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Taste perception is important for animals to take adequate nutrients and avoid toxins for their survival. Appetitive and aversive behaviors are produced by value evaluation of taste and taste expectation caused by other sensations. The value evaluation, coupled with a cue presentation, produces outcome expectation and guides flexible behaviors when the environment is changed. Experimental studies demonstrated distinct functional roles of basolateral amygdala (ABL) and orbitofrontal cortex (OFC) in value evaluation and adaptive behavior. ABL is involved in generating a cue–outcome association, whereas OFC makes a contribution of generating a cue-triggered expectation to guide adaptive behavior. However, it remains unclear how ABL and OFC form their functional roles, with the learning of adaptive behavior. To address this issue, we focus on an odor discrimination task of rats and develop a computational model that consists of OFC and ABL, interacting with reward and decision systems. We present the neural mechanisms underlying the rapid formation of cue–outcome association in ABL and late behavioral adaptation mediated by OFC. Moreover, we offer 2 functions of cue-selective neurons in OFC: one is that the activation of cue-selective neurons transmits value information to decision area to guide behavior and another is that persistent activity of cue-selective neurons evokes a weak activity of taste-sensitive OFC neurons, leading to cue-outcome expectation. Our model further accounts for ABL and OFC responses caused by lesions of these areas. The results provide a computational framework of how ABL and OFC are functionally linked through their interactions with the reward and decision systems. [ABSTRACT FROM AUTHOR]

Visual recognition involves bidirectional information flow, which consists of bottom-up information coding from retina and top-down information coding from higher visual areas. Recent studies have demonstrated the involvement of early visual areas such as primary visual area (V1) in recognition and memory formation. V1 neurons are not passive transformers of sensory inputs but work as adaptive processor, changing their function according to behavioral context. Top-down signals affect tuning property of V1 neurons and contribute to the gating of sensory information relevant to behavior. However, little is known about the neuronal mechanism underlying the gating of task-relevant information in V1. To address this issue, we focus on task-dependent tuning modulations of V1 neurons in two tasks of perceptual learning. We develop a model of the V1, which receives feedforward input from lateral geniculate nucleus and top-down input from a higher visual area. We show here that the change in a balance between excitation and inhibition in V1 connectivity is necessary for gating task-relevant information in V1. The balance change well accounts for the modulations of tuning characteristic and temporal properties of V1 neuronal responses. We also show that the balance change of V1 connectivity is shaped by top-down signals with temporal correlations reflecting the perceptual strategies of the two tasks. We propose a learning mechanism by which synaptic balance is modulated. To conclude, top-down signal changes the synaptic balance between excitation and inhibition in V1 connectivity, enabling early visual area such as V1 to gate context-dependent information under multiple task performances. [ABSTRACT FROM AUTHOR]

Abstract: To investigate the mechanism of sensory coding of spatiotemporally varying stimuli, we study the population coding of receptor network in electrosensory system. Weakly electric fish use amplitude modulations of their self-generated electric organ discharge (EOD AM) to detect distance and size of an object such as prey. We developed a model of fish by which we calculate numerically the spatiotemporal patterns of electric field around the fish body, and made a model of electroreceptor network. We showed previously that the information of object distance and size are represented by a combination of two characteristic features of EOD AM, the maximum amplitude and half-maximum width of EOD AM. We show here that the information of object distance and its size are encoded as synchronous firing of receptor network, leading to a reliable encoding of the two features of EOD AM in target neurons to which the receptors project, through a rapid synaptic integration of the receptor outputs. We further show that an optimal distribution of receptor sensitivity to EOD stimuli may provide a significant change of the two EOD AM features depending on object distance. [Copyright &y& Elsevier]

Abstract: Sensory systems utilize dynamical response properties of neurons to extract features of spatiotemporally varying stimuli. We propose a dynamic population coding of sensory stimuli in which the stimulus features are encoded into a spatiotemporal firing pattern of neuron population. Using electrolocation of weakly electric fish as a model system, we study how the spatiotemporal features of electric field modulated depending on the distance and size of an object are encoded into the spatial distribution and time interval between burst spikes of neuron population. We showed that the information about distance and size of object are represented as a function of the spatial area of and time interval between the synchronous firing of neuron population in a higher nucleus. [Copyright &y& Elsevier]

To investigate a role of burst firings of neurons in encoding of spatiotemporally-varying stimulus, we focus on electrosensory system of a weakly electric fish. Weakly electric fish generates electric field around its body using electric organ discharge and can accurately detect the location of an object using the modulation of electric field induced by the object. We developed a model of fish body by which we numerically describe the spatiotemporal patterns of electric field around the fish body. We also made neural models of electroreceptor distributed on the fish body and of electrosensory lateral-line lobe (ELL) to investigate what kinds of information of electric field distorted by an object they detect. Here we show that the spatiotemporal features of electric field around the fish body are encoded by the timing of burst firings of ELL neurons. The information of object distance is extracted by the area of synchronous firings of neurons in a higher nucleus, torus semicircularis. [Copyright &y& Elsevier]

The ability to group visual stimuli into meaningful categories is a fundamental cognitive process. Some experiments are made to investigate the neural mechanism of visual categorization. Although experimental evidence is known that prefrontal cortex (PFC) and inferior temporal cortex (ITC) neurons sensitively respond in categorization task, little is known about the role of interaction between ITC and PFC in categorization task that gives the category boundaries in relation to behavioral consequences. To address this issue, we propose a functional model of visual system in which categorization task is achieved based on functional roles of ITC and PFC. The functional role of ITC is to represent features of object parts, based on different resolution maps in early visual system such as V1 and V4. In ITC, visual stimuli are categorized by the similarity based on the features of object parts. The PFC neurons combine the information about feature and location of object parts, and generate a working memory of the object information relevant to the categorization task. The synaptic connections between ITC and PFC are learned so as to achieve the categorization task. The feedback signals from PFC to ITC enhance the sensitivity of ITC neurons that respond to the features of object parts critical for the categorization task. In the present study, we present a neural network model, which makes categories of visual objects depending on categorization task. We investigated the neural mechanism of the categorization task of line drawings of faces used by Sigala and Logothetis (N. Sigala, N.K. Logothetis, Visual categorization shapes feature selectivity in the primate temporal cortex, Nature 415 (2002) 318–320). Using this model we show that ITC represents similarity of face images based on the information of the resolution maps in V1 and V4. We also show that PFC generates a working memory state, in which only the information of face features relevant to the categorization task are sustained. It is further shown that the feedback connections from PFC to ITC may play an important role in extracting the diagnostic features of visual images in the categorization task. [Copyright &y& Elsevier]

Abstract: We present a functional model of form pathway in visual cortex based on the predictive recognition scheme, in which feedback image signals predicted from the memory system are compared with the feedforward image signals from the retina. We propose the functional roles of three kinds of spatial resolution maps in predictive perception of object form. The broad resolution map is used to categorize visual image based on the outline of the object and to choose the prediction image in the memory system based on the categorization. The middle resolution map is used for more detailed categorizations and for focusing prediction range. The fine resolution map is used to compare the feedforward image of a part of the object with the image predicted. [Copyright &y& Elsevier]