Parallel processing in an identified neural circuit: the Aplysia californica gill-withdrawal response model system

The response of the gill of Aplysia calfornica 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.     

Investigations on the Retroactive Influence of Latent Visual Learning on Previously Formed Memory Traces in Fish1

Experiments with crucians (Carassius carassius L.) and goldfish (Carassius auratus L.) have shown that a consolidated memory trace of a simple visual pattern became disturbed by later seeing series of similar patterns (unrewarded), that is to say by latent learning. In a test in which the experimental group (es) had to choose between the originally learnt and the similar pattern, it did not prefer the former, whereas the control group (cs), which had only seen white walls of the aquarium, still preferred the originally learnt pattern. The same happened when es learnt a similar pattern by reward after the memory of the original pattern had been consolidated. In another set of experiments the negative influence of post-exposure could be demonstrated in facilitation of later learning the postexposed pattern as positive versus the originally learnt pattern, now negative. When es had seen a dissimilar wall-paper pattern then the memory of the primarily learnt pattern was not disturbed. A tentative explanation is based on the notion that two similar engrams have part of the neuronal network in common.     

Exploring Memory Representations with Activity-Based Genetics

The brain is thought to represent specific memories through the activity of sparse and distributed neural ensembles. In this review, we examine the use of immediate early genes (IEGs), genes that are induced by neural activity, to specifically identify and genetically modify neurons activated naturally by environmental experience. Recent studies using this approach have identified cellular and molecular changes specific to neurons activated during learning relative to their inactive neighbors. By using opto- and chemogenetic regulators of neural activity, the neurons naturally recruited during learning can be artificially reactivated to directly test their role in coding external information. In contextual fear conditioning, artificial reactivation of learning-induced neural ensembles in the hippocampus or neocortex can substitute for the context itself. That is, artificial stimulation of these neurons can apparently cause the animals to “think” they are in the context. This represents a powerful approach to testing the principles by which the brain codes for the external world and how these circuits are modified with learning.A central feature of nervous systems is that, to function properly, specific neurons must become active in response to specific stimuli. The nature of this selective activation and its modification with experience is the focus of much neuroscience research, ranging from studies of sensory processing in experimental animals to disorders of thought such as schizophrenia in humans. The central dogma of neuroscience is that perceptions, memories, thoughts, and higher mental functions arise from the pattern and timing of the activity in neural ensembles in specific parts of the brain at specific points in time. Until quite recently, the investigation of these “circuit”-based questions has primarily been limited to observational techniques, such as single unit recording, functional magnetic resonance imagery (fMRI), and calcium imaging, to document the patterns of neural activity evoked by sensory experience or even complex psychological contingencies in human fMRI studies. These techniques have been enormously successful and created a framework for understanding information processing in the brain. For example, recordings in the visual system have indicated that, in the primary visual cortex, neurons are tuned to the orientation of linear stimuli (Hubel and Wiesel 1962). In contrast, neurons in higher brain areas can respond to discrete items. The most striking example of this specificity comes from in vivo recording in the human medial temporal lobe in which single units have been identified that respond to photos of the actress Halle Berry as well as her written name (Quiroga et al. 2005). This highly selective tuning of neural activity is suggestive of function, but how can this be directly tested? What would be the effect of stimulating just this rare population of neurons, a memory of the actress, a sensory illusion of her image? How does this type of specific firing arise? Do these neurons differ from their nonresponsive neighbors in terms of biochemistry, cell biology, or connectivity? Do they undergo molecular alterations when new information is learned about this individual and are these changes required for the learning? These types of questions have recently become accessible to study in mice through the use of activity-based genetic manipulation, in which neurons that are activated by a specific sensory stimulus can be altered to express any gene of experimental interest. These studies and approaches will be the focus of this work.     

A Neurosemantic Theory of Concrete Noun Representation Based on the Underlying Brain Codes

This article describes the discovery of a set of biologically-driven semantic dimensions underlying the neural representation of concrete nouns, and then demonstrates how a resulting theory of noun representation can be used to identify simple thoughts through their fMRI patterns. We use factor analysis of fMRI brain imaging data to reveal the biological representation of individual concrete nouns like apple, in the absence of any pictorial stimuli. From this analysis emerge three main semantic factors underpinning the neural representation of nouns naming physical objects, which we label manipulation, shelter, and eating. Each factor is neurally represented in 3–4 different brain locations that correspond to a cortical network that co-activates in non-linguistic tasks, such as tool use pantomime for the manipulation factor. Several converging methods, such as the use of behavioral ratings of word meaning and text corpus characteristics, provide independent evidence of the centrality of these factors to the representations. The factors are then used with machine learning classifier techniques to show that the fMRI-measured brain representation of an individual concrete noun like apple can be identified with good accuracy from among 60 candidate words, using only the fMRI activity in the 16 locations associated with these factors. To further demonstrate the generativity of the proposed account, a theory-based model is developed to predict the brain activation patterns for words to which the algorithm has not been previously exposed. The methods, findings, and theory constitute a new approach of using brain activity for understanding how object concepts are represented in the mind.     

Embedding Responses in Spontaneous Neural Activity Shaped through Sequential Learning

Recent experimental measurements have demonstrated that spontaneous neural activity in the absence of explicit external stimuli has remarkable spatiotemporal structure. This spontaneous activity has also been shown to play a key role in the response to external stimuli. To better understand this role, we proposed a viewpoint, “memories-as-bifurcations,” that differs from the traditional “memories-as-attractors” viewpoint. Memory recall from the memories-as-bifurcations viewpoint occurs when the spontaneous neural activity is changed to an appropriate output activity upon application of an input, known as a bifurcation in dynamical systems theory, wherein the input modifies the flow structure of the neural dynamics. Learning, then, is a process that helps create neural dynamical systems such that a target output pattern is generated as an attractor upon a given input. Based on this novel viewpoint, we introduce in this paper an associative memory model with a sequential learning process. Using a simple Hebbian-type learning, the model is able to memorize a large number of input/output mappings. The neural dynamics shaped through the learning exhibit different bifurcations to make the requested targets stable upon an increase in the input, and the neural activity in the absence of input shows chaotic dynamics with occasional approaches to the memorized target patterns. These results suggest that these dynamics facilitate the bifurcations to each target attractor upon application of the corresponding input, which thus increases the capacity for learning. This theoretical finding about the behavior of the spontaneous neural activity is consistent with recent experimental observations in which the neural activity without stimuli wanders among patterns evoked by previously applied signals. In addition, the neural networks shaped by learning properly reflect the correlations of input and target-output patterns in a similar manner to those designed in our previous study.     

Dynamic Changes in Single Unit Activity and Gamma Oscillations in a Thalamocortical Circuit during Rapid Instrumental Learning

The medial prefrontal cortex (mPFC) and mediodorsal thalamus (MD) together form a thalamocortical circuit that has been implicated in the learning and production of goal-directed actions. In this study we measured neural activity in both regions simultaneously, as rats learned to press a lever to earn food rewards. In both MD and mPFC, instrumental learning was accompanied by dramatic changes in the firing patterns of the neurons, in particular the rapid emergence of single-unit neural activity reflecting the completion of the action and reward delivery. In addition, we observed distinct patterns of changes in the oscillatory LFP response in MD and mPFC. With learning, there was a significant increase in theta band oscillations (6–10 Hz) in the MD, but not in the mPFC. By contrast, gamma band oscillations (40–55 Hz) increased in the mPFC, but not in the MD. Coherence between these two regions also changed with learning: gamma coherence in relation to reward delivery increased, whereas theta coherence did not. Together these results suggest that, as rats learned the instrumental contingency between action and outcome, the emergence of task related neural activity is accompanied by enhanced functional interaction between MD and mPFC in response to the reward feedback.     

Sequence learning recodes cortical representations instead of strengthening initial ones

We contrast two computational models of sequence learning. The associative learner posits that learning proceeds by strengthening existing association weights. Alternatively, recoding posits that learning creates new and more efficient representations of the learned sequences. Importantly, both models propose that humans act as optimal learners but capture different statistics of the stimuli in their internal model. Furthermore, these models make dissociable predictions as to how learning changes the neural representation of sequences. We tested these predictions by using fMRI to extract neural activity patterns from the dorsal visual processing stream during a sequence recall task. We observed that only the recoding account can explain the similarity of neural activity patterns, suggesting that participants recode the learned sequences using chunks. We show that associative learning can theoretically store only very limited number of overlapping sequences, such as common in ecological working memory tasks, and hence an efficient learner should recode initial sequence representations.     

Fronto-temporal brain systems supporting spoken language comprehension

The research described here combines psycholinguistically well-motivated questions about different aspects of human language comprehension with behavioural and neuroimaging studies of normal performance, incorporating both subtractive analysis techniques and functional connectivity methods, and applying these tasks and techniques to the analysis of the functional and neural properties of brain-damaged patients with selective linguistic deficits in the relevant domains. The results of these investigations point to a set of partially dissociable sub-systems supporting three major aspects of spoken language comprehension, involving regular inflectional morphology, sentence-level syntactic analysis and sentence-level semantic interpretation. Differential patterns of fronto-temporal connectivity for these three domains confirm that the core aspects of language processing are carried out in a fronto-temporo-parietal language system which is modulated in different ways as a function of different linguistic processing requirements. No one region or sub-region holds the key to a specific language function; each requires the coordination of activity within a number of different regions. Functional connectivity analysis plays the critical role of indicating the regions which directly participate in a given sub-process, by virtue of their joint time-dependent activity. By revealing these codependencies, connectivity analysis sharpens the pattern of structure-function relations underlying specific aspects of language performance.     

Identifying Autism from Neural Representations of Social Interactions: Neurocognitive Markers of Autism

Autism is a psychiatric/neurological condition in which alterations in social interaction (among other symptoms) are diagnosed by behavioral psychiatric methods. The main goal of this study was to determine how the neural representations and meanings of social concepts (such as to insult) are altered in autism. A second goal was to determine whether these alterations can serve as neurocognitive markers of autism. The approach is based on previous advances in fMRI analysis methods that permit (a) the identification of a concept, such as the thought of a physical object, from its fMRI pattern, and (b) the ability to assess the semantic content of a concept from its fMRI pattern. These factor analysis and machine learning methods were applied to the fMRI activation patterns of 17 adults with high-functioning autism and matched controls, scanned while thinking about 16 social interactions. One prominent neural representation factor that emerged (manifested mainly in posterior midline regions) was related to self-representation, but this factor was present only for the control participants, and was near-absent in the autism group. Moreover, machine learning algorithms classified individuals as autistic or control with 97% accuracy from their fMRI neurocognitive markers. The findings suggest that psychiatric alterations of thought can begin to be biologically understood by assessing the form and content of the altered thought’s underlying brain activation patterns.     

Beyond Motor Scheme: A Supramodal Distributed Representation in the Action-Observation Network

The representation of actions within the action-observation network is thought to rely on a distributed functional organization. Furthermore, recent findings indicate that the action-observation network encodes not merely the observed motor act, but rather a representation that is independent from a specific sensory modality or sensory experience. In the present study, we wished to determine to what extent this distributed and ‘more abstract’ representation of action is truly supramodal, i.e. shares a common coding across sensory modalities. To this aim, a pattern recognition approach was employed to analyze neural responses in sighted and congenitally blind subjects during visual and/or auditory presentation of hand-made actions. Multivoxel pattern analyses-based classifiers discriminated action from non-action stimuli across sensory conditions (visual and auditory) and experimental groups (blind and sighted). Moreover, these classifiers labeled as ‘action’ the pattern of neural responses evoked during actual motor execution. Interestingly, discriminative information for the action/non action classification was located in a bilateral, but left-prevalent, network that strongly overlaps with brain regions known to form the action-observation network and the human mirror system. The ability to identify action features with a multivoxel pattern analyses-based classifier in both sighted and blind individuals and independently from the sensory modality conveying the stimuli clearly supports the hypothesis of a supramodal, distributed functional representation of actions, mainly within the action-observation network.     

Behavioral specialization of cortical and hypothalamic neurons in the rabbit

Behavioural specialization was analyzed of hypothalamic and limbic neurones, with their activity recorded in rabbits during food-acquisition behaviour. The neurones with activity changed during staying of the animal in a definite place of the cage or during behavioural acts, characteristic of a specific behaviour in the cage, are considered as specialized in relation to the most "new" systems, acquired by the rabbit directly during learning of the given behaviour. Neurones with the activity changed with rabbit's turns, i.e. connected with behavioural acts, which the rabbit has not specially learnt, are considered specialized in relation to more "old" inborn systems. Neurones, in which no constant connection with any part of the studied behaviour was observed, are related to the most "ancient" systems. Comparison of the number of hypothalamic and limbic neurones of different groups showed that in the cortex there were some more neurones specialized in relation to behavioural acts, which were formed directly during learning of the rabbit in the experimental cage.     

A neural network model of the cerebellar cortex performing dynamic associations

The present paper proposes a model which applies formal neural network modeling techniques to construct a theoretical representation of the cerebellar cortex and its performances in motor control. A schema that makes explicit use of propagation delays of neural signals, is introduced to describe the ability to store temporal sequences of patterns in the Golgi-granule cell system. A perceptron association is then performed on these sequences of patterns by the Purkinje cell layer. The model conforms with important biological constraints, such as the known excitatory or inhibitory nature of the various synapses. Also, as suggested by experimental evidence, the synaptic plasticity underlying the learning ability of the model, is confined to the parallel fiber — Purkinje cell synapses, and takes place under the control of the climbing fibers. The result is a neural network model, constructed according to the anatomy of the cerebellar cortex, and capable of learning and retrieval of temporal sequences of patterns. It provides a framework to represent and interpret properties of learning and control of movements by the cerebellum, and to assess the capacity of formal neural network techniques for modeling of real neural systems.     

Human Decision Making Based on Variations in Internal Noise: An EEG Study

Perceptual decision making is prone to errors, especially near threshold. Physiological, behavioural and modeling studies suggest this is due to the intrinsic or ‘internal’ noise in neural systems, which derives from a mixture of bottom-up and top-down sources. We show here that internal noise can form the basis of perceptual decision making when the external signal lacks the required information for the decision. We recorded electroencephalographic (EEG) activity in listeners attempting to discriminate between identical tones. Since the acoustic signal was constant, bottom-up and top-down influences were under experimental control. We found that early cortical responses to the identical stimuli varied in global field power and topography according to the perceptual decision made, and activity preceding stimulus presentation could predict both later activity and behavioural decision. Our results suggest that activity variations induced by internal noise of both sensory and cognitive origin are sufficient to drive discrimination judgments.     

Selective and Efficient Neural Coding of Communication Signals Depends on Early Acoustic and Social Environment

Previous research has shown that postnatal exposure to simple, synthetic sounds can affect the sound representation in the auditory cortex as reflected by changes in the tonotopic map or other relatively simple tuning properties, such as AM tuning. However, their functional implications for neural processing in the generation of ethologically-based perception remain unexplored. Here we examined the effects of noise-rearing and social isolation on the neural processing of communication sounds such as species-specific song, in the primary auditory cortex analog of adult zebra finches. Our electrophysiological recordings reveal that neural tuning to simple frequency-based synthetic sounds is initially established in all the laminae independent of patterned acoustic experience; however, we provide the first evidence that early exposure to patterned sound statistics, such as those found in native sounds, is required for the subsequent emergence of neural selectivity for complex vocalizations and for shaping neural spiking precision in superficial and deep cortical laminae, and for creating efficient neural representations of song and a less redundant ensemble code in all the laminae. Our study also provides the first causal evidence for ‘sparse coding’, such that when the statistics of the stimuli were changed during rearing, as in noise-rearing, that the sparse or optimal representation for species-specific vocalizations disappeared. Taken together, these results imply that a layer-specific differential development of the auditory cortex requires patterned acoustic input, and a specialized and robust sensory representation of complex communication sounds in the auditory cortex requires a rich acoustic and social environment.     

Manipulating neural activity in physiologically classified neurons: triumphs and challenges

Understanding brain function requires knowing both how neural activity encodes information and how this activity generates appropriate responses. Electrophysiological, imaging and immediate early gene immunostaining studies have been instrumental in identifying and characterizing neurons that respond to different sensory stimuli, events and motor actions. Here we highlight approaches that have manipulated the activity of physiologically classified neurons to determine their role in the generation of behavioural responses. Previous experiments have often exploited the functional architecture observed in many cortical areas, where clusters of neurons share response properties. However, many brain structures do not exhibit such functional architecture. Instead, neurons with different response properties are anatomically intermingled. Emerging genetic approaches have enabled the identification and manipulation of neurons that respond to specific stimuli despite the lack of discernable anatomical organization. These approaches have advanced understanding of the circuits mediating sensory perception, learning and memory, and the generation of behavioural responses by providing causal evidence linking neural response properties to appropriate behavioural output. However, significant challenges remain for understanding cognitive processes that are probably mediated by neurons with more complex physiological response properties. Currently available strategies may prove inadequate for determining how activity in these neurons is causally related to cognitive behaviour.     

Dissociation between Active and Observational Learning from Positive and Negative Feedback in Parkinsonism

Feedback to both actively performed and observed behaviour allows adaptation of future actions. Positive feedback leads to increased activity of dopamine neurons in the substantia nigra, whereas dopamine neuron activity is decreased following negative feedback. Dopamine level reduction in unmedicated Parkinson’s Disease patients has been shown to lead to a negative learning bias, i.e. enhanced learning from negative feedback. Recent findings suggest that the neural mechanisms of active and observational learning from feedback might differ, with the striatum playing a less prominent role in observational learning. Therefore, it was hypothesized that unmedicated Parkinson’s Disease patients would show a negative learning bias only in active but not in observational learning. In a between-group design, 19 Parkinson’s Disease patients and 40 healthy controls engaged in either an active or an observational probabilistic feedback-learning task. For both tasks, transfer phases aimed to assess the bias to learn better from positive or negative feedback. As expected, actively learning patients showed a negative learning bias, whereas controls learned better from positive feedback. In contrast, no difference between patients and controls emerged for observational learning, with both groups showing better learning from positive feedback. These findings add to neural models of reinforcement-learning by suggesting that dopamine-modulated input to the striatum plays a minor role in observational learning from feedback. Future research will have to elucidate the specific neural underpinnings of observational learning.     

Neural network control systems that learn to perform appropriately

Setting up a neural network with a learning algorithm that determines how it can best operate is an efficient way to formulate control systems for many engineering applications, and is often much more feasible than direct programming. This paper examines three important aspects of this approach: the details of the cost function that is used with the gradient descent learning algorithm, how the resulting system depends on the initial pre-learning connection weights, and how the resulting system depends on the pattern of learning rates chosen for the different components of the system. We explore these issues by explicit simulations of a toy model that is a simplified abstraction of part of the human oculomotor control system. This allows us to compare our system with that produced by human evolution and development. We can then go on to consider how we might improve on the human system and apply what we have learnt to control systems that have no human analogue.     

A Sensitive and Specific Neural Signature for Picture-Induced Negative Affect

Neuroimaging has identified many correlates of emotion but has not yet yielded brain representations predictive of the intensity of emotional experiences in individuals. We used machine learning to identify a sensitive and specific signature of emotional responses to aversive images. This signature predicted the intensity of negative emotion in individual participants in cross validation (n =121) and test (n = 61) samples (high–low emotion = 93.5% accuracy). It was unresponsive to physical pain (emotion–pain = 92% discriminative accuracy), demonstrating that it is not a representation of generalized arousal or salience. The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience. Furthermore, it was not reducible to activity in traditional “emotion-related” regions (e.g., amygdala, insula) or resting-state networks (e.g., “salience,” “default mode”). Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes.     

Behavioural assessment of fetal health

The assessment of behavioural activity of the fetus is widely used to assess fetal health in clinical practice as part of the biophysical profile as well as the nonstress test. Considerable information regarding normal activity patterns of healthy human fetuses have been obtained from 24 to 40 weeks gestation. It is this information which has provided the scientific foundation for the development of fetal assessment protocols. Studies in chronically-catheterized fetal sheep have demonstrated that acute hypoxemia leads to an inhibition of fetal breathing movements although prolonged reductions in oxygen delivery to the fetus in the absence of acidemia are associated with adaptation by the fetus and subsequent return to normal incidence of behavioural activity. The behavioural responses of the fetus to specific stimuli including vibroacoustic stimulation have been examined in relation to gestational age as well as type of stimulus, it is proposed that fetal acoustic stimulation could possibly be used to assess fetal neurological function although prior to it being accepted as an universal method for assessing fetal health it is essential that a greater understanding of the mechanisms involved in fetal responses to sound and vibration be determined using appropriate experimental techniques.     

Moonstruck Primates: Owl Monkeys (Aotus) Need Moonlight for Nocturnal Activity in Their Natural Environment

Primates show activity patterns ranging from nocturnality to diurnality, with a few species showing activity both during day and night. Among anthropoids (monkeys, apes and humans), nocturnality is only present in the Central and South American owl monkey genus Aotus. Unlike other tropical Aotus species, the Azara''s owl monkeys (A. azarai) of the subtropics have switched their activity pattern from strict nocturnality to one that also includes regular diurnal activity. Harsher climate, food availability, and the lack of predators or diurnal competitors, have all been proposed as factors favoring evolutionary switches in primate activity patterns. However, the observational nature of most field studies has limited an understanding of the mechanisms responsible for this switch in activity patterns. The goal of our study was to evaluate the hypothesis that masking, namely the stimulatory and/or inhibitory/disinhibitory effects of environmental factors on synchronized circadian locomotor activity, is a key determinant of the unusual activity pattern of Azara''s owl monkeys. We use continuous long-term (6–18 months) 5-min-binned activity records obtained with actimeter collars fitted to wild owl monkeys (n = 10 individuals) to show that this different pattern results from strong masking of activity by the inhibiting and enhancing effects of ambient luminance and temperature. Conclusive evidence for the direct masking effect of light is provided by data showing that locomotor activity was almost completely inhibited when moonlight was shadowed during three lunar eclipses. Temperature also negatively masked locomotor activity, and this masking was manifested even under optimal light conditions. Our results highlight the importance of the masking of circadian rhythmicity as a determinant of nocturnality in wild owl monkeys and suggest that the stimulatory effects of dim light in nocturnal primates may have been selected as an adaptive response to moonlight. Furthermore, our data indicate that changes in sensitivity to specific environmental stimuli may have been an essential key for evolutionary switches between diurnal and nocturnal habits in primates.