Co-evolution of human consciousness and language.

This article recalls Cajal's brief mention of consciousness in the Textura as a function of the human brain quite distinct from reflex action, and discusses the view that human consciousness may share aspects of "animal awareness" with other species, but has its unique form because humans possess language. Three ingredients of a theory of the evolution of human consciousness are offered: the view that a précis of intended activity is necessarily formed in the brain of a human that communicates in a human way; the notion that such a précis constitutes consciousness; and a new theory of the evolution of human language based on the mirror system of monkeys and the role of communication by means of hand gestures as a stepping-stone to speech.

Modeling functions of striatal dopamine modulation in learning and planning.

The activity of midbrain dopamine neurons is strikingly similar to the reward prediction error of temporal difference reinforcement learning models. Experimental evidence and simulation studies suggest that dopamine neuron activity serves as an effective reinforcement signal for learning of sensorimotor associations in striatal matrisomes. In the current study, we simulate dopamine neuron activity with the extended temporal difference model of Pavlovian learning and examine the influences of this signal on medium spiny neurons in striatal matrisomes. The modeled influences include transient membrane effects of dopamine D(1) receptor activation, dopamine-dependent long-term adaptations of corticostriatal transmission, and effects of dopamine on rhythmic fluctuations of the membrane potential between an elevated "up-state" and a hyperpolarized "down-state". The most dominant activity in the striatal matrisomes is assumed to elicit behaviors via projections from the basal ganglia to the thalamus and the cortex. This "standard model" performs successfully when tested for sensorimotor learning and goal-directed behavior (planning). To investigate the contributions of our model assumptions to learning and planning, we test the performance of several model variants that lack one of these mechanisms. These simulations show that the adaptation of the dopamine-like signal is necessary for sensorimotor learning and planning. Sensorimotor learning requires dopamine-dependent long-term adaptation of corticostriatal transmission. Lack of dopamine-like novelty responses decreases the number of exploratory acts, which impairs planning capabilities. The model loses its planning capabilities if the dopamine-like signal is simulated with the original temporal difference model, because the original temporal difference model does not form novel associative chains. Transient membrane effects of the dopamine-like signal on striatal firing substantially shorten the reaction time in the planning task. The capability for planning is improved by influences of dopamine on the durations of membrane potential fluctuations and by manipulations that prolong the reaction time of the model. These results suggest that responses of dopamine neurons to conditioned stimuli contribute to sensorimotor reward learning, novelty responses of dopamine neurons stimulate exploration, and transient dopamine membrane effects are important for planning.

Competitive Hebbian learning and the hippocampal place cell system: modeling the interaction of visual and path integration cues.

The hippocampus has long been thought essential for implementing a cognitive map of the environment. However, almost 30 years since place cells were found in rodent hippocampal field CA1, it is still unclear how such an allocentric representation arises from an ego-centrically perceived world. By means of a competitive Hebbian learning rule responsible for coding visual and path integration cues, our model is able to explain the diversity of place cell responses observed in a large set of electrophysiological experiments with a single fixed set of parameters. Experiments included changes observed in place fields due to exploration of a new environment, darkness, retrosplenial cortex inactivation, and removal, rotation, and permutation of landmarks. To code for visual cues for each landmark, we defined two perceptual schemas representing landmark bearing and distance information over a linear array of cells. The information conveyed by the perceptual schemas is further processed through a network of adaptive layers which ultimately modulate the resulting activity of our simulated place cells. In path integration terms, our system is able to dynamically remap a bump of activity coding for the displacement of the animal in relation to an environmental anchor. We hypothesize that path integration information is computed in the rodent posterior parietal cortex and conveyed to the hippocampus where, together with visual information, it modulates place cell activity. The resulting network yields a more direct treatment of partial remapping of place fields than other models. In so doing, it makes new predictions regarding the nature of the interaction between visual and path integration cues during new learning and when the system is challenged with environmental changes.

Cerebellar learning of accurate predictive control for fast-reaching movements.

Long conduction delays in the nervous system prevent the accurate control of movements by feedback control alone. We present a new, biologically plausible cerebellar model to study how fast arm movements can be executed in spite of these delays. To provide a realistic test-bed of the cerebellar neural model, we embed the cerebellar network in a simulated biological motor system comprising a spinal cord model and a six-muscle two-dimensional arm model. We argue that if the trajectory errors are detected at the spinal cord level, memory traces in the cerebellum can solve the temporal mismatch problem between efferent motor commands and delayed error signals. Moreover, learning is made stable by the inclusion of the cerebello-nucleo-olivary loop in the model. It is shown that the cerebellar network implements a nonlinear predictive regulator by learning part of the inverse dynamics of the plant and spinal circuit. After learning, fast accurate reaching movements can be generated.

Synthetic brain imaging: grasping, mirror neurons and imitation.

The article contributes to the quest to relate global data on brain and behavior (e.g. from PET, Positron Emission Tomography, and fMRI. functional Magnetic Resonance Imaging) to the underpinning neural networks. Models tied to human brain imaging data often focus on a few "boxes" based on brain regions associated with exceptionally high blood flow, rather than analyzing the cooperative computation of multiple brain regions. For analysis directly at the level of such data, a schema-based model may be most appropriate. To further address neurophysiological data, the Synthetic PET imaging method uses computational models of biological neural circuitry based on animal data to predict and analyze the results of human PET studies. This technique makes use of the hypothesis that rCBF (regional cerebral blood flow) is correlated with the integrated synaptic activity in a localized brain region. We also describe the possible extension of the Synthetic PET method to fMRI. The second half of the paper then exemplifies this general research program with two case studies, one on visuo-motor processing for control of grasping (Section 3 in which the focus is on Synthetic PET) and the imitation of motor skills (Sections 4 and 5, with a focus on Synthetic fMRI). Our discussion of imitation pays particular attention to data on the mirror system in monkey (neural circuitry which allows the brain to recognize actions as well as execute them). Finally, Section 6 outlines the immense challenges in integrating models of different portions of the nervous system which address detailed neurophysiological data from studies of primates and other species; summarizes key issues for developing the methodology of Synthetic Brain Imaging; and shows how comparative neuroscience and evolutionary arguments will allow us to extend Synthetic Brain Imaging even to language and other cognitive functions for which few or no animal data are available.

Précis of Neural organization: structure, function, and dynamics.

NEURAL ORGANIZATION: Structure, function, and dynamics shows how theory and experiment can supplement each other in an integrated, evolving account of the brain's structure, function, and dynamics. (1) STRUCTURE: Studies of brain function and dynamics build on and contribute to an understanding of many brain regions, the neural circuits that constitute them, and their spatial relations. We emphasize Szentágothai's modular architectonics principle, but also stress the importance of the microcomplexes of cerebellar circuitry and the lamellae of hippocampus. (2) FUNCTION: Control of eye movements, reaching and grasping, cognitive maps, and the roles of vision receive a functional decomposition in terms of schemas. Hypotheses as to how each schema is implemented through the interaction of specific brain regions provide the basis for modeling the overall function by neural networks constrained by neural data. Synthetic PET integrates modeling of primate circuitry with data from human brain imaging. (3) DYNAMICS: Dynamic system theory analyzes spatiotemporal neural phenomena, such as oscillatory and chaotic activity in both single neurons and (often synchronized) neural networks, the self-organizing development and plasticity of ordered neural structures, and learning and memory phenomena associated with synaptic modification. Rhythm generation involves multiple levels of analysis, from intrinsic cellular processes to loops involving multiple brain regions. A variety of rhythms are related to memory functions. The Précis presents a multifaceted case study of the hippocampus. We conclude with the claim that language and other cognitive processes can be fruitfully studied within the framework of neural organization that the authors have charted with John Szentágothai.

Role of the cerebellum in reaching movements in humans. I. Distributed inverse dynamics control.

This study focuses on the role of the motor cortex, the spinal cord and the cerebellum in the dynamics stage of the control of arm movement. Currently, two classes of models have been proposed for the neural control of movements, namely the virtual trajectory control hypothesis and the acquisition of internal models of the motor apparatus hypothesis. In the present study, we expand the virtual trajectory model to whole arm reaching movements. This expanded model accurately reproduced slow movements, but faster reaching movements deviated significantly from the planned trajectories, indicating that for fast movements, this model was not sufficient. These results led us to propose a new distributed functional model consistent with behavioural, anatomical and neurophysiological data, which takes into account arm muscles, spinal cord, motor cortex and cerebellum and is consistent with the view that the central nervous system acquires a distributed inverse dynamics model of the arm. Previous studies indicated that the cerebellum compensates for the interaction forces that arise during reaching movements. We show here how the cerebellum may increase the accuracy of reaching movements by compensating for the interaction torques by learning a portion of an inverse dynamics model that refines a basic inverse model in the motor cortex and spinal cord.

Role of the cerebellum in reaching movements in humans. II. A neural model of the intermediate cerebellum.

The cerebellum is essential for the control of multijoint movements; when the cerebellum is lesioned, the performance error is more than the summed errors produced by single joints. In the companion paper (Schweighofer et al., 1998), a functional anatomical model for visually guided arm movement was proposed. The model comprised a basic feedforward/feedback controller with realistic transmission delays and was connected to a two-link, six-muscle, planar arm. In the present study, we examined the role of the cerebellum in reaching movements by embedding a novel, detailed cerebellar neural network in this functional control model. We could derive realistic cerebellar inputs and the role of the cerebellum in learning to control the arm was assessed. This cerebellar network learned the part of the inverse dynamics of the arm not provided by the basic feedforward/feedback controller. Despite realistically low inferior olive firing rates and noisy mossy fibre inputs, the model could reduce the error between intended and planned movements. The responses of the different cell groups were comparable to those of biological cell groups. In particular, the modelled Purkinje cells exhibited directional tuning after learning and the parallel fibres, due to their length, provide Purkinje cells with the input required for this coordination task. The inferior olive responses contained two different components; the earlier response, locked to movement onset, was always present and the later response disappeared after learning. These results support the theory that the cerebellum is involved in motor learning.

Language within our grasp.

In monkeys, the rostral part of ventral premotor cortex (area F5) contains neurons that discharge, both when the monkey grasps or manipulates objects and when it observes the experimenter making similar actions. These neurons (mirror neurons) appear to represent a system that matches observed events to similar, internally generated actions, and in this way forms a link between the observer and the actor. Transcranial magnetic stimulation and positron emission tomography (PET) experiments suggest that a mirror system for gesture recognition also exists in humans and includes Broca's area. We propose here that such an observation/execution matching system provides a necessary bridge from'doing' to'communicating',as the link between actor and observer becomes a link between the sender and the receiver of each message.

Dorsal premotor cortex and conditional movement selection: A PET functional mapping study.

Positron emission tomography (PET) brain mapping was used to investigate whether or not human dorsal premotor cortex is involved in selecting motor acts based on arbitrary visual stimuli. Normal subjects performed four movement selection tasks. A manipulandum with three graspable stations was used. An imperative visual cue (LEDs illuminated in random order) indicated which station to grasp next with no instructional delay period. In a power task, a large aperture power grip was used for all trials, irrespective of the LED color. In a precision task, a pincer grasp of thumb and index finger was used. In a conditional task, the type of grasp (power or precision) was randomly determined by LED color. Comparison of the conditional selection task versus the average of the power and precision tasks revealed increased blood flow in left dorsal premotor cortex and superior parietal lobule. The average rate of producing the different grasp types and transport to the manipulandum stations was equivalent across this comparison, minimizing the contribution of movement attributes such as planning the individual movements (as distinct from planning associated with use of instructional stimuli), kinematics, or direction of target or limb movement. A comparison of all three movement tasks versus a rest task identified movement related activity involving a large area of central, precentral and postcentral cortex. In the region of the precentral sulcus movement related activity was located immediately caudal to the area activated during selection. The results establish a role for human dorsal premotor cortex and superior parietal cortex in selecting stimulus guided movements and suggest functional segregation within dorsal premotor cortex.

Modeling the physiological responses of anuran R3 ganglion cells.

Teeters and Arbib (Bio Cybernet 1991;64:197-207) presented a model of the anuran retina which qualitatively accounts for some of the characteristic response properties used to distinguish ganglion cell type in anurans. Teeters et al. (Vis Res 1993;33:2361-2379) tested the model's ability to reproduce data of Ewert and Hock (Exp Brain Res 1972;16:41-59) relating toad R2, R3 and R4 ganglion cell responses to moving worm, antiworm and square-shaped stimuli of various edge lengths for stimulus shape and size dependency. In this paper we provide an exhaustive analysis of the performance of the modeled R3 cells with respect to most of the known qualitative and quantitative physiological properties of natural R3 ganglion cells. We also introduce several relevant predictions of the model relating different responses of R3 cells under the effect of changes in different model components. In some cases the predictions have been tested in neurophysiological experiments.

A model of cerebellar metaplasticity.

The term "learning rule" in neural network theory usually refers to a rule for the plasticity of a given synapse, whereas metaplasticity involves a "metalearning algorithm" describing higher level control mechanisms for apportioning plasticity across a population of synapses. We propose here that the cerebellar cortex may use metaplasticity, and we demonstrate this by introducing the Cerebellar Adaptive Rate Learning (CARL) algorithm that concentrates learning on those Purkinje cell synapses whose adaptation is most relevant to learning an overall pattern. Our results show that this biologically plausible metalearning algorithm not only improves significantly the learning capability of the cerebellum but is very robust. Finally, we identify several putative neurochemicals that could be involved in a cascade of events leading to adaptive learning rates in Purkinje cell synapses.

Self and society: between God and brain.

Cognitive Neuroscience and Divine Action, 21-27 June 1998, Paserbiec, Poland.

From visual affordances in monkey parietal cortex to hippocampo-parietal interactions underlying rat navigation.

This paper explores the hypothesis that various subregions (but by no means all) of the posterior parietal cortex are specialized to process visual information to extract a variety of affordances for behaviour. Two biologically based models of regions of the posterior parietal cortex of the monkey are introduced. The model of the lateral intraparietal area (LIP) emphasizes its roles in dynamic remapping of the representation of targets during a double saccade task, and in combining stored, updated input with current visual input. The model of the anterior intraparietal area (AIP) addresses parietal-premotor interactions involved in grasping, and analyses the interaction between the AIP and premotor area F5. The model represents the role of other intraparietal areas working in concert with the inferotemporal cortex as well as with corollary discharge from F5 to provide and augment the affordance information in the AIP, and suggests how various constraints may resolve the action opportunities provided by multiple affordances. Finally, a systems-level model of hippocampo parietal interactions underlying rat navigation is developed, motivated by the monkey data used in developing the above two models as well as by data on neurones in the posterior parietal cortex of the monkey that are sensitive to visual motion. The formal similarity between dynamic remapping (primate saccades) and path integration (rat navigation) is noted, and certain available data on rat posterior parietal cortex in terms of affordances for locomotion are explained. The utility of further modelling, linking the World Graph model of cognitive maps for motivated behaviour with hippocampal-parietal interactions involved in navigation, is also suggested. These models demonstrate that posterior parietal cortex is not only itself a network of interacting subsystems, but functions through cooperative computation with many other brain regions.

Colliding saccades evoked by frontal eye field stimulation: artifact or evidence for an oculomotor compensatory mechanism underlying double-step saccades?

What happens when the goal is changed before the movement is executed? Both the double-step and colliding saccade paradigms address this issue as they introduce a discrepancy between the retinal images of targets in space and the commands generated by the oculomotor system necessary to attain those targets. To maintain spatial accuracy under such conditions, transformations must update "retinal error' as eye position changes, and must also accommodate neural transmission delays in the system so that retinal and eye position information are temporally aligned. Different hypotheses have been suggested to account for these phenomena, based on observations of dissociable cortical and subcortical compensatory mechanisms. We now demonstrate how a single compensatory mechanism can be invoked to explain both double-step and colliding saccade paradigm results, based on the use of a damped signal of change in position that is used in both cases to update retinal error and, thereby, account for intervening movements. We conclude that the collision effect is not an artifact, but instead reveals a compensatory mechanism for saccades whose targets appear near the onset of a preceding saccade.

Premotor cortex activation during observation and naming of familiar tools.

Positron emission tomography was used to investigate whether observation of real objects (tools of common use) activates premotor areas in the absence of any overt motor demand. Silent naming of the presented tools and silent naming of their use were also studied. Right-handed normal subjects were employed. Tool observation strongly activated the left dorsal premotor cortex. In contrast, silent tool naming activated Broca's area without additional activity in the dorsal premotor cortex. Silent tool-use naming, in addition to activating Broca's area, increased the activity in the left dorsal premotor cortex and recruited the left ventral premotor cortex and the left supplementary motor area. These data indicate that, even in the absence of any subsequent movement, the left premotor cortex processes objects that, like tools, have a motor valence. This dorsal premotor activation, which further augments when the subject names the tool use, should reflect the neural activity related to motor schemata for object use. The presence of an activation of both dorsal premotor cortex and ventral premotor cortex during tool-use naming suggests a role for these two areas in understanding object semantics.

Localization of grasp representations in humans by positron emission tomography. 2. Observation compared with imagination.

Positron emission tomography imaging of cerebral blood flow was used to localize brain areas involved in the representation of hand grasping movements. Seven normal subjects were scanned under three conditions. In the first, they observed precision grasping of common objects performed by the examiner. In the second, they imagined themselves grasping the objects without actually moving the hand. These two tasks were compared with a control task of object viewing. Grasp observation activated the left rostral superior temporal sulcus, left inferior frontal cortex (area 45), left rostral inferior parietal cortex (area 40), the rostral part of left supplementary motor area (SMA-proper), and the right dorsal premotor cortex. Imagined grasping activated the left inferior frontal (area 44) and middle frontal cortex, left caudal inferior parietal cortex (area 40), a more extensive response in left rostral SMA-proper, and left dorsal premotor cortex. The two conditions activated different areas of the right posterior cerebellar cortex. We propose that the areas active during grasping observation may form a circuit for recognition of hand-object interactions, whereas the areas active during imagined grasping may be a putative human homologue of a circuit for hand grasping movements recently defined in nonhuman primates. The location of responses in SMA-proper confirms the rostrocaudal segregation of this area for imagined and real movement. A similar segregation is also present in the cerebellum, with imagined and observed grasping movements activating different parts of the posterior lobe and real movements activating the anterior lobe.

A model of the cerebellum in adaptive control of saccadic gain. I. The model and its biological substrate.

We review data showing that the cerebellum is required for adaptation of saccadic gain to repeated presentations of dual-step visual targets and thus, presumably, for providing adaptive corrections for the brainstem saccade generator in response to any error created by the open-loop saccadic system. We model the adaptability of the system in terms of plasticity of synapses from parallel fibers to Purkinje cells in cerebellar cortex, stressing the integration of cerebellar cortex and nuclei in microzones as the units for correction of motor pattern generators. We propose a model of the inferior olive as an error detector, and use a 'window of eligibility' to insure that error signals that elicit a corrective movement are used to adjust the original movement, not the secondary movement. In a companion paper we simulate this large, realistic network of neural-like units to study the complex spatiotemporal behavior of neuronal subpopulations implicated in the control and adaptation of saccades.

A model of the cerebellum in adaptive control of saccadic gain. II. Simulation results.

A large, realistic cerebellar neural network has been incorporated into a previously developed saccade model. Using this model, in the present paper, we simulate the complex spatiotemporal behavior of the neuronal subpopulations implicated in adaptive saccadic control. Our simulation results are in good agreement with neurophysiological and behavioral data. Furthermore, we suggest several new experiments to test the validity of our predictions on adaptive saccadic control.