Enhanced affective brain representations of chocolate in cravers vs. non-cravers

To examine the neural circuitry involved in food craving, in making food particularly appetitive and thus in driving wanting and eating, we used fMRI to measure the response to the flavour of chocolate, the sight of chocolate and their combination in cravers vs. non-cravers. Statistical parametric mapping (SPM) analyses showed that the sight of chocolate produced more activation in chocolate cravers than non-cravers in the medial orbitofrontal cortex and ventral striatum. For cravers vs. non-cravers, a combination of a picture of chocolate with chocolate in the mouth produced a greater effect than the sum of the components (i.e. supralinearity) in the medial orbitofrontal cortex and pregenual cingulate cortex. Furthermore, the pleasantness ratings of the chocolate and chocolate-related stimuli had higher positive correlations with the fMRI blood oxygenation level-dependent signals in the pregenual cingulate cortex and medial orbitofrontal cortex in the cravers than in the non-cravers. To our knowledge, this is the first study to show that there are differences between cravers and non-cravers in their responses to the sensory components of a craved food in the orbitofrontal cortex, ventral striatum and pregenual cingulate cortex, and that in some of these regions the differences are related to the subjective pleasantness of the craved foods. Understanding individual differences in brain responses to very pleasant foods helps in the understanding of the mechanisms that drive the liking for specific foods and thus intake of those foods.

Speed, noise, information and the graded nature of neuronal responses

How the firing rate of a neuron carries information depends on the time over which rates are measured. For very short times, the amount of information conveyed depends, in a universal way, on the mean rates only (trial-to-trial variability is irrelevant) and the cell response can be taken to be binary (although an ideal binary response would convey more). For longer times, noise as well as the graded nature of the response come into play, with opposite effects. Which times can be considered 'short' varies with the brain area considered and, possibly, with the processing speed it is required to operate at.

Expected value, reward outcome, and temporal difference error representations in a probabilistic decision task

In probabilistic decision tasks, an expected value (EV) of a choice is calculated, and after the choice has been made, this can be updated based on a temporal difference (TD) prediction error between the EV and the reward magnitude (RM) obtained. The EV is measured as the probability of obtaining a reward x RM. To understand the contribution of different brain areas to these decision-making processes, functional magnetic resonance imaging activations related to EV versus RM (or outcome) were measured in a probabilistic decision task. Activations in the medial orbitofrontal cortex were correlated with both RM and with EV and confirmed in a conjunction analysis to extend toward the pregenual cingulate cortex. From these representations, TD reward prediction errors could be produced. Activations in areas that receive from the orbitofrontal cortex including the ventral striatum, midbrain, and inferior frontal gyrus were correlated with the TD error. Activations in the anterior insula were correlated negatively with EV, occurring when low reward outcomes were expected, and also with the uncertainty of the reward, implicating this region in basic and crucial decision-making parameters, low expected outcomes, and uncertainty.

Neuronal selectivity, population sparseness, and ergodicity in the inferior temporal visual cortex

The sparseness of the encoding of stimuli by single neurons and by populations of neurons is fundamental to understanding the efficiency and capacity of representations in the brain, and was addressed as follows. The selectivity and sparseness of firing to visual stimuli of single neurons in the primate inferior temporal visual cortex were measured to a set of 20 visual stimuli including objects and faces in macaques performing a visual fixation task. Neurons were analysed with significantly different responses to the stimuli. The firing rate distribution of 36% of the neurons was exponential. Twenty-nine percent of the neurons had too few low rates to be fitted by an exponential distribution, and were fitted by a gamma distribution. Interestingly, the raw firing rate distribution taken across all neurons fitted an exponential distribution very closely. The sparseness a (s) or selectivity of the representation of the set of 20 stimuli provided by each of these neurons (which takes a maximal value of 1.0) had an average across all neurons of 0.77, indicating a rather distributed representation. The sparseness of the representation of a given stimulus by the whole population of neurons, the population sparseness a (p), also had an average value of 0.77. The similarity of the average single neuron selectivity a (s) and population sparseness for any one stimulus taken at any one time a (p) shows that the representation is weakly ergodic. For this to occur, the different neurons must have uncorrelated tuning profiles to the set of stimuli.

Invariant object recognition with trace learning and multiple stimuli present during training

Over successive stages, the ventral visual system develops neurons that respond with view, size and position invariance to objects including faces. A major challenge is to explain how invariant representations of individual objects could develop given visual input from environments containing multiple objects. Here we show that the neurons in a 1-layer competitive network learn to represent combinations of three objects simultaneously present during training if the number of objects in the training set is low (e.g. 4), to represent combinations of two objects as the number of objects is increased to for e.g. 10, and to represent individual objects as the number of objects in the training set is increased further to for e.g. 20. We next show that translation invariant representations can be formed even when multiple stimuli are always present during training, by including a temporal trace in the learning rule. Finally, we show that these concepts can be extended to a multi-layer hierarchical network model (VisNet) of the ventral visual system. This approach provides a way to understand how a visual system can, by self-organizing competitive learning, form separate invariant representations of each object even when each object is presented in a scene with multiple other objects present, as in natural visual scenes.

Sensory processing in the brain related to the control of food intake

Complementary neurophysiological recordings in rhesus macaques (Macaca mulatta) and functional neuroimaging in human subjects show that the primary taste cortex in the rostral insula and adjoining frontal operculum provides separate and combined representations of the taste, temperature and texture (including viscosity and fat texture) of food in the mouth independently of hunger and thus of reward value and pleasantness. One synapse on, in the orbitofrontal cortex, these sensory inputs are for some neurons combined by learning with olfactory and visual inputs. Different neurons respond to different combinations, providing a rich representation of the sensory properties of food. In the orbitofrontal cortex feeding to satiety with one food decreases the responses of these neurons to that food, but not to other foods, showing that sensory-specific satiety is computed in the primate (including the human) orbitofrontal cortex. Consistently, activation of parts of the human orbitofrontal cortex correlates with subjective ratings of the pleasantness of the taste and smell of food. Cognitive factors, such as a word label presented with an odour, influence the pleasantness of the odour, and the activation produced by the odour in the orbitofrontal cortex. Food intake is thus controlled by building a multimodal representation of the sensory properties of food in the orbitofrontal cortex and gating this representation by satiety signals to produce a representation of the pleasantness or reward value of food that drives food intake. Factors that lead this system to become unbalanced and contribute to overeating and obesity are described.

Umami: a delicious flavor formed by convergence of taste and olfactory pathways in the human brain

Umami taste is produced by glutamate acting on a fifth taste system. However, glutamate presented alone as a taste stimulus is not highly pleasant, and does not act synergistically with other tastes (sweet, salt, bitter and sour). We show here that when glutamate is given in combination with a consonant, savory, odour (vegetable), the resulting flavor can be much more pleasant. Moreover, we showed using functional brain imaging with fMRI that the glutamate taste and savory odour combination produced much greater activation of the medial orbitofrontal cortex and pregenual cingulate cortex than the sum of the activations by the taste and olfactory components presented separately. Supralinear effects were much less (and significantly less) evident for sodium chloride and vegetable odour. Further, activations in these brain regions were correlated with the pleasantness and fullness of the flavor, and with the consonance of the taste and olfactory components. Supralinear effects of glutamate taste and savory odour were not found in the insular primary taste cortex. We thus propose that glutamate acts by the nonlinear effects it can produce when combined with a consonant odour in multimodal cortical taste-olfactory convergence regions. We propose the concept that umami can be thought of as a rich and delicious flavor that is produced by a combination of glutamate taste and a consonant savory odour. Glutamate is thus a flavor enhancer because of the way that it can combine supralinearly with consonant odours in cortical areas where the taste and olfactory pathways converge far beyond the receptors.

Learning movement sequences with a delayed reward signal in a hierarchical model of motor function

A key problem in reinforcement learning is how an animal is able to learn a sequence of movements when the reward signal only occurs at the end of the sequence. We describe how a hierarchical dynamical model of motor function is able to solve the problem of delayed reward in learning movement sequences using associative (Hebbian) learning. At the lowest level, the motor system encodes simple movements or primitives, while at higher levels the system encodes sequences of primitives. During training, the network is able to learn a high level motor program composed of a specific temporal sequence of motor primitives. The network is able to achieve this despite the fact that the reward signal, which indicates whether or not the desired motor program has been performed correctly, is received only at the end of each trial during learning. Use of a continuous attractor network in the architecture enables the network to generate the motor outputs required to produce the continuous movements necessary to implement the motor sequence.

Brain mechanisms underlying flavour and appetite

Complementary neurophysiological recordings in macaques and functional neuroimaging in humans show that the primary taste cortex in the rostral insula and adjoining frontal operculum provides separate and combined representations of the taste, temperature and texture (including viscosity and fat texture) of food in the mouth independently of hunger and thus of reward value and pleasantness. One synapse on, in the orbitofrontal cortex, these sensory inputs are for some neurons combined by learning with olfactory and visual inputs. Different neurons respond to different combinations, providing a rich representation of the sensory properties of food. In the orbitofrontal cortex, feeding to satiety with one food decreases the responses of these neurons to that food, but not to other foods, showing that sensory-specific satiety is computed in the primate (including human) orbitofrontal cortex. Consistently, activation of parts of the human orbitofrontal cortex correlates with subjective ratings of the pleasantness of the taste and smell of food. Cognitive factors, such as a word label presented with an odour, influence the pleasantness of the odour and the activation produced by the odour in the orbitofrontal cortex. These findings provide a basis for understanding how what is in the mouth is represented by independent information channels in the brain; how the information from these channels is combined; and how and where the reward and subjective affective value of food is represented and is influenced by satiety signals. Activation of these representations in the orbitofrontal cortex may provide the goal for eating, and understanding them helps to provide a basis for understanding appetite and its disorders.

The representation of information about faces in the temporal and frontal lobes

Neurophysiological evidence is described showing that some neurons in the macaque inferior temporal visual cortex have responses that are invariant with respect to the position, size and view of faces and objects, and that these neurons show rapid processing and rapid learning. Which face or object is present is encoded using a distributed representation in which each neuron conveys independent information in its firing rate, with little information evident in the relative time of firing of different neurons. This ensemble encoding has the advantages of maximising the information in the representation useful for discrimination between stimuli using a simple weighted sum of the neuronal firing by the receiving neurons, generalisation and graceful degradation. These invariant representations are ideally suited to provide the inputs to brain regions such as the orbitofrontal cortex and amygdala that learn the reinforcement associations of an individual's face, for then the learning, and the appropriate social and emotional responses, generalise to other views of the same face. A theory is described of how such invariant representations may be produced in a hierarchically organised set of visual cortical areas with convergent connectivity. The theory proposes that neurons in these visual areas use a modified Hebb synaptic modification rule with a short-term memory trace to capture whatever can be captured at each stage that is invariant about objects as the objects change in retinal view, position, size and rotation. Another population of neurons in the cortex in the superior temporal sulcus encodes other aspects of faces such as face expression, eye gaze, face view and whether the head is moving. These neurons thus provide important additional inputs to parts of the brain such as the orbitofrontal cortex and amygdala that are involved in social communication and emotional behaviour. Outputs of these systems reach the amygdala, in which face-selective neurons are found, and also the orbitofrontal cortex, in which some neurons are tuned to face identity and others to face expression. In humans, activation of the orbitofrontal cortex is found when a change of face expression acts as a social signal that behaviour should change; and damage to the orbitofrontal cortex can impair face and voice expression identification, and also the reversal of emotional behaviour that normally occurs when reinforcers are reversed.

Invariant Global Motion Recognition in the Dorsal Visual System: A Unifying Theory

The motion of an object (such as a wheel rotating) is seen as consistent independent of its position and size on the retina. Neurons in higher cortical visual areas respond to these global motion stimuli invariantly, but neurons in early cortical areas with small receptive fields cannot represent this motion, not only because of the aperture problem but also because they do not have invariant representations. In a unifying hypothesis with the design of the ventral cortical visual system, we propose that the dorsal visual system uses a hierarchical feedforward network architecture (V1, V2, MT, MSTd, parietal cortex) with training of the connections with a short-term memory trace associative synaptic modification rule to capture what is invariant at each stage. Simulations show that the proposal is computationally feasible, in that invariant representations of the motion flow fields produced by objects self-organize in the later layers of the architecture. The model produces invariant representations of the motion flow fields produced by global in-plane motion of an object, in-plane rotational motion, looming versus receding of the object, and object-based rotation about a principal axis. Thus, the dorsal and ventral visual systems may share some similar computational principles.

Entorhinal cortex grid cells can map to hippocampal place cells by competitive learning

'Grid cells' in the dorsocaudal medial entorhinal cortex (dMEC) are activated when a rat is located at any of the vertices of a grid of equilateral triangles covering the environment. dMEC grid cells have different frequencies and phase offsets. However, cells in the dentate gyrus (DG) and hippocampal area CA3 of the rodent typically display place fields, where individual cells are active over only a single portion of the space. In a model of the hippocampus, we have shown that the connectivity from the entorhinal cortex to the dentate granule cells could allow the dentate granule cells to operate as a competitive network to recode their inputs to produce sparse orthogonal representations, and this includes spatial pattern separation. In this paper we show that the same computational hypothesis can account for the mapping of EC grid cells to dentate place cells. We show that the learning in the competitive network is an important part of the way in which the mapping can be achieved. We further show that incorporation of a short term memory trace into the associative learning can help to produce the relatively broad place fields found in the hippocampus.

Self-organizing path integration using a linked continuous attractor and competitive network: path integration of head direction

A key issue is how networks in the brain learn to perform path integration, that is update a represented position using a velocity signal. Using head direction cells as an example, we show that a competitive network could self-organize to learn to respond to combinations of head direction and angular head rotation velocity. These combination cells can then be used to drive a continuous attractor network to the next head direction based on the incoming rotation signal. An associative synaptic modification rule with a short term memory trace enables preceding combination cell activity during training to be associated with the next position in the continuous attractor network. The network accounts for the presence of neurons found in the brain that respond to combinations of head direction and angular head rotation velocity. Analogous networks in the hippocampal system could self-organize to perform path integration of place and spatial view representations.

Attention in natural scenes: Neurophysiological and computational bases

How does attention operate in natural scenes? We show that the receptive fields of inferior temporal cortex neurons that implement object representations become small and located at the fovea in complex natural scenes. This facilitates the readout of information about an object that may be reward or punishment associated, and may be the target for action. Top-down biased competition to implement attention has a much weaker effect in complex natural scenes than in otherwise blank scenes with two objects. Part of the solution to the binding problem is thus that competition and the foveal cortical magnification factor emphasize what is present at the fovea, and limit the binding problem. Part of the solution to the binding problem is that neurons respond to combinations of features present in the correct relative spatial positions. Stimulus-dependent neuronal synchrony does not appear to be quantitatively important in feature binding, and in attention, in natural visual scenes, at least in the inferior temporal visual cortex, as shown by information theoretic analyses. The perception of multiple objects in a scene is facilitated by the fact that inferior temporal visual cortex neurons have asymmetrical receptive fields with respect to the fovea in complex scenes. Computational models of this processing are described.

Invariant visual object recognition: a model, with lighting invariance

How are invariant representations of objects formed in the visual cortex? We describe a neurophysiological and computational approach which focusses on a feature hierarchy model in which invariant representations can be built by self-organizing learning based on the statistics of the visual input. The model can use temporal continuity in an associative synaptic learning rule with a short term memory trace, and/or it can use spatial continuity in Continuous Transformation learning. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and in this paper we show also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in for example spatial and object search tasks. The model has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene.

Spatial vs temporal continuity in view invariant visual object recognition learning

We show in a 4-layer competitive neuronal network that continuous transformation learning, which uses spatial correlations and a purely associative (Hebbian) synaptic modification rule, can build view invariant representations of complex 3D objects. This occurs even when views of the different objects are interleaved, a condition where temporal trace learning fails. Human psychophysical experiments showed that view invariant object learning can occur when spatial but not temporal continuity applies because of interleaving of stimuli, although sequential presentation, which produces temporal continuity, can facilitate learning. Thus continuous transformation learning is an important principle that may contribute to view invariant object recognition.

Decision‐making and Weber's law: a neurophysiological model

We describe an integrate-and-fire attractor model of the decision-related activity of ventral premotor cortex (VPC) neurons during a vibrotactile frequency comparison task [Romo et al. (2004)Neuron, 41, 165-173]. Populations of neurons for each decision in a biased competition attractor network receive a bias input that depends on the firing rates of VPC neurons that code for the two vibrotactile frequencies. The firing rate of the neurons in whichever attractor wins, reflects the sign of the difference in the frequencies (Deltaf) being compared but not the absolute frequencies. However, the transition from the initial spontaneous firing state to one of the two possible attractor states depends probabilistically on the difference of the vibrotactile frequencies scaled by the base frequency. This is due to finite size noise effects related to the spiking activity in the network, and the divisive feedback inhibition implemented through inhibitory interneurons. Thus the neurophysiological basis for a psychophysical effect, Weber's Law, can be related to statistical fluctuations and divisive inhibition in an attractor decision-making network.

Spatial view cells in the primate hippocampus and memory recall

Hippocampal spatial view neurons in primates provide allocentric representations of a view of space 'out there'. The responses depend on where the monkey is looking; and can be updated by idiothetic (self-motion) inputs provided by eye movements when the view is hidden. In a room-based object-place memory task, some hippocampal neurons respond to the objects shown, some to the places viewed, and some to combinations of the places viewed and the objects present in those locations. In an object-place recall task when the location in space at which an object has been seen is recalled by the presentation of the object, some primate hippocampal neurons maintain their responding to the object recall cue in a delay period without the object visible while the place is being recalled; and other neurons respond to the place being recalled. Other spatial view neurons form associations with the rewards present at particular locations in space. These findings, and computational models of the hippocampus, help to show how the primate including human hippocampus is involved in episodic memory.

A computational theory of hippocampal function, and empirical tests of the theory

The main aim of the paper is to present an up-to-date computational theory of hippocampal function and the predictions it makes about the different subregions (dentate gyrus, CA3 and CA1), and to examine behavioral and electrophysiological data that address the functions of the hippocampus and particularly its subregions. Based on the computational proposal that the dentate gyrus produces sparse representations by competitive learning and via the mossy fiber pathway forces new representations on the CA3 during learning (encoding), it has been shown behaviorally that the dentate gyrus supports spatial pattern separation during learning. Based on the computational proposal that CA3-CA3 autoassociative networks are important for episodic memory, it has been shown behaviorally that the CA3 supports spatial rapid one-trial learning, learning of arbitrary associations where space is a component, pattern completion, spatial short-term memory, and sequence learning by associations formed between successive items. The concept that the CA1 recodes information from CA3 and sets up associatively learned backprojections to neocortex to allow subsequent retrieval of information to neocortex, is consistent with findings on consolidation. Behaviorally, the CA1 is implicated in processing temporal information as shown by investigations requiring temporal order pattern separation and associations across time; computationally this could involve temporal decay memory, and temporal sequence memory which might also require CA3. The perforant path input to DG is implicated in learning, to CA3 in retrieval from CA3, and to CA1 in retrieval after longer time intervals ("intermediate-term memory").

Reward-spatial view representations and learning in the primate hippocampus

The primate anterior hippocampus (which corresponds to the rodent ventral hippocampus) receives inputs from brain regions involved in reward processing, such as the amygdala and orbitofrontal cortex. To investigate how this affective input may be incorporated into primate hippocampal function, we recorded neuronal activity while rhesus macaques performed a reward-place association task in which each spatial scene shown on a video monitor had one location that, if touched, yielded a preferred fruit juice reward and a second location that yielded a less-preferred juice reward. Each scene had different locations for the different rewards. Of 312 neurons analyzed in the hippocampus, 18% responded more to the location of the preferred reward in different scenes, and 5% responded to the location of the less-preferred reward. When the locations of the preferred rewards in the scenes were reversed, 60% of 44 hippocampal neurons tested reversed the location to which they responded, showing that the reward-place associations could be altered by new learning in a few trials. The majority (82%) of these 44 hippocampal neurons tested did not respond to reward associations in a visual discrimination, object-reward association task. Thus, the primate hippocampus contains a representation of the reward associations of places "out there" being viewed. By associating places with the rewards available, the concept that the primate hippocampus is involved in object-place event memory is now extended to remembering goals available at different spatial locations. This is an important type of association memory.

Learning invariant object recognition in the visual system with continuous transformations

The cerebral cortex utilizes spatiotemporal continuity in the world to help build invariant representations. In vision, these might be representations of objects. The temporal continuity typical of objects has been used in an associative learning rule with a short-term memory trace to help build invariant object representations. In this paper, we show that spatial continuity can also provide a basis for helping a system to self-organize invariant representations. We introduce a new learning paradigm "continuous transformation learning" which operates by mapping spatially similar input patterns to the same postsynaptic neurons in a competitive learning system. As the inputs move through the space of possible continuous transforms (e.g. translation, rotation, etc.), the active synapses are modified onto the set of postsynaptic neurons. Because other transforms of the same stimulus overlap with previously learned exemplars, a common set of postsynaptic neurons is activated by the new transforms, and learning of the new active inputs onto the same postsynaptic neurons is facilitated. We demonstrate that a hierarchical model of cortical processing in the ventral visual system can be trained with continuous transform learning, and highlight differences in the learning of invariant representations to those achieved by trace learning.