Biomedical subjects

Tomoki Fukai

Publications and source records attributed to Tomoki Fukai.

11 recordsLinked to original sources

PubMed2007

Local cortical circuit model inferred from power-law distributed neuronal avalanches.

How cortical neurons process information crucially depends on how their local circuits are organized. Spontaneous synchronous neuronal activity propagating through neocortical slices displays highly diverse, yet repeatable, activity patterns called "neuronal avalanches". They obey power-law distributions of the event sizes and lifetimes, presumably reflecting the structure of local circuits developed in slice cultures. However, the explicit network structure underlying the power-law statistics remains unclear. Here, we present a neuronal network model of pyramidal and inhibitory neurons that enables stable propagation of avalanche-like spiking activity. We demonstrate a neuronal wiring rule that governs the formation of mutually overlapping cell assemblies during the development of this network. The resultant network comprises a mixture of feedforward chains and recurrent circuits, in which neuronal avalanches are stable if the former structure is predominant. Interestingly, the recurrent synaptic connections formed by this wiring rule limit the number of cell assemblies embeddable in a neuron pool of given size. We investigate how the resultant power laws depend on the details of the cell-assembly formation as well as on the inhibitory feedback. Our model suggests that local cortical circuits may have a more complex topological design than has previously been thought.

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PubMed2006

Computational algorithms and neuronal network models underlying decision processes.

Animals or humans often encounter such situations in which they must choose their behavioral responses to be made in the near or distant future. Such a decision is made through continuous and bidirectional interactions between the environment surrounding the brain and its internal state or dynamical processes. Therefore, decision making may provide a unique field of researches for studying information processing by the brain, a biological system open to information exchanges with the external world. To make a decision, the brain must analyze pieces of information given externally, past experiences in a similar situation, possible behavioral responses, and predicted outcomes of the individual responses. In this article, we review results of recent experimental and theoretical studies of neuronal substrates and computational algorithms for decision processes.

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PubMed2004

Self-organized two-state membrane potential transitions in a network of realistically modeled cortical neurons.

Recent studies have revealed that in vivo cortical neurons show spontaneous transitions between two subthreshold levels of the membrane potentials, 'up' and 'down' states. The neural mechanism of generating those spontaneous states transitions, however, remains unclear. Recent electrophysiological studies have suggested that those state transitions may occur through activation of a hyperpolarization-activated cation current (H-current), possibly by inhibitory synaptic inputs. Here, we demonstrate that two-state membrane potential fluctuations similar to those exhibited by in vivo neurons can be generated through a spike-timing-dependent self-organizing process in a network of inhibitory neurons and excitatory neurons expressing the H-current.

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PubMed2003

A stochastic method to predict the consequence of arbitrary forms of spike-timing-dependent plasticity.

Synapses in various neural preparations exhibit spike-timing-dependent plasticity (STDP) with a variety of learning window functions. The window functions determine the magnitude and the polarity of synaptic change according to the time difference of pre- and postsynaptic spikes. Numerical experiments revealed that STDP learning with a single-exponential window function resulted in a bimodal distribution of synaptic conductances as a consequence of competition between synapses. A slightly modified window function, however, resulted in a unimodal distribution rather than a bimodal distribution. Since various window functions have been observed in neural preparations, we develop a rigorous mathematical method to calculate the conductance distribution for any given window function. Our method is based on the Fokker-Planck equation to determine the conductance distribution and on the Ornstein-Uhlenbeck process to characterize the membrane potential fluctuations. Demonstrating that our method reproduces the known quantitative results of STDP learning, we apply the method to the type of STDP learning found recently in the CA1 region of the rat hippocampus. We find that this learning can result in nearly optimized competition between synapses. Meanwhile, we find that the type of STDP learning found in the cerebellum-like structure of electric fish can result in all-or-none synapses: either all the synaptic conductances are maximized, or none of them becomes significantly large. Our method also determines the window function that optimizes synaptic competition.

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PubMed2003

Synchrony of fast-spiking interneurons interconnected by GABAergic and electrical synapses.

Fast-spiking (FS) interneurons have specific types (Kv3.1/3.2 type) of the delayed potassium channel, which differ from the conventional Hodgkin-Huxley (HH) type potassium channel (Kv1.3 type) in several aspects. In this study, we show dramatic effects of the Kv3.1/3.2 potassium channel on the synchronization of the FS interneurons. We show analytically that two identical electrically coupled FS interneurons modeled with Kv3.1/3.2 channel fire synchronously at arbitrary firing frequencies, unlike similarly coupled FS neurons modeled with Kv1.3 channel that show frequency-dependent synchronous and antisynchronous firing states. Introducing GABA(A) receptor-mediated synaptic connections into an FS neuron pair tends to induce an antisynchronous firing state, even if the chemical synapses are bidirectional. Accordingly, an FS neuron pair connected simultaneously by electrical and chemical synapses achieves both synchronous firing state and antisynchronous firing state in a physiologically plausible range of the conductance ratio between electrical and chemical synapses. Moreover, we find that a large-scale network of FS interneurons connected by gap junctions and bidirectional GABAergic synapses shows similar bistability in the range of gamma frequencies (30-70 Hz).

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PubMed2003

Gamma rhythmic bursts: coherence control in networks of cortical pyramidal neurons.

Much evidence indicates that synchronized gamma-frequency (20-70 Hz) oscillation plays a significant functional role in the neocortex and hippocampus. Chattering neuron is a possible neocortical pacemaker for the gamma oscillation. Based on our recent model of chattering neurons, here we study how gamma-frequency bursting is synchronized in a network of these neurons. Using a phase oscillator description, we first examine how two coupled chattering neurons are synchronized. The analysis reveals that an incremental change of the bursting mode, such as from singlet to doublet, always accompanies a rapid transition from antisynchronous to synchronous firing. The state transition occurs regardless of what changes the bursting mode. Within each bursting mode, the neuronal activity undergoes a gradual change from synchrony to antisynchrony. Since the sensitivity to Ca(2+) and the maximum conductance of Ca(2+)-dependent cationic current as well as the intensity of input current systematically control the bursting mode, these quantities may be crucial for the regulation of the coherence of local cortical activity. Numerical simulations demonstrate that the modulations of the calcium sensitivity and the amplitude of the cationic current can induce rapid transitions between synchrony and asynchrony in a large-scale network of chattering neurons. The rapid synchronization of chattering neurons is shown to synchronize the activities of regular spiking pyramidal neurons at the gamma frequencies, as may be necessary for selective attention or binding processing in object recognition.

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PubMed2002

Two-state membrane potential transitions of striatal spiny neurons as evidenced by numerical simulations and electrophysiological recordings in awake monkeys.

Spontaneous membrane potential fluctuations of striatal spiny projection neurons play a crucial role in their spike generation. Previous intracellular recording studies in anesthetized rats have shown that the membrane potential of striatal spiny neurons shifts between the depolarized "up" state and the hyperpolarized "down" state. Here we report evidence for the occurrence of such two-state membrane potential transitions by numerical simulations and electrophysiological recordings in awake monkeys. Data from our simulations of a striatal spiny neuron model demonstrated that spike latency histograms of the model neuron displayed two separate (i.e., early and late) peaks in response to excitatory cortical input, corresponding to neuronal activity in the up or down state, respectively. Then, we addressed experimentally whether the latency distribution of cortically induced spike firing of striatal spiny neurons might show dual peaks. Striatal neuron activity was extracellularly recorded in response to electrical stimulation in the two cortical motor-related areas, the primary motor cortex and the supplementary motor area, of awake monkeys. Analysis of spike latency histograms has defined that striatal spiny neurons typically exhibit two temporally distinct peaks, as obtained by the numerical simulations. Thus, the membrane potential shifts between the up and down states appear to occur in striatal spiny neurons of the behaving animal.

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PubMed2002

Self-organization of memory activity through spike-timing-dependent plasticity.

We studied the self-organization of memory-related activity through spike-timing-dependent plasticity (STDP). Relatively short time windows (approximately 10 ms) for the plasticity rule give rise to asynchronous persistent activity of low rates (20-30 Hz), which is typically observed in delay periods of working memory task. We demonstrate some network level effects on the activity regulation that cannot be addressed in single-neuron studies. For longer time windows (approximately 20 ms), the layered cell assemblies that propagate synchronized spikes (synfire chain) are self-organized. Synchronous spike propagation was suggested to underlie the precisely timed spikes in the monkey prefrontal cortex. The present results suggest that the two networks for sustained activity are different realizations of the same principle for synaptic wiring.

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PubMedDate not supplied

A cellular mechanism for graded persistent activity in a model neuron and its implications in working memory.

Working memory represents the ability of the brain to hold information for relatively short periods of time. Working memory is believed mediated by persistent neuronal firing. A broadly accepted hypothesis is that persistent activity is generated by reverberating synaptic input. However, single cortical neurons capable of showing persistent firing were recently reported. In this modeling study, we propose a cellular mechanism to generate persistent firing of multiple firing rates in single neurons. In the proposed mechanism, bistable concentrations of inositol 1,4,5-trisphosphate (IP3) and Ca2+ is achieved by IP3 formation and IP3-induced Ca2+ release from stores in multiple subcellular domains. A postsynaptic firing rate-dependent switching of these bistable elements can demonstrate graded persistent firing of the rat entorhinal neurons. Such a firing rate-dependent switching may be extended to a variety of intracellular Ca2+ signaling cascades.

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PubMedDate not supplied

Physiologically realistic modelling of a mechanism for neural representation of intervals of time.

A model for a recurrent network of bistable spiking neurons is examined. Each neuron is described by a leaky-integrate-and-fire formulation with biophysically realistic currents and noise. Specially, neuronal bistability is equipped by after-depolarisation current. Results obtained by computer simulation show that spiking of each neuron starting at an initial time continues for an extended period and then suddenly ceases at around a certain time. We hypothesise that activation of neurons that starts at t = 0 and voluntarily ceases at t = T is a neural underpinning of internal representation of an interval of time T. The above results theoretically support this hypothesis by demonstrating one possible mechanism to generate such time course of neuronal activation.

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PubMedDate not supplied

Temporal characteristics of the predictive synchronous firing modeled by spike-timing-dependent plasticity.

When a sensory cue was repeatedly followed by a behavioral event with fixed delays, pairs of premotor and primary motor neurons showed significant increases of coincident spikes at times a monkey was expecting the event. These results provided evidence that neuronal firing synchrony has predictive power. To elucidate the underlying mechanism, here we argue some nontrivial characteristics of the predictive synchronous firing developed by spike-timing-dependent plasticity in a paradigm similar to classical conditioning. We find that the computationally developed synchrony shows the modulations of temporal precision, which are quite similar to those observed experimentally. Thus, our model suggests that the important characteristics of predictive synchronous firing, which were previously attributed to an animal's higher cognitive function, can emerge from a synaptic-level mechanism.

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