondarily, from the fact that the numbers of synaptic inputs to given elements are inappropriate.

In this contribution we shall make an attempt to quantify edge effects in terms of a simple conception of interneuronal distance, reasoning that the asymptotic autonomous behavior of neural models will hinge critically on the topological properties of the net. This will be especially true of the repertoire of cyclic modes (Clark, Rafelski & Winston, 1985) of an assembly of N binary threshold elements operating syncronously in discrete time. As a first approximation to a meaningful definition of the distance dki from neuron i to neuron k in such models, one may use simply the minimum number of synaptic junctions which information must traverse in going from i to k.

Introduction

Cognition, according to Ulric Neisser, ‘refers to all processes by which the sensory input is transformed, reduced, elaborated, stored, recovered, and used. It is concerned with these processes even when they operate in the absence of relevent stimulation, as in images and hallucinations’ (Neisser, 1966). To discover some of the neural mechanisms operating in these processes remains one of the great challenges of neuroscience.

Significant progress has been made in analyzing the mapping and coding processes that occur in the first few stages of mammalian vision. These processes of so-called early vision involve neural mechanisms that sort out geometrical features, heighten contrast, emphasize contours, and help determine shape and motion of objects (Marr & Poggio, 1979; Marr & Hildreth, 1980; Marr, 1982).

Cognition, as Neisser defines it, must go beyond these early processes, utilizing not only innate circuitry, but bringing the full richness of stored experience to bear on the new sensory information. The time scale of such processes must extend from the order of a hundred milliseconds to seconds and beyond.

Cognition and reafference

These higher and later cognitive processes are usually relegated to cortical association areas. In the present model, however, we assume that significant changes in the afferent sensory information are brought about at relatively peripheral sensory levels – the lateral geniculate nucleus in the case of vision – and that these changes play an important role in the transformation, reduction, and elaboration of the sensory input.

Introduction

The use of pharmacological agents in neuroendocrine studies had a significant impact on our perception of the control mechanisms involved in prolactin secretion. In contrast to other anterior pituitary hormones, prolactin is thought to be regulated by the hypothalamus through a prolactin inhibiting factor (PIF) a peptide of MW < 5000 that is tonically released into the hypophysial portal vessels. The prolactin secretory cells themselves are assumed to be driven by a prolactin releasing factor (PRF), an unidentified as yet neurosecretory product. PRF neurosecretory cells are in turn thought to be driven by serotoninergic neurons located in the medial basal hypothalamus.

Of the major CNS neurotransmitters dopamine is a potent inhibitor of prolactin release, although the exact nature of the interaction between the PIF and dopamine is at best unclear at present. The original postulate that PIF secretion is stimulated by dopaminergic neurons located in the medial basal hypothalamus is challenged by the fact that dopamine receptors have been located on the prolactin secretory cells (Clemens, 1976). The emerging synthetic view of the problem postulates that the PIF secreting cells act in parallel and are at the same time driven by the dopaminergic neurons of the medial basal hypothalamus (Fig. 33.1).

Serotonin is the other major CNS neurotransmitter involved in the control of prolactin secretion. It is again as yet unclear whether serotonin stimulates prolactin secretion by inhibiting the PIF release or by stimulating PRF release.