The following factors underlying behavioral plasticity are discussed: (1) reflex adaptability and its role in the voluntary control of movement, (2) degrees of freedom and motor equivalence, and (3) the problem of the discrete organization of motor behavior. Our discussion concerns a variety of innate motor patterns, with emphasis on the wiping reflex in the frog.

It is proposed that central regulation of stretch reflex thresholds governs voluntary control over muscle force and length. This suggestion is an integral part of the equilibrium-point hypothesis, two versions of which are compared.

Kinematic analysis of the wiping reflex in the spinal frog has shown that each stimulated skin site is associated with a group of different but equally effective trajectories directed to the target site. Such phenomena reflect the principle of motor equivalence -the capacity of the neuronal structures responsible for movement to select one or another of a set of possible trajectories leading to the goal. Redundancy of degrees of freedom at the neuronal level as well as at the mechanical level of the body's joints makes motor equivalence possible. This sort of equivalence accommodates the overall flexibility of motor behavior.

An integrated behavioral act or a single movement consists of dynamic components. We distinguish six components for the wiping reflex, each associated with a certain functional goal, specific body positions, and motor-equivalent movement patterns. The nervous system can combine the available components in various ways in forming integrated behavioral sequences. The significance of command neuronal organization is discussed with respect to (1) the combinatory strategy of the nervous system and (2) the relation between continuous and discrete forms of motor control. We conclude that voluntary movements are effected by the central nervous system with the help of the mechanisms that underlie the variability and modifiability of innate motor patterns.

Neuroethology, an interdisciplinary subdivision of neuroscience, has emerged in recent years. Since 1976 there has been a regular session under this heading at the annual meeting of the Society for Neuroscience. In 1980 two introductory texts in English were published on the subject (Ewert 1980; Guthrie 1980), and a third (Camhi 1984) was published recently. There is widespread interest in neural mechanisms underlying behavior, but they encompass such a vast array of often unrelated topics that proponents do not share common goals. This article describes the emergence of ethology as a discipline, pointing out that its practitioners were successful because they confined their research to stereotyped, complex, nonlearned, innate behavioral acts. A limited number of profoundly significant principles emerged. Each of these is redefined. The major concepts of earlier ethology were embodied in a simple hydraulic model used by Konrad Lorenz in 1949 (Lorenz 1950). It is pointed out that this model implies the existence of common neurophysiological mechanisms and neuronal circuitry. This model has now been made obsolete by neurophysiological progress, but with appropriate modifications an updated version may still be useful in focusing attention on possible principles. The initial aim of neuroethology should be to examine the neurophysiological events in a variety of behaviors, exhibited by diverse animals from different phyla, which meet the criteria of innate behavioral acts. The behaviors should be sufficiently complex to interest ethologists, yet they should be addressable with neurophysiological methods down to the cellular level. In the case of vertebrates this may mean working with brain slices as well as whole animals, but for some invertebrates recording should be possible in the nearly intact animal during execution of the behavior. The work will be exacting and very difficult, and it is not likely to get done at all unless neuroethologists recognize that they should both train and discipline themselves and restrict their attention to welldefined goals.

“Sign stimuli” elicit specific patterns of behavior when an organism's motivation is appropriate. In the toad, visually released prey-catching involves orienting toward the prey, approaching, fixating, and snapping. For these action patterns to be selected and released, the prey must be recognized and localized in space. Toads discriminate prey from nonprey by certain spatiotemporal stimulus features. The stimulus-response relations are mediated by innate releasing mechanisms (RMs) with recognition properties partly modifiable by experience. Striato-pretecto-tectal connectivity determines the RM's recognition and localization properties, whereas medialpallio-thalamo-tectal circuitry makes the system sensitive to changes in internal state and to prior history of exposure to stimuli. RMs encode the diverse stimulus conditions referring to the same prey object through different combinations of “specialized” tectal neurons, involving cells selectively tuned to prey features. The prey-selective neurons express the outcome of information processing in functional units consisting of interconnected cells. Excitatory and inhibitory interactions among feature-sensitive tectal and pretectal neurons specify the perceptual operations involved in distinguishing the prey from its background, selecting its features, and discriminating it from predators. Other connections indicate stimulus location. The results of these analyses are transmitted by specialized neurons projecting from the tectum to bulbar/spinal motor systems, providing a sensorimotor interface. Specific combinations of such projective neurons – mediating feature- and space-related messages – form “command releasing systems” that activate corresponding motor pattern generators for appropriate prey-catching action patterns.

Intermediate constructs are required as bridges between complex behaviors and realistic models of neural circuitry. For cognitive scientists in general, schemas are the appropriate functional units; brain theorists can work with neural layers as units intermediate between structures subserving schemas and small neural circuits.

After an account of different levels of analysis, we describe visuomotor coordination in terms of perceptual schemas and motor schemas. The interest of schemas to cognitive science in general is illustrated with the example of perceptual schemas in high-level vision and motor schemas in the control of dextrous hands.

Rana computatrix, the computational frog, is introduced to show how one constructs an evolving set of model families to mediate flexible cooperation between theory and experiment. Rana computatrix may be able to do for the study of the organizational principles of neural circuitry what Aplysia has done for the study of subcellular mechanisms of learning. Approach, avoidance, and detour behavior in frogs and toads are analyzed in terms of interacting schemas. Facilitation and prey recognition are implemented as tectal-pretectal interactions, with the tectum modeled by an array of tectal columns. We show how layered neural computation enters into models of stereopsis and how depth schemas may involve the interaction of accommodation and binocular cues in anurans.

The response of the gill of Aplysia californica Cooper to weak to moderate tactile stimulation of the siphon, the gill-withdrawal response or GWR, has been an important model system for work aimed at understanding the relationship between neural plasticity and simple forms of non-associative and associative learning. Interest in the GWR has been based largely on the hypothesis that the response could be explained adequately by parallel monosynaptic reflex arcs between six parietovisceral ganglion (PVG) gill motor neurons (GMNs) and a cluster of sensory neurons termed the LE cluster. This hypothesis, the Kupfermann–Kandel model, made clear, falsifiable predictions that have stimulated experimental work for many years. Here, we review tests of three predictions of the Kupfermann–Kandel model: (1) that the GWR is a simple, reflexive behaviour graded with stimulus intensity; (2) that central nervous system (CNS) pathways are necessary and sufficient for the GWR; and (3) that activity in six identified GMNs is sufficient to account for the GWR. The available data suggest that (1) a variety of action patterns occur in the context of the GWR; (2) the PVG is not necessary and the diffuse peripheral nervous system (PNS) is sufficient to mediate these action patterns; and (3) the role of any individual GMN in the behaviour varies. Both the control of gill-withdrawal responses, and plasticity in these responses, are broadly distributed across both PNS and CNS pathways. The Kupfermann–Kandel model is inconsistent with the available data and therefore stands rejected. There is, no known causal connection or correlation between the observed plasticity at the identified synapses in this system and behavioural changes during non-associative and associative learning paradigms.

Critical examination of these well-studied central pathways suggests that they represent a ‘wetware’ neural network, architecturally similar to the neural network models of the widely used ‘Perceptron’ and/or ‘Back-propagation’ type. Such models may offer a more biologically realistic representation of nervous system organisation than has been thought. In this model, the six parallel GMNs of the CNS correspond to a hidden layer within one module of the gill-control system. That is, the gill-control system appears to be organised as a distributed system with several parallel modules, some of which are neural networks in their own right. A new model is presented here which predicts that the six GMNs serve as components of a ‘push-pull’ gain control system, along with known but largely unidentified inhibitory motor neurons from the PVG. This ‘push-pull’ gain control system sets the responsiveness of the peripheral gill motor system. Neither causal nor correlational links between specific forms of neural plasticity and behavioural plasticity have been demonstrated in the GWR model system. However, the GWR model system does provide an opportunity to observe and describe directly the physiological and biochemical mechanisms of distributed representation and parallel processing in a largely identifiable ‘wetware’ neural network.

How should biological behaviour be modelled? A relatively new approach is to investigate problems in neuroethology by building physical robot models of biological sensorimotor systems. The explication and justification of this approach are here placed within a framework for describing and comparing models in the behavioural and biological sciences. First, simulation models – the representation of a hypothesis about a target system – are distinguished from several other relationships also termed “modelling” in discussions of scientific explanation. Seven dimensions on which simulation models can differ are defined and distinctions between them discussed:

1. Relevance: whether the model tests and generates hypotheses applicable to biology.

2. Level: the elemental units of the model in the hierarchy from atoms to societies.

3. Generality: the range of biological systems the model can represent.

4. Abstraction: the complexity, relative to the target, or amount of detail included in the model.

5. Structural accuracy: how well the model represents the actual mechanisms underlying the behaviour.

6. Performance match: to what extent the model behaviour matches the target behaviour.

7. Medium: the physical basis by which the model is implemented.

No specific position in the space of models thus defined is the only correct one, but a good modelling methodology should be explicit about its position and the justification for that position. It is argued that in building robot models biological relevance is more effective than loose biological inspiration; multiple levels can be integrated; that generality cannot be assumed but might emerge from studying specific instances; abstraction is better done by simplification than idealisation; accuracy can be approached through iterations of complete systems; that the model should be able to match and predict target behaviour; and that a physical medium can have significant advantages. These arguments reflect the view that biological behaviour needs to be studied and modelled in context, that is, in terms of the real problems faced by real animals in real environments.

Neuroethological investigations of mammalian and avian auditory systems have documented species-specific specializations for processing complex acoustic signals that could, if viewed in abstract terms, have an intriguing and striking relevance for human speech sound categorization and representation. Each species forms biologically relevant categories based on combinatorial analysis of information-bearing parameters within the complex input signal. This target article uses known neural models from the mustached bat and barn owl to develop, by analogy, a conceptualization of human processing of consonant plus vowel sequences that offers a partial solution to the noninvariance dilemma – the nontransparent relationship between the acoustic waveform and the phonetic segment. Critical input sound parameters used to establish species-specific categories in the mustached bat and barn owl exhibit high correlation and linearity due to physical laws. A cue long known to be relevant to the perception of stop place of articulation is the second formant (F2) transition. This article describes an empirical phenomenon – the locus equations – that describes the relationship between the F2 of a vowel and the F2 measured at the onset of a consonant-vowel (CV) transition. These variables, F2 onset and F2 vowel within a given place category, are consistently and robustly linearly correlated across diverse speakers and languages, and even under perturbation conditions as imposed by bite blocks. A functional role for this category-level extreme correlation and linearity (the “orderly output constraint”) is hypothesized based on the notion of an evolutionarily conserved auditory-processing strategy. High correlation and linearity between critical parameters in the speech signal that help to cue place of articulation categories might have evolved to satisfy a preadaptation by mammalian auditory systems for representing tightly correlated, linearly related components of acoustic signals.