3,594 research outputs found
Inhibitory synchrony as a mechanism for attentional gain modulation
Recordings from area V4 of monkeys have revealed that when the focus of
attention is on a visual stimulus within the receptive field of a cortical
neuron, two distinct changes can occur: The firing rate of the neuron can
change and there can be an increase in the coherence between spikes and the
local field potential in the gamma-frequency range (30-50 Hz). The hypothesis
explored here is that these observed effects of attention could be a
consequence of changes in the synchrony of local interneuron networks. We
performed computer simulations of a Hodgkin-Huxley type neuron driven by a
constant depolarizing current, I, representing visual stimulation and a
modulatory inhibitory input representing the effects of attention via local
interneuron networks. We observed that the neuron's firing rate and the
coherence of its output spike train with the synaptic inputs was modulated by
the degree of synchrony of the inhibitory inputs. The model suggest that the
observed changes in firing rate and coherence of neurons in the visual cortex
could be controlled by top-down inputs that regulated the coherence in the
activity of a local inhibitory network discharging at gamma frequencies.Comment: J.Physiology (Paris) in press, 11 figure
Characterizing synaptic conductance fluctuations in cortical neurons and their influence on spike generation
Cortical neurons are subject to sustained and irregular synaptic activity
which causes important fluctuations of the membrane potential (Vm). We review
here different methods to characterize this activity and its impact on spike
generation. The simplified, fluctuating point-conductance model of synaptic
activity provides the starting point of a variety of methods for the analysis
of intracellular Vm recordings. In this model, the synaptic excitatory and
inhibitory conductances are described by Gaussian-distributed stochastic
variables, or colored conductance noise. The matching of experimentally
recorded Vm distributions to an invertible theoretical expression derived from
the model allows the extraction of parameters characterizing the synaptic
conductance distributions. This analysis can be complemented by the matching of
experimental Vm power spectral densities (PSDs) to a theoretical template, even
though the unexpected scaling properties of experimental PSDs limit the
precision of this latter approach. Building on this stochastic characterization
of synaptic activity, we also propose methods to qualitatively and
quantitatively evaluate spike-triggered averages of synaptic time-courses
preceding spikes. This analysis points to an essential role for synaptic
conductance variance in determining spike times. The presented methods are
evaluated using controlled conductance injection in cortical neurons in vitro
with the dynamic-clamp technique. We review their applications to the analysis
of in vivo intracellular recordings in cat association cortex, which suggest a
predominant role for inhibition in determining both sub- and supra-threshold
dynamics of cortical neurons embedded in active networks.Comment: 9 figures, Journal of Neuroscience Methods (in press, 2008
Neuronal Synchronization Can Control the Energy Efficiency of Inter-Spike Interval Coding
The role of synchronous firing in sensory coding and cognition remains
controversial. While studies, focusing on its mechanistic consequences in
attentional tasks, suggest that synchronization dynamically boosts sensory
processing, others failed to find significant synchronization levels in such
tasks. We attempt to understand both lines of evidence within a coherent
theoretical framework. We conceptualize synchronization as an independent
control parameter to study how the postsynaptic neuron transmits the average
firing activity of a presynaptic population, in the presence of
synchronization. We apply the Berger-Levy theory of energy efficient
information transmission to interpret simulations of a Hodgkin-Huxley-type
postsynaptic neuron model, where we varied the firing rate and synchronization
level in the presynaptic population independently. We find that for a fixed
presynaptic firing rate the simulated postsynaptic interspike interval
distribution depends on the synchronization level and is well-described by a
generalized extreme value distribution. For synchronization levels of 15% to
50%, we find that the optimal distribution of presynaptic firing rate,
maximizing the mutual information per unit cost, is maximized at ~30%
synchronization level. These results suggest that the statistics and energy
efficiency of neuronal communication channels, through which the input rate is
communicated, can be dynamically adapted by the synchronization level.Comment: 47 pages, 14 figures, 2 Table
Balanced Synaptic Input Shapes the Correlation between Neural Spike Trains
Stimulus properties, attention, and behavioral context influence correlations between the spike times produced by a pair of neurons. However, the biophysical mechanisms that modulate these correlations are poorly understood. With a combined theoretical and experimental approach, we show that the rate of balanced excitatory and inhibitory synaptic input modulates the magnitude and timescale of pairwise spike train correlation. High rate synaptic inputs promote spike time synchrony rather than long timescale spike rate correlations, while low rate synaptic inputs produce opposite results. This correlation shaping is due to a combination of enhanced high frequency input transfer and reduced firing rate gain in the high input rate state compared to the low state. Our study extends neural modulation from single neuron responses to population activity, a necessary step in understanding how the dynamics and processing of neural activity change across distinct brain states
General features of the retinal connectome determine the computation of motion anticipation
Motion anticipation allows the visual system to compensate for the slow speed of phototransduction so that a moving object can be accurately located. This correction is already present in the signal that ganglion cells send from the retina but the biophysical mechanisms underlying this computation are not known. Here we demonstrate that motion anticipation is computed autonomously within the dendritic tree of each ganglion cell and relies on feedforward inhibition. The passive and non-linear interaction of excitatory and inhibitory synapses enables the somatic voltage to encode the actual position of a moving object instead of its delayed representation. General rather than specific features of the retinal connectome govern this computation: an excess of inhibitory inputs over excitatory, with both being randomly distributed, allows tracking of all directions of motion, while the average distance between inputs determines the object velocities that can be compensated for
Does Corticothalamic Feedback Control Cortical Velocity Tuning?
The thalamus is the major gate to the cortex and its contribution to cortical
receptive field properties is well established. Cortical feedback to the
thalamus is, in turn, the anatomically dominant input to relay cells, yet its
influence on thalamic processing has been difficult to interpret. For an
understanding of complex sensory processing, detailed concepts of the
corticothalamic interplay need yet to be established. To study
corticogeniculate processing in a model, we draw on various physiological and
anatomical data concerning the intrinsic dynamics of geniculate relay neurons,
the cortical influence on relay modes, lagged and nonlagged neurons, and the
structure of visual cortical receptive fields. In extensive computer
simulations we elaborate the novel hypothesis that the visual cortex controls
via feedback the temporal response properties of geniculate relay cells in a
way that alters the tuning of cortical cells for speed.Comment: 31 pages, 7 figure
Does Corticothalamic Feedback Control Cortical Velocity Tuning?
The thalamus is the major gate to the cortex and its contribution to cortical receptive field properties is well established. Cortical feedback to the thalamus is, in turn, the anatomically dominant input to relay cells, yet its influence on thalamic processing has been difficult to interpret. For an understanding of complex sensory processing, detailed concepts of the corticothalamic interplay need yet to be established. To study corticogeniculate processing in a model, we draw on various physiological and anatomical data concerning the intrinsic dynamics of geniculate relay neurons, the cortical influence on relay modes, lagged and nonlagged neurons, and the structure of visual cortical receptive fields. In extensive computer simulations we elaborate the novel hypothesis that the visual cortex controls via feedback the temporal response properties of geniculate relay cells in a way that alters the tuning of cortical cells for speed
Regulation of Irregular Neuronal Firing by Autaptic Transmission
The importance of self-feedback autaptic transmission in modulating
spike-time irregularity is still poorly understood. By using a biophysical
model that incorporates autaptic coupling, we here show that self-innervation
of neurons participates in the modulation of irregular neuronal firing,
primarily by regulating the occurrence frequency of burst firing. In
particular, we find that both excitatory and electrical autapses increase the
occurrence of burst firing, thus reducing neuronal firing regularity. In
contrast, inhibitory autapses suppress burst firing and therefore tend to
improve the regularity of neuronal firing. Importantly, we show that these
findings are independent of the firing properties of individual neurons, and as
such can be observed for neurons operating in different modes. Our results
provide an insightful mechanistic understanding of how different types of
autapses shape irregular firing at the single-neuron level, and they highlight
the functional importance of autaptic self-innervation in taming and modulating
neurodynamics.Comment: 27 pages, 8 figure
Glycinergic inhibition tunes coincidence detection in the auditory brainstem
Neurons in the medial superior olive (MSO) detect microsecond differences in the arrival time of sounds between the ears (interaural time differences or ITDs),a crucial binaural cue for sound localization. Synaptic inhibition has been implicated in tuning ITD sensitivity, but the cellular mechanisms underlying its influence on coincidence detection are debated. Here we determine the impact of inhibition on coincidence detection in adult Mongolian gerbil MSO brain slices by testing precise temporal integration of measured synaptic responses using conductance-clamp. We find that inhibition dynamically shifts the peak timing of excitation, depending on its relative arrival time, which in turn modulates the timing of best coincidence detection. Inhibitory control of coincidence detection timing is consistent with the diversity of ITD functions observed in vivo and is robust under physiologically relevant conditions. Our results provide strong evidence that temporal interactions between excitation and inhibition on microsecond timescales are critical for binaural processing
Spike Train Auto-Structure Impacts Post-Synaptic Firing and Timing-Based Plasticity
Cortical neurons are typically driven by several thousand synapses. The precise spatiotemporal pattern formed by these inputs can modulate the response of a post-synaptic cell. In this work, we explore how the temporal structure of pre-synaptic inhibitory and excitatory inputs impact the post-synaptic firing of a conductance-based integrate and fire neuron. Both the excitatory and inhibitory input was modeled by renewal gamma processes with varying shape factors for modeling regular and temporally random Poisson activity. We demonstrate that the temporal structure of mutually independent inputs affects the post-synaptic firing, while the strength of the effect depends on the firing rates of both the excitatory and inhibitory inputs. In a second step, we explore the effect of temporal structure of mutually independent inputs on a simple version of Hebbian learning, i.e., hard bound spike-timing-dependent plasticity. We explore both the equilibrium weight distribution and the speed of the transient weight dynamics for different mutually independent gamma processes. We find that both the equilibrium distribution of the synaptic weights and the speed of synaptic changes are modulated by the temporal structure of the input. Finally, we highlight that the sensitivity of both the post-synaptic firing as well as the spike-timing-dependent plasticity on the auto-structure of the input of a neuron could be used to modulate the learning rate of synaptic modification
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