The gustatory cortex and multisensory integration.

The central gustatory pathways are part of the brain circuits upon which rest the decision to ingest or reject a food. The quality of food stimuli, however, relies not only on their taste but also on properties such as odor, texture and temperature. We will review anatomical and functional evidence showing that the central gustatory system, in particular its cortical aspect, functions as an integrative circuit in which taste-responsive neurons also show sensitivity to somatosensory and olfactory stimulation. In addition, gustatory pathways are modulated by the internal state of the body, with neuronal responses to tastes changing according to variations in physiological parameters such as gastrointestinal hormones or blood glucose levels. Therefore, rather than working as the receptive field of peripheral taste receptor cells, the central gustatory pathways seem to operate as a multisensory system dedicated to evaluating the biological significance of intra-oral stimuli.

Multisensory Processing of Gustatory Stimuli.

Gustatory perception is inherently multimodal, since approximately the same time that intra-oral stimuli activate taste receptors, somatosensory information is concurrently sent to the CNS. We review evidence that gustatory perception is intrinsically linked to concurrent somatosensory processing. We will show that processing of multisensory information can occur at the level of the taste cells through to the gustatory cortex. We will also focus on the fact that the same chemical and physical stimuli that activate the taste system also activate the somatosensory system (SS), but they may provide different types of information to guide behavior.

Dopamine levels modulate the updating of tastant values.

To survive, animals must constantly update the internal value of stimuli they encounter; a process referred to as incentive learning. Although there have been many studies investigating whether dopamine is necessary for reward, or for the association between stimuli and actions with rewards, less is known about the role of dopamine in the updating of the internal value of stimuli per se. We used a single-bottle forced-choice task to investigate the role of dopamine in learning the value of tastants. We show that dopamine transporter knock-out mice (DAT-KO), which have constitutively elevated dopamine levels, develop a more positive bias towards a hedonically positive tastant (sucrose 400 mM) than their wild-type littermates. Furthermore, when compared to wild-type littermates, DAT-KO mice develop a less negative bias towards a hedonically negative tastant (quinine HCl 10 mM). Importantly, these effects develop with training, because at the onset of training DAT-KO and wild-type mice display similar biases towards sucrose and quinine. These data suggest that dopamine levels can modulate the updating of tastant values, a finding with implications for understanding sensory-specific motivation and reward seeking.

Representation of umami taste in the human brain.

Umami taste stimuli, of which an exemplar is monosodium glutamate (MSG) and which capture what is described as the taste of protein, were shown using functional MRI (fMRI) to activate similar cortical regions of the human taste system to those activated by a prototypical taste stimulus, glucose. These taste regions included the insular/opercular cortex and the caudolateral orbitofrontal cortex. A part of the rostral anterior cingulate cortex (ACC) was also activated. When the nucleotide 0.005 M inosine 5'-monophosphate (IMP) was added to MSG (0.05 M), the blood oxygenation-level dependent (BOLD) signal in an anterior part of the orbitofrontal cortex showed supralinear additivity; this may reflect the subjective enhancement of umami taste that has been described when IMP is added to MSG. These results extend to humans previous studies in macaques showing that single neurons in these taste cortical areas can be tuned to umami stimuli.

Self-organizing continuous attractor networks and motor function.

Motor skill learning may involve training a neural system to automatically perform sequences of movements, with the training signals provided by a different system, used mainly during training to perform the movements, that operates under visual sensory guidance. We use a dynamical systems perspective to show how complex motor sequences could be learned by the automatic system. The network uses a continuous attractor network architecture to perform path integration on an efference copy of the motor signal to keep track of the current state, and selection of which motor cells to activate by a movement selector input where the selection depends on the current state being represented in the continuous attractor network. After training, the correct motor sequence may be selected automatically by a single movement selection signal. A feature of the model presented is the use of 'trace' learning rules which incorporate a form of temporal average of recent cell activity. This form of temporal learning underlies the ability of the networks to learn temporal sequences of behaviour. We show that the continuous attractor network models developed here are able to demonstrate the key features of motor function. That is, (i) the movement can occur at arbitrary speeds; (ii) the movement can occur with arbitrary force; (iii) the agent spends the same relative proportions of its time in each part of the motor sequence; (iv) the agent applies the same relative force in each part of the motor sequence; and (v) the actions always occur in the same sequence.

Self-organizing continuous attractor networks and path integration: two-dimensional models of place cells.

Single-neuron recording studies have demonstrated the existence of neurons in the hippocampus which appear to encode information about the place where a rat is located, and about the place at which a macaque is looking. We describe 'continuous attractor' neural network models of place cells with Gaussian spatial fields in which the recurrent collateral synaptic connections between the neurons reflect the distance between two places. The networks maintain a localized packet of neuronal activity that represents the place where the animal is located. We show for two related models how the representation of the two-dimensional space in the continuous attractor network of place cells could self-organize by modifying the synaptic connections between the neurons, and also how the place being represented can be updated by idiothetic (self-motion) signals in a neural implementation of path integration.

Self-organizing continuous attractor networks and path integration: one-dimensional models of head direction cells.

Some neurons encode information about the orientation or position of an animal, and can maintain their response properties in the absence of visual input. Examples include head direction cells in rats and primates, place cells in rats and spatial view cells in primates. 'Continuous attractor' neural networks model these continuous physical spaces by using recurrent collateral connections between the neurons which reflect the distance between the neurons in the state space (e.g. head direction space) of the animal. These networks maintain a localized packet of neuronal activity representing the current state of the animal. We show how the synaptic connections in a one-dimensional continuous attractor network (of for example head direction cells) could be self-organized by associative learning. We also show how the activity packet could be moved from one location to another by idiothetic (self-motion) inputs, for example vestibular or proprioceptive, and how the synaptic connections could self-organize to implement this. The models described use 'trace' associative synaptic learning rules that utilize a form of temporal average of recent cell activity to associate the firing of rotation cells with the recent change in the representation of the head direction in the continuous attractor. We also show how a nonlinear neuronal activation function that could be implemented by NMDA receptors could contribute to the stability of the activity packet that represents the current state of the animal.

A view model which accounts for the spatial fields of hippocampal primate spatial view cells and rat place cells.

Hippocampal spatial view cells found in primates respond to a region of visual space being looked at, relatively independently of where the monkey is located. Rat place cells have responses which depend on where the rat is located. We investigate the hypothesis that in both types of animal, hippocampal cells respond to a combination of visual cues in the correct spatial relation to each other. In rats, which have a wide visual field, such a combination might define a place. In primates, including humans, which have a much smaller visual field and a fovea which is directed towards a part of the environment, the same mechanism might lead to spatial view cells. A computational model in which the neurons become organized by learning to respond to a combination of a small number of visual cues spread within an angle of a 30 degrees receptive field resulted in cells with visual properties like those of primate spatial view cells. The same model, but operating with a receptive field of 270 degrees, produced cells with visual properties like those of rat place cells. Thus a common hippocampal mechanism operating with different visual receptive field sizes could account for some of the visual properties of both place cells in rodents and spatial view cells in primates.