Searching for visual features that explain response variance of face neurons in inferior temporal cortex.

Despite a large body of research on response properties of neurons in the inferior temporal (IT) cortex, studies to date have not yet produced quantitative feature descriptions that can predict responses to arbitrary objects. This deficit in the research prevents a thorough understanding of object representation in the IT cortex. Here we propose a fragment-based approach for finding quantitative feature descriptions of face neurons in the IT cortex. The development of the proposed method was driven by the assumption that it is possible to recover features from a set of natural image fragments if the set is sufficiently large. To find the feature from the set, we compared object responses predicted from each fragment and responses of neurons to these objects, and search for the fragment that revealed the highest correlation with neural object responses. Prediction of object responses of each fragment was made by normalizing Euclidian distance between the fragment and each object to 0 to 1 such that the smaller distance gives the higher value. The distance was calculated at the space where images were transformed to a local orientation space by a Gabor filter and a local max operation. The method allowed us to find features with a correlation coefficient between predicted and neural responses of 0.68 on average (number of object stimuli, 104) from among 560,000 feature candidates, reliably explaining differential responses among faces as well as a general preference for faces over to non-face objects. Furthermore, predicted responses of the resulting features to novel object images were significantly correlated with neural responses to these images. Identification of features comprising specific, moderately complex combinations of local orientations and colors enabled us to predict responses to upright and inverted faces, which provided a possible mechanism of face inversion effects. (292/300).

Gap junctions facilitate propagation of synchronous firing in the cortical neural population: a numerical simulation study.

This study investigates the effect of gap junctions on firing propagation in a feedforward neural network by a numerical simulation with biologically plausible parameters. Gap junctions are electrical couplings between two cells connected by a binding protein, connexin. Recent electrophysiological studies have reported that a large number of inhibitory neurons in the mammalian cortex are mutually connected by gap junctions, and synchronization of gap junctions, spread over several hundred microns, suggests that these have a strong effect on the dynamics of the cortical network. However, the effect of gap junctions on firing propagation in cortical circuits has not been examined systematically. In this study, we perform numerical simulations using biologically plausible parameters to clarify this effect on population firing in a feedforward neural network. The results suggest that gap junctions switch the temporally uniform firing in a layer to temporally clustered firing in subsequent layers, resulting in an enhancement in the propagation of population firing in the feedforward network. Because gap junctions are often modulated in physiological conditions, we speculate that gap junctions could be related to a gating function of population firing in the brain.

Temperature-modulated synchronization transition in coupled neuronal oscillators.

We study two firing properties to characterize the activities of a neuron: frequency-current (f-I) curves and phase response curves (PRCs), with variation in the intrinsic temperature scaling parameter (µ) controlling the opening and closing of ionic channels. We show a peak of the firing frequency for small µ in a class I neuron with the I value immediately after the saddle-node bifurcation, which is entirely different from previous experimental reports as well as model studies. The PRC takes a type II form on a logarithmic f-I curve when µ is small. Then, we analyze the synchronization phenomena in a two-neuron network using the phase-reduction method. We find common µ-dependent transition and bifurcation of synchronizations, regardless of the values of I. Such results give us helpful insight into synchronizations tuned with a sinusoidal-wave temperature modulation on neurons.

Dendritic slow dynamics enables localized cortical activity to switch between mobile and immobile modes with noisy background input.

Mounting lines of evidence suggest the significant computational ability of a single neuron empowered by active dendritic dynamics. This motivates us to study what functionality can be acquired by a network of such neurons. The present paper studies how such rich single-neuron dendritic dynamics affects the network dynamics, a question which has scarcely been specifically studied to date. We simulate neurons with active dendrites networked locally like cortical pyramidal neurons, and find that naturally arising localized activity--called a bump--can be in two distinct modes, mobile or immobile. The mode can be switched back and forth by transient input to the cortical network. Interestingly, this functionality arises only if each neuron is equipped with the observed slow dendritic dynamics and with in vivo-like noisy background input. If the bump activity is considered to indicate a point of attention in the sensory areas or to indicate a representation of memory in the storage areas of the cortex, this would imply that the flexible mode switching would be of great potential use for the brain as an information processing device. We derive these conclusions using a natural extension of the conventional field model, which is defined by combining two distinct fields, one representing the somatic population and the other representing the dendritic population. With this tool, we analyze the spatial distribution of the degree of after-spike adaptation and explain how we can understand the presence of the two distinct modes and switching between the modes. We also discuss the possible functional impact of this mode-switching ability.

Near scale-free dynamics in neural population activity of waking/sleeping rats revealed by multiscale analysis.

A neuron embedded in an intact brain, unlike an isolated neuron, participates in network activity at various spatial resolutions. Such multiple scale spatial dynamics is potentially reflected in multiple time scales of temporal dynamics. We identify such multiple dynamical time scales of the inter-spike interval (ISI) fluctuations of neurons of waking/sleeping rats by means of multiscale analysis. The time scale of large non-Gaussianity in the ISI fluctuations, measured with the Castaing method, ranges up to several minutes, markedly escaping the low-pass filtering characteristics of neurons. A comparison between neural activity during waking and sleeping reveals that non-Gaussianity is stronger during waking than sleeping throughout the entire range of scales observed. We find a remarkable property of near scale independence of the magnitude correlations as the primary cause of persistent non-Gaussianity. Such scale-invariance of correlations is characteristic of multiplicative cascade processes and raises the possibility of the existence of a scale independent memory preserving mechanism.

Flexible traffic control of the synfire-mode transmission by inhibitory modulation: nonlinear noise reduction.

Intermingled neural connections apparent in the brain make us wonder what controls the traffic of propagating activity in the brain to secure signal transmission without harmful crosstalk. Here, we reveal that inhibitory input but not excitatory input works as a particularly useful traffic controller because it controls the degree of synchrony of population firing of neurons as well as controlling the size of the population firing bidirectionally. Our dynamical system analysis reveals that the synchrony enhancement depends crucially on the nonlinear membrane potential dynamics and a hidden slow dynamical variable. Our electrophysiological study with rodent slice preparations show that the phenomenon happens in real neurons. Furthermore, our analysis with the Fokker-Planck equations demonstrates the phenomenon in a semianalytical manner.

Bidirectional plasticity in fast-spiking GABA circuits by visual experience.

Experience-dependent plasticity in the brain requires balanced excitation-inhibition. How individual circuit elements contribute to plasticity outcome in complex neocortical networks remains unknown. Here we report an intracellular analysis of ocular dominance plasticity-the loss of acuity and cortical responsiveness for an eye deprived of vision in early life. Unlike the typical progressive loss of pyramidal-cell bias, direct recording from fast-spiking cells in vivo reveals a counterintuitive initial shift towards the occluded eye followed by a late preference for the open eye, consistent with a spike-timing-dependent plasticity rule for these inhibitory neurons. Intracellular pharmacology confirms a dynamic switch of GABA (gamma-aminobutyric acid) impact to pyramidal cells following deprivation in juvenile mice only. Together these results suggest that the bidirectional recruitment of an initially binocular GABA circuit may contribute to experience-dependent plasticity in the developing visual cortex.

Interplay between a phase response curve and spike-timing-dependent plasticity leading to wireless clustering.

A phase response curve (PRC) characterizes the signal transduction between oscillators such as neurons on a fixed network in a minimal manner, while spike-timing-dependent plasiticity (STDP) characterizes the way of rewiring networks in an activity-dependent manner. This paper demonstrates that these two key properties both related to the interaction times of oscillators work synergetically to carve functionally useful circuits. STDP working on neurons that prefer asynchrony converts the initial asynchronous firing to clustered firing with synchrony within a cluster. They get synchronized within a cluster despite their preference to asynchrony because STDP selectively disrupts intracluster connections, which we call wireless clustering. Our PRC analysis reveals a triad mechanism: the network structure affects how the PRC is read out to determine the synchrony tendency, the synchrony tendency affects how the STDP works, and STDP affects the network structure, closing the loop.

Relation between single neuron and population spiking statistics and effects on network activity.

To simplify theoretical analyses of neural networks, individual neurons are often modeled as Poisson processes. An implicit assumption is that even if the spiking activity of each neuron is non-Poissonian, the composite activity obtained by summing many spike trains limits to a Poisson process. Here, we show analytically and through simulations that this assumption is invalid. Moreover, we show with Fokker-Planck equations that the behavior of feedforward networks is reproduced accurately only if the tendency of neurons to fire periodically is incorporated by using colored noise whose autocorrelation has a negative component.

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.

Kinetic analysis of multisite phosphorylation using analytic solutions to Michaelis-Menten equations.

Phosphorylation-induced expression or modulation of a functional protein is a common signal in living cells. Many functional proteins are phosphorylated at multiple sites and it is frequently observed that phosphorylation at one site enhances or suppresses phosphorylation at another site. Therefore, characterizing such cooperative phosphorylation is important. In this study, we determine a temporal progress curve of multisite phosphorylation by analytically integrating the Michaelis-Menten equations in time. Using this theoretical progress curve, we derive the useful criterion that an intersection of two progress curves implies the presence of cooperativity. Experiments generally yield noisy progress curves. We fit the theoretical progress curves to noisy progress curves containing 4% Gaussian noise in order to determine the kinetics of the phosphorylation. This fitting correctly identifies the sites involved in cooperative phosphorylation.

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.

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.