Biomedical subjects

Y Kashimori

Publications and source records attributed to Y Kashimori.

11 recordsLinked to original sources

PubMed2005

Behavior pattern (innate action) of individuals in fish schools generating efficient collective evasion from predation.

The schooling of fishes is one typical animal social behavior. One primary function of fish school is to protect members when attacked by predators. One main way that the school reduces the predator's chance of making a successful kill is to confuse the predator as it makes its strike. This may be accomplished by collective evasion behaviors organized through integration of motions of individual fish made based on their innate actions (behavior patterns). In order to investigate what kind of behavior pattern of individuals can generate the efficient collective evasion of a school, we present a model of evasion behavior pattern which consists of three component behavior patterns, schooling, cooperative escape, and selfish escape behavior patterns and the rule for choice of one among them with proper timing. Each fish determines its movement direction taking into account simultaneously three kinds of elemental motions, mimicking its neighbors, avoiding collisions with its nearest neighbors, and escaping from an approaching predator. The weights of three elemental motions are changed depending on which component behavior pattern the fish carries out. The values of the weights for three component behavior patterns can be definitively determined under the condition that the collective evasion of the school becomes the most efficient, that is, the probability that any member is eaten by the predator becomes minimum.

Animals↗

PubMed2001

A neural mechanism of hyperaccurate detection of phase advance and delay in the jamming avoidance response of weakly electric fish.

The weakly electric fish Eigenmannia can detect the phase difference between a jamming signal and its own signal down to micros. To clarify the neuronal mechanism of this hyperaccurate detection of phase difference, we present a neural network model of the torus of the midbrain which plays an essential role in the detection of phase advances and delays. The small-cell model functions as a coincidence detector and can discriminate a time difference of more than 100 micros. The torus model consists of laminae 6 and 8. The model of lamina 6 is made with multiple encoding units, each of which consists of a single linear array of small cells and a single giant cell. The encoding unit encodes the phase difference into its spatio-temporal firing pattern. The spatially random distribution of small cells in each encoding unit improves the encoding ability of phase modulation. The neurons in lamina 8 can discriminate the phase advance and delay of jamming electric organ discharges (EODs) compared with the phase of the fish's own EOD by integrating simultaneously the outputs from multiple encoding units in lamina 6. The discrimination accuracy of the feature-detection neurons is of the order of 1 micros. The neuronal mechanism generating this hyperacuity arises from the spatial feature of the system that the innervation sites of small cells in different encoding units are distributed randomly and differently on the dendrites of single feature-detection neurons. The mechanism is similar to that of noise-enhanced information transmission.

Animals↗

PubMed2001

A hierarchical dynamical map as a basic frame for cortical mapping and its application to priming.

A hierarchical dynamical map is proposed as the basic framework for sensory cortical mapping. To show how the hierarchical dynamical map works in cognitive processes, we applied it to a typical cognitive task known as priming, in which cognitive performance is facilitated as a consequence of prior experience. Prior to the priming task, the network memorizes a sensory scene containing multiple objects presented simultaneously using a hierarchical dynamical map. Each object is composed of different sensory features. The hierarchical dynamical map presented here is formed by random itinerancy among limit-cycle attractors into which these objects are encoded. Each limit-cycle attractor contains multiple point attractors into which elemental features belonging to the same object are encoded. When a feature stimulus is presented as a priming cue, the network state is changed from the itinerant state to a limit-cycle attractor relevant to the priming cue. After a short priming period, the network state reverts to the itinerant state. Under application of the test cue, consisting of some feature belonging to the object relevant to the priming cue and fragments of features belonging to others, the network state is changed to a limit-cycle attractor and finally to a point attractor relevant to the target feature. This process is considered as the identification of the target. The model consistently reproduces various observed results for priming processes such as the difference in identification time between cross-modality and within-modality priming tasks, the effect of interval between priming cue and test cue on identification time, the effect of priming duration on the time, and the effect of repetition of the same priming task on neural activity.

Brain Mapping↗

PubMed2000

A neural mechanism of hierarchical discrimination of odors in the olfactory cortex based on spatiotemporal encoding of odor information.

We propose a neural mechanism for discrimination of different complex odors in the olfactory cortex based on the dynamical encoding scheme. Both constituent molecules of the odor and their mixing ratios are encoded simultaneously into a spatiotemporal activity pattern (limit cycle attractor) in the olfactory bulb [Hoshino O, Kashimori Y, Kambara T (1998) Biol Cybern 79:109-120]. We present a functional model of the olfactory cortex consisting of some dynamical mapping modules. Each dynamical map is represented by itinerancy among the limit cycle attractors. When a temporal sequence of spatial activity patterns corresponding to a complex odor is injected from the bulb to the network of the olfactory cortex, the neural activity state of each mapping module is fixed to a relevant spatial pattern injected. Recognition of an odor is accomplished by a combination of firing patterns fixed in all the mapping modules. The stronger the response strength of the component, the earlier the component is recognized. The hierarchical discrimination of an odor is made by recognizing the components in order of decreasing response strengths.

Discrimination Learning↗

PubMed1999

Electroreceptor model of weakly electric fish Gnathonemus petersii: II. Cellular origin of inverse waveform tuning.

In part I (. Biophys. J. 75:1712-1726), we presented a cellular model of the A- and B-electroreceptors of the weakly electric fish Gnathonemus petersii. The model made clear the cellular origin of the differences in the response functions of A- and B-receptors, which sensitively code the intensity of the fish's own electric organ discharge (EOD) and the variations in the EOD waveform, respectively. The main purpose of the present paper is to clarify the cellular origin of the inverse waveform tuning of the B-receptors by using the receptor model. Inverse waveform tuning means that B-receptors respond more sensitively to the 180 degrees inverted EOD than to undistorted or less distorted EODs. We investigated how the A- and B-receptor models respond to EODs with various waveforms, which are the phase-shifted EODs, whose shift angle is varied from -1 degrees to -180 degrees, and single-period sine wave stimuli of various frequencies. We show that the tuning properties of the B-receptors arise mainly from the combination of two attributes: 1) The waveform of the stimuli (Bstim) effectively sensed by the B-receptor cells. This consists of a first smaller and a second larger positive peak, even though in the original phase-shifted EOD stimuli, the amplitudes of the two positive peaks are reversed. 2) The effective time constant of dynamical response of the receptor cells. It is on the order of the duration of a single EOD pulse. We also calculated the response properties of the A- and B-receptor models when stimulated with natural EODs distorted by various capacitive and resistive objects. Furthermore, we investigated the effect of EOD amplitude on the receptor responses to capacitive and resistive objects. The models presented can systematically reproduce the experimentally observed response properties of natural A- and B-receptor cells. The mechanism producing these properties can be reasonably explained by the variation in the stimulus waveforms effectively sensed by the A- and B-receptor cells and by time constants.

Action Potentials↗

PubMed1998

An olfactory recognition model based on spatio-temporal encoding of odor quality in the olfactory bulb.

In order to study the problem how the olfactory neural system processes the odorant molecular information for constructing the olfactory image of each object, we present a dynamic model of the olfactory bulb constructed on the basis of well-established experimental and theoretical results. The information relevant to a single odor, i.e. its constituent odorant molecules and their mixing ratios, are encoded into a spatio-temporal pattern of neural activity in the olfactory bulb, where the activity pattern corresponds to a limit cycle attractor in the mitral cell network. The spatio-temporal pattern consists of a temporal sequence of spatial firing patterns: each constituent molecule is encoded into a single spatial pattern, and the order of magnitude of the mixing ratio is encoded into the temporal sequence. The formation of a limit cycle attractor under the application of a novel odor is carried out based on the intensity-to-time-delay encoding scheme. The dynamic state of the olfactory bulb, which has learned many odors, becomes a randomly itinerant state in which the current firing state of the bulb itinerates randomly among limit cycle attractors corresponding to the learned odors. The recognition of an odor is generated by the dynamic transition in the network from the randomly itinerant state to a limit cycle attractor state relevant to the odor, where the transition is induced by the short-term synaptic changes made according to the Hebbian rule under the application of the odor stimulus.

Animals↗

PubMed1998

Effect of syncytium structure of receptor systems on stochastic resonance induced by chaotic potential fluctuation.

To study a role of syncytium structure of sensory receptor systems in the detection of weak signals through stochastic resonance, we present a model of a receptor system with syncytium structure in which receptor cells are interconnected by gap junctions. The apical membrane of each cell includes two kinds of ion channels whose gating processes are described by the deterministic model. The membrane potential of each cell fluctuates chaotically or periodically, depending on the dynamical state of collective channel gating. The chaotic fluctuation of membrane potential acts as internal noise for the stochastic resonance. The detection ability of the system increases as the electric conductance between adjacent cells generated by the gap junction increases. This effect of gap junctions arises mainly from the fact that the synchronization of chaotic fluctuation of membrane potential between the receptor cells is strengthened as the density of gap junctions is increased.

Animals↗

PubMed1998

Electroreceptor model of the weakly electric fish Gnathonemus petersii. I. The model and the origin of differences between A- and B-receptors.

We present an electroreceptor model of the A- and B-receptors of the weakly electric fish Gnathonemus petersii. The model consists of a sensory cell, whose membrane is separated into an apical and basal portions by support cells, and an afferent fiber. The apical membrane of the cell contains only leak channels, while the basal membrane contains voltage-sensitive Ca2+ channels, voltage-sensitive and Ca2+-activated K+ channels, and leak channels. The afferent fiber is described with the modified Hodgkin-Huxley equation, in which the voltage-sensitive gate of the K+ channels is a dynamic variable. In our model we suggest that the electroreceptors detect and process the information provided by an electric organ discharge (EOD) as follows: the current caused by an EOD stimulus depolarizes the basal membrane to a greatly depolarized state. Then the release of transmitter excites the afferent fiber to oscillate after a certain time interval. Due to the resistance-capacitance structure of the cells, they not only perceive the EOD intensity, but also sense the variation of the EOD waveform, which can be strongly distorted by the capacitive component of an object. Because of the different morphologies of A- and B-cells, as well as the different conductance of leak ion channels in the apical membrane and the different capacitance of A- and B-cells, A-receptors mainly respond to the EOD intensity, while B-receptors are sensitive to the variation of EOD waveform.

Animals↗

PubMed1996

Self-organized phase transitions in neural networks as a neural mechanism of information processing.

Transitions between dynamically stable activity patterns imposed on an associative neural network are shown to be induced by self-organized infinitesimal changes in synaptic connection strength and to be a kind of phase transition. A key event for the neural process of information processing in a population coding scheme is transition between the activity patterns encoding usual entities. We propose that the infinitesimal and short-term synaptic changes based on the Hebbian learning rule are the driving force for the transition. The phase transition between the following two dynamical stable states is studied in detail, the state where the firing pattern is changed temporally so as to itinerate among several patterns and the state where the firing pattern is fixed to one of several patterns. The phase transition from the pattern itinerant state to a pattern fixed state may be induced by the Hebbian learning process under a weak input relevant to the fixed pattern. The reverse transition may be induced by the Hebbian unlearning process without input. The former transition is considered as recognition of the input stimulus, while the latter is considered as clearing of the used input data to get ready for new input. To ensure that information processing based on the phase transition can be made by the infinitesimal and short-term synaptic changes, it is absolutely necessary that the network always stays near the critical state corresponding to the phase transition point.

Animals↗

PubMed1996

Model of P- and T-electroreceptors of weakly electric fish.

To clarify the microscopic mechanisms by which P- and T-receptors encode amplitude modulation and zero crossing time of jamming signals, we present a model of P- and T-receptors based on their physiological and anatomical properties. The model consists of a receptor cell, supporting cells, and an afferent nerve fiber. The basal membrane of the receptor cell includes voltage-sensitive Ca2+ channels, Ca(2+)-activated K+ channels, and leak channels of Na+, K+, and Cl-. The driving force of potential change under stimulation is generated by the voltage-sensitive Ca2+ channels, and the suppressing force of the change is generated by Ca(2+)-activated K+ channels. It has been shown that in T-receptor cells the driving force is much stronger than the suppressing force, whereas in P-receptor cells the driving force is comparable with the suppressing force. The difference in various kinds of response properties between P- and T-receptors have been consistently explained based on the difference in the relative strengths of the driving and suppressing forces between P- and T-receptor cells. The response properties considered are encoding function, probability of firing of afferent nerve, pattern of damped oscillation, shape of tuning curves, values of the optimum frequency, and response latency.

Afferent Pathways↗

PubMed1996

Mechanism of nonlinear responses of taste cells to mixed tastes.

Coupling between ionic flows through different kinds of channels affects essentially the responses of multisensitive taste cells to single- and mixed-taste stimuli. The mechanism of taste transduction is studied by using a realistic model of the multisensitive cells of the rat. The transduction pathways considered are amiloride-sensitive Na+ channels for NaCl and HCl and adenosine 3',5'-cyclic monophosphate-mediating K+ channels for sucrose. Contributions of paracellular pathways and taste-insensitive ionic channels are also studied. The calculated responses of various multisensitive cells induced by single stimuli of NaCl, HCl, or sucrose reproduce experimentally observed responses as well. The two main conclusions are as follows. 1) Whether the response of a cell to a binary mixture becomes synergetic or antagonistic can be estimated from the individual responses of the cell to each component of the mixture. 2) Whether the response is enhanced or suppressed by addition of a new taste to the original taste can be determined, for most receptive cells, from a viewpoint of linear superposition of the individual responses.

Animals↗