152 research outputs found
Lognormal firing rate distribution reveals prominent fluctuation-driven regime in spinal motor networks
When spinal circuits generate rhythmic movements it is important that the neuronal activity remains within stable bounds to avoid saturation and to preserve responsiveness. Here, we simultaneously record from hundreds of neurons in lumbar spinal circuits of turtles and establish the neuronal fraction that operates within either a âmean-drivenâ or a âfluctuationâdrivenâ regime. Fluctuation-driven neurons have a âsupralinearâ input-output curve, which enhances sensitivity, whereas the mean-driven regime reduces sensitivity. We find a rich diversity of firing rates across the neuronal population as reflected in a lognormal distribution and demonstrate that half of the neurons spend at least 50 [Formula: see text] of the time in the âfluctuationâdrivenâ regime regardless of behavior. Because of the disparity in inputâoutput properties for these two regimes, this fraction may reflect a fine tradeâoff between stability and sensitivity in order to maintain flexibility across behaviors. DOI: http://dx.doi.org/10.7554/eLife.18805.00
A mean-field model for conductance-based networks of adaptive exponential integrate-and-fire neurons
Voltage-sensitive dye imaging (VSDi) has revealed fundamental properties of
neocortical processing at mesoscopic scales. Since VSDi signals report the
average membrane potential, it seems natural to use a mean-field formalism to
model such signals. Here, we investigate a mean-field model of networks of
Adaptive Exponential (AdEx) integrate-and-fire neurons, with conductance-based
synaptic interactions. The AdEx model can capture the spiking response of
different cell types, such as regular-spiking (RS) excitatory neurons and
fast-spiking (FS) inhibitory neurons. We use a Master Equation formalism,
together with a semi-analytic approach to the transfer function of AdEx
neurons. We compare the predictions of this mean-field model to simulated
networks of RS-FS cells, first at the level of the spontaneous activity of the
network, which is well predicted by the mean-field model. Second, we
investigate the response of the network to time-varying external input, and
show that the mean-field model accurately predicts the response time course of
the population. One notable exception was that the "tail" of the response at
long times was not well predicted, because the mean-field does not include
adaptation mechanisms. We conclude that the Master Equation formalism can yield
mean-field models that predict well the behavior of nonlinear networks with
conductance-based interactions and various electrophysiolgical properties, and
should be a good candidate to model VSDi signals where both excitatory and
inhibitory neurons contribute.Comment: 21 pages, 7 figure
Death and rebirth of neural activity in sparse inhibitory networks
In this paper, we clarify the mechanisms underlying a general phenomenon
present in pulse-coupled heterogeneous inhibitory networks: inhibition can
induce not only suppression of the neural activity, as expected, but it can
also promote neural reactivation. In particular, for globally coupled systems,
the number of firing neurons monotonically reduces upon increasing the strength
of inhibition (neurons' death). However, the random pruning of the connections
is able to reverse the action of inhibition, i.e. in a sparse network a
sufficiently strong synaptic strength can surprisingly promote, rather than
depress, the activity of the neurons (neurons' rebirth). Thus the number of
firing neurons reveals a minimum at some intermediate synaptic strength. We
show that this minimum signals a transition from a regime dominated by the
neurons with higher firing activity to a phase where all neurons are
effectively sub-threshold and their irregular firing is driven by current
fluctuations. We explain the origin of the transition by deriving an analytic
mean field formulation of the problem able to provide the fraction of active
neurons as well as the first two moments of their firing statistics. The
introduction of a synaptic time scale does not modify the main aspects of the
reported phenomenon. However, for sufficiently slow synapses the transition
becomes dramatic, the system passes from a perfectly regular evolution to an
irregular bursting dynamics. In this latter regime the model provides
predictions consistent with experimental findings for a specific class of
neurons, namely the medium spiny neurons in the striatum.Comment: 19 pages, 10 figures, submitted to NJ
Stimulus competition by inhibitory interference
When two stimuli are present in the receptive field of a V4 neuron, the
firing rate response is between the weakest and strongest response elicited by
each of the stimuli alone (Reynolds et al, 1999, Journal of Neuroscience
19:1736-1753). When attention is directed towards the stimulus eliciting the
strongest response (the preferred stimulus), the response to the pair is
increased, whereas the response decreases when attention is directed to the
other stimulus (the poor stimulus). These experimental results were reproduced
in a model of a V4 neuron under the assumption that attention modulates the
activity of local interneuron networks. The V4 model neuron received
stimulus-specific asynchronous excitation from V2 and synchronous inhibitory
inputs from two local interneuron networks in V4. Each interneuron network was
driven by stimulus-specific excitatory inputs from V2 and was modulated by a
projection from the frontal eye fields. Stimulus competition was present
because of a delay in arrival time of synchronous volleys from each interneuron
network. For small delays, the firing rate was close to the rate elicited by
the preferred stimulus alone, whereas for larger delays it approached the
firing rate of the poor stimulus. When either stimulus was presented alone the
neuron's response was not altered by the change in delay. The model suggests
that top-down attention biases the competition between V2 columns for control
of V4 neurons by changing the relative timing of inhibition rather than by
changes in the degree of synchrony of interneuron networks. The mechanism
proposed here for attentional modulation of firing rate - gain modulation by
inhibitory interference - is likely to have more general applicability to
cortical information processing.Comment: 20 pages, 7 figures, 1 tabl
Intrinsic Stability of Temporally Shifted Spike-Timing Dependent Plasticity
Spike-timing dependent plasticity (STDP), a widespread synaptic modification mechanism, is sensitive to correlations between presynaptic spike trains and it generates competition among synapses. However, STDP has an inherent instability because strong synapses are more likely to be strengthened than weak ones, causing them to grow in strength until some biophysical limit is reached. Through simulations and analytic calculations, we show that a small temporal shift in the STDP window that causes synchronous, or nearly synchronous, pre- and postsynaptic action potentials to induce long-term depression can stabilize synaptic strengths. Shifted STDP also stabilizes the postsynaptic firing rate and can implement both Hebbian and anti-Hebbian forms of competitive synaptic plasticity. Interestingly, the overall level of inhibition determines whether plasticity is Hebbian or anti-Hebbian. Even a random symmetric jitter of a few milliseconds in the STDP window can stabilize synaptic strengths while retaining these features. The same results hold for a shifted version of the more recent âtripletâ model of STDP. Our results indicate that the detailed shape of the STDP window function near the transition from depression to potentiation is of the utmost importance in determining the consequences of STDP, suggesting that this region warrants further experimental study
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