Coordination of top-down influence on V1 responses by interneurons and brain rhythms.

Top-down processing in neocortex underlies functions such as prediction, expectation, and attention. Visual systems have much feedback connection that carries information of behavioral context. Top-down signals along feedback pathways modulate the representation of visual information in early visual areas such as primary visual cortex (V1). Recent studies have shown further that beta rhythms are responsible for the transmission of behavioral-context information to lower visual areas. However, the mechanism underlying top-down influence and the role of brain rhythms in top-down processing are poorly understood. To address these issues, we focus on experimental studies on top-down influence in visual perceptual tasks. We develop a model of visual system, in which early visual areas are subjected to top-down influence from a recognition area. We show that task-relevant information in early visual areas is regulated by a push-pull effect, produced by somatostatin-expressing interneurons and top-down signal. We also show that task-context information is coordinated by the phase-phase coupling of beta rhythms, while the local, task-relevant stimulus features are enhanced by the phase-amplitude coupling of beta and gamma rhythms. Furthermore, the feedback from a higher visual area such as secondary visual area facilitates the gating of task-relevant information in V1. The results provide insights to understanding the roles of inhibitory interneurons and brain rhythms in top-down influence on information processing in early visual areas.

A Neural Mechanism of Cue-Outcome Expectancy Generated by the Interaction Between Orbitofrontal Cortex and Amygdala.

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.

Representation of object's shape by multiple electric images in electrolocation.

Weakly electric fish generate an electric field by discharging an electric organ located on the tail region. An object near the fish modulates the self-generated electric field. The modulated field enables the fish to perceive objects even in complete darkness. The ability to perceive objects is provided by the electrosensory system of the fish. Electroreceptors distributed on the fish's skin surface can sense the modulated field, on the basis of transdermal voltage across the skin surface, called electric images. The fish can extract object's features such as lateral distance, size, shape, and electric property from an electric image. Although previous studies have demonstrated the relationship between electric-image features and object's distance and size, it remains unclear what features of an electric image represent the object's shape. We make here a hypothesis that shape information is not represented by a single image but by multiple images caused by the object's rotation or fish movement around the object. To test the hypothesis, we develop a computational model that can predict electric images produced by the rotation of differently shaped objects. We used five different shapes of resistive objects: a circle, a square, an equilateral triangle, a rectangle, and an ellipsoid. We show that differently shaped objects of a fixed arrangement generate similar Gaussian electric images, irrespective of their shapes. We also show that the features of an electric image such as the peak amplitude, half-maximum width, and peak position exhibit the angle-dependent variations characteristic to object rotation, depending on object shapes and lateral distances. Furthermore, we demonstrate that an integration effect of the peak amplitude and half-maximum width could be an invariant measure of object shape. These results suggest that the fish could perceive an object shape by combining those image features produced during exploratory behaviors around the object.

A neural mechanism of dynamic gating of task-relevant information by top-down influence in primary visual cortex.

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.

A Neural Mechanism of Taste Perception Modulated by Odor Information.

Taste perception is significantly affected by other sensory modalities such as vision, smell, and somatosensation. Such taste sensation elicited by integrating gustatory and other sensory information is referred to as flavor. Although experimental studies have demonstrated the characteristics of flavor perception influenced by other sensory modalities and the involved brain areas, it remains unknown how flavor emerges from the brain circuits. Of the involved brain areas, orbitofrontal cortex (OFC), as well as gustatory cortex (GC), plays a dominant role in flavor perception. We develop here a neural model of gustatory system which consists of GC and OFC networks and examine the neural mechanism of odor-induced taste perception. Using the model, we show that flavor perception is shaped by experience-dependent learning of foods with congruent taste-odor pairs, providing a unique representation of flavor through the interaction between OFC and GC neurons. Our model also shows that feedback signals from OFC to GC modulate the dynamic stability of taste attractors in GC, leading to the enhancement or suppression of taste responses by smells. Furthermore, modeling shows that spatial variability in GC activity evoked by tastants determines to what extent odor enhances congruent taste responses. The results suggest that flavor perception is deeply associated with dynamic stability of GC attractors through the interaction between GC and OFC.

A neural mechanism of phase-locked responses to sinusoidally amplitude-modulated signals in the inferior colliculus.

The central nucleus of the inferior colliculus (ICc) is an auditory region that receives convergent inputs from a large number of lower auditory nuclei. ICc neurons phase-lock to low frequencies of sinusoidally amplitude-modulated (SAM) signals but have a different mechanism in the phase-locking from that in neurons of lower nuclei. In the mustached bat, the phase-locking ability in lower nuclei is created by the coincidence of phase-locked excitatory and inhibitory inputs that have slightly different latencies. In contrast, the phase-locking property of ICc neurons is little influenced by the blocking of inhibitory synapses. Moreover, ICc neurons exhibit different characteristics in the spike patterns and synchronicity, classified here by three types of ICc neurons, or sustained, onset, and non-onset phase-locking neurons. However it remains unclear how ICc neurons create the phase-locking ability and the different characteristics. To address this issue, we developed a model of ICc neuronal population. Using this model, we show that the phase-locking ability of ICc neurons to low SAM frequencies is created by an intrinsic membrane property of ICc neuron, limited by inhibitory ion channels. We also show that response characteristics of the three types of neurons arise from the difference in an inhibitory effect sensitive to SAM frequencies. Our model reproduces well the experimental results observed in the mustached bat. These findings provide necessary conditions of how ICc neurons can give rise to the phase-locking ability and characteristic responses to low SAM frequencies.

Neural mechanism of dynamic responses of neurons in inferior temporal cortex in face perception.

Understanding the neural mechanisms of object and face recognition is one of the fundamental challenges of visual neuroscience. The neurons in inferior temporal (IT) cortex have been reported to exhibit dynamic responses to face stimuli. However, little is known about how the dynamic properties of IT neurons emerge in the face information processing. To address this issue, we made a model of IT cortex, which performs face perception via an interaction between different IT networks. The model was based on the face information processed by three resolution maps in early visual areas. The network model of IT cortex consists of four kinds of networks, in which the information about a whole face is combined with the information about its face parts and their arrangements. We show here that the learning of face stimuli makes the functional connections between these IT networks, causing a high spike correlation of IT neuron pairs. A dynamic property of subthreshold membrane potential of IT neuron, produced by Hodgkin-Huxley model, enables the coordination of temporal information without changing the firing rate, providing the basis of the mechanism underlying face perception. We show also that the hierarchical processing of face information allows IT cortex to perform a "coarse-to-fine" processing of face information. The results presented here seem to be compatible with experimental data about dynamic properties of IT neurons.

Modeling the electric image produced by objects with complex impedance in weakly electric fish.

Weakly electric fish generate an electric field around their body by electric organ discharge (EOD). By measuring the modulation of the electric field produced by an object in the field these fish are able to accurately locate an object. Theoretical and experimental studies have focused on the amplitude modulations of EODs produced by resistive objects. However, little is known about the phase modulations produced by objects with complex impedance. The fish must be able to detect changes in object impedance to discriminate between food and nonfood objects. To investigate the features of electric images produced by objects with complex impedance, we developed a model that can be used to map the electric field around the fish body. The present model allows us to calculate the spatial distribution of the amplitude and phase shift in an electric image. This is the first study to investigate the changes in amplitude and phase shift of electric images induced by objects with complex impedance in wave-type fish. Using the model, we show that the amplitude of the electric image exhibits a sigmoidal change as the capacitance and resistance of an object are increased. Similarly, the phase shift exhibits a significant change within the object capacitance range of 0.1-100 nF. We also show that the spatial distribution of the amplitude and phase shifts of the electric image resembles a "Mexican hat" in shape for varying object distances and sizes. The spatial distribution of the phase shift and the amplitude was dependent on the object distance and size. Changes in the skin capacitance were associated with a tradeoff relationship between the magnitude of the amplitude and phase shift of the electric image. The specific range of skin capacitance (1-100 nF) allows the receptor afferents to extract object features that are relevant to electrolocation. These results provide a useful basis for the study of the neural mechanisms by which weakly electric fish recognize object features such as distance, size, and impedance.

A quantitative measure for discriminating between self and non-self antigens in immune response.

We present a new theory of how lymphocyte-antigen interaction is governed. We present 'chronicity', a quantitative record of previous lymphocyte-antigen interactions, which is used to regulate lymphocyte behavior. When the chronicity of a lymphocyte increases with the interaction and gets beyond the lower threshold, the lymphocyte can proliferate. Non-self antigens cause lymphocyte proliferation which destroys the antigen. However, self antigens are not destroyed. When the chronicity gets beyond the upper threshold, the lymphocytes get in the tolerance state ensuring non-destruction of self antigens. The discrimination between self and non-self results from the difference in the termination process between self and non-self antigens, caused by the difference in the frequency between interaction of lymphocyte with both antigens.

Functional connections between visual areas in extracting object features critical for a visual categorization task.

The ability to group visual stimuli into meaningful categories is a fundamental cognitive process. Several experiments have been made to investigate the neural mechanism of visual categorization task. Although experimental evidence is known that prefrontal cortex (PFC) and inferior temporal cortex (ITC) sensitively respond in categorization task, little is known about the functional role of interaction between PFC and ITC in categorization task. To address this issue, we present a model, which performs categorization via an interaction between ITC, PFC, and posterior parietal (PP). Using the model, we show here that the functional connections of synapses between neurons in these areas are organized by the learning depending on a reward that is given only by correct behaviors for the task. We also show that the feedback from PFC to ITC allows the sensitivity enhancement of the ITC neurons encoding the object features critical for the task, and the feedback from PFC to PP works as a spatial attention required for finding object feature critical for the task. The model seems to be comparable with experimental data about categorization.

Spatiotemporal burst coding for extracting features of spatiotemporally varying stimuli.

Encoding features of spatiotemporally varying stimuli is quite important for understanding the neural mechanisms of various sensory coding. Temporal coding can encode features of time-varying stimulus, and population coding with temporal coding is adequate for encoding spatiotemporal correlation of stimulus features into spatiotemporal activity of neurons. However, little is known about how spatiotemporal features of stimulus are encoded by spatiotemporal property of neural activity. To address this issue, we propose here a population coding with burst spikes, called here spatiotemporal burst (STB) coding. In STB coding, the temporal variation of stimuli is encoded by the precise onset timing of burst spike, and the spatiotemporal correlation of stimuli is emphasized by one specific aspect of burst firing, or spike packet followed by silent interval. To show concretely the role of STB coding, we study the electrosensory system of a weakly electric fish. Weakly electric fish must perceive the information about an object nearby by analyzing spatiotemporal modulations of electric field around it. On the basis of well-characterized circuitry, we constructed a neural network model of the electrosensory system. Here we show that STB coding encodes well the information of object distance and size by extracting the spatiotemporal correlation of the distorted electric field. The burst activity of electrosensory neurons is also affected by feedback signals through synaptic plasticity. We show that the control of burst activity caused by the synaptic plasticity leads to extracting the stimulus features depending on the stimulus context. Our results suggest that sensory systems use burst spikes as a unit of sensory coding in order to extract spatiotemporal features of stimuli from spatially distributed stimuli.

A role of synchronicity of neural activity based on dynamic plasticity of synapses in encoding spatiotemporal features of electrosensory stimuli.

It is quite important for investigation of sensory mechanism to understand how dynamical property of neurons is used for encoding the feature of spatiotemporally varying stimuli. To consider concretely the problem, we focus our study on electrosensory system of a weakly electric fish. Weakly electric fish generate electric field around their body using electric organ discharge (EOD) and accurately detect the location of an object through the modulation of electric field induced by the object. We made a neural network model of electrosensory lateral-line lobe (ELL). Here we show that the features of EOD modulation depending specifically distance and size of an object are encoded into the timing of burst firing of ELL neurons. These features can be represented by the spatial area of synchronous burst firing and the interburst interval in the ELL network. We show that short-term changes of excitatory and inhibitory synapses, induced by efferent signals, regulate the ELL activity so as to effectively encode the features of EOD modulation.

A neural model of predictive recognition in form pathway of visual cortex.

We present a functional model of form pathway in visual cortex based on predictive coding scheme, in which the prediction is compared with feedforward signals filtered by two kinds of spatial resolution maps, broad and fine resolution map. We propose here the functional role of the prediction and of the two kinds of resolution maps in perception of object form in visual system. The prediction is represented based on memory of dynamical attractors in temporal cortex, categorized by an elemental figure in posterior temporal cortex. The prediction is generated by the feedforward signals of main neurons in broad resolution maps of V(1) and V(4), and then is compared with the feedforward signals of main neurons in fine resolution map of V(1) and V(4).

A role of burst firings in encoding of spatiotemporally-varying stimulus.

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.

A neural mechanism for detecting the distance of a selected target by modulating the FM sweep rate of biosonar in echolocation of bat.

Most species of bats making echolocation use frequency modulated (FM) ultrasonic pulses to measure the distance to targets. These bats detect with a high accuracy the arrival time differences between emitted pulses and their echoes generated by targets. In order to clarify the neural mechanism for echolocation, we present neural model of inferior colliculus (IC), medial geniculate body (MGB) and auditory cortex (AC) along which information of echo delay times is processed. The bats increase the downward frequency sweep rate of emitted FM pulse as they approach the target. The functional role of this modulation of sweep rate is not yet clear. In order to investigate the role, we calculated the response properties of our models of IC, MGB, and AC changing the target distance and the sweep rate. We found based on the simulations that the distance of a target in various ranges may be encoded the most clearly into the activity pattern of delay time map network in AC, when the sweep rate of FM pulse used is coincided with the observed value which the bats adopt for each range of target distance.

Role of Itinerancy Among Attractors as Dynamical Map in Distributed Coding Scheme.

A basic frame, based on which certain features of sensory stimuli can be extracted systematically in a distributed coding scheme, has not been clarified yet. This paper proposes that the basic frame can be a dynamical map represented by itinerancy among attractors. Features of entities are encoded by attractors of neural networks. Relations between the features in each modality are mapped on dynamical links between the attractors in each network relevant to each modality. The itinerant states, in which the network dynamic state itinerates chaotically or cyclically among the attractors, can work as dynamical maps. The recognition of features is carried out by a phase transition from an itinerant state to a constituent attractor. The phase transition is induced by a short-term synaptic change based on the Hebbian rule under application of a relevant stimulation. A theta-like global oscillation is necessary for self-organized formation of the chaotically itinerant state.