1,765 research outputs found
The general anaesthetic etomidate inhibits the excitability of mouse thalamocortical relay neurons by modulating multiple modes of GABA<sub>A</sub> receptor-mediated inhibition
Modulation of thalamocortical (TC) relay neuron function has been implicated in the sedative and hypnotic effects of general anaesthetics. Inhibition of TC neurons is mediated predominantly by a combination of phasic and tonic inhibition, together with a recently described ‘spillover’ mode of inhibition, generated by the dynamic recruitment of extrasynaptic γ-aminobutyric acid (GABA)(A) receptors (GABA(A)Rs). Previous studies demonstrated that the intravenous anaesthetic etomidate enhances tonic and phasic inhibition in TC relay neurons, but it is not known how etomidate may influence spillover inhibition. Moreover, it is unclear how etomidate influences the excitability of TC neurons. Thus, to investigate the relative contribution of synaptic (α1β2γ2) and extrasynaptic (α4β2δ) GABA(A)Rs to the thalamic effects of etomidate, we performed whole-cell recordings from mouse TC neurons lacking synaptic (α1(0/0)) or extrasynaptic (δ(0/0)) GABA(A)Rs. Etomidate (3 μm) significantly inhibited action-potential discharge in a manner that was dependent on facilitation of both synaptic and extrasynaptic GABA(A)Rs, although enhanced tonic inhibition was dominant in this respect. Additionally, phasic inhibition evoked by stimulation of the nucleus reticularis exhibited a spillover component mediated by δ-GABA(A)Rs, which was significantly prolonged in the presence of etomidate. Thus, etomidate greatly enhanced the transient suppression of TC spike trains by evoked inhibitory postsynaptic potentials. Collectively, these results suggest that the deactivation of thalamus observed during etomidate-induced anaesthesia involves potentiation of tonic and phasic inhibition, and implicate amplification of spillover inhibition as a novel mechanism to regulate the gating of sensory information through the thalamus during anaesthetic states
Homeostatic plasticity and external input shape neural network dynamics
In vitro and in vivo spiking activity clearly differ. Whereas networks in
vitro develop strong bursts separated by periods of very little spiking
activity, in vivo cortical networks show continuous activity. This is puzzling
considering that both networks presumably share similar single-neuron dynamics
and plasticity rules. We propose that the defining difference between in vitro
and in vivo dynamics is the strength of external input. In vitro, networks are
virtually isolated, whereas in vivo every brain area receives continuous input.
We analyze a model of spiking neurons in which the input strength, mediated by
spike rate homeostasis, determines the characteristics of the dynamical state.
In more detail, our analytical and numerical results on various network
topologies show consistently that under increasing input, homeostatic
plasticity generates distinct dynamic states, from bursting, to
close-to-critical, reverberating and irregular states. This implies that the
dynamic state of a neural network is not fixed but can readily adapt to the
input strengths. Indeed, our results match experimental spike recordings in
vitro and in vivo: the in vitro bursting behavior is consistent with a state
generated by very low network input (< 0.1%), whereas in vivo activity suggests
that on the order of 1% recorded spikes are input-driven, resulting in
reverberating dynamics. Importantly, this predicts that one can abolish the
ubiquitous bursts of in vitro preparations, and instead impose dynamics
comparable to in vivo activity by exposing the system to weak long-term
stimulation, thereby opening new paths to establish an in vivo-like assay in
vitro for basic as well as neurological studies.Comment: 14 pages, 8 figures, accepted at Phys. Rev.
A unified view on weakly correlated recurrent networks
The diversity of neuron models used in contemporary theoretical neuroscience
to investigate specific properties of covariances raises the question how these
models relate to each other. In particular it is hard to distinguish between
generic properties and peculiarities due to the abstracted model. Here we
present a unified view on pairwise covariances in recurrent networks in the
irregular regime. We consider the binary neuron model, the leaky
integrate-and-fire model, and the Hawkes process. We show that linear
approximation maps each of these models to either of two classes of linear rate
models, including the Ornstein-Uhlenbeck process as a special case. The classes
differ in the location of additive noise in the rate dynamics, which is on the
output side for spiking models and on the input side for the binary model. Both
classes allow closed form solutions for the covariance. For output noise it
separates into an echo term and a term due to correlated input. The unified
framework enables us to transfer results between models. For example, we
generalize the binary model and the Hawkes process to the presence of
conduction delays and simplify derivations for established results. Our
approach is applicable to general network structures and suitable for
population averages. The derived averages are exact for fixed out-degree
network architectures and approximate for fixed in-degree. We demonstrate how
taking into account fluctuations in the linearization procedure increases the
accuracy of the effective theory and we explain the class dependent differences
between covariances in the time and the frequency domain. Finally we show that
the oscillatory instability emerging in networks of integrate-and-fire models
with delayed inhibitory feedback is a model-invariant feature: the same
structure of poles in the complex frequency plane determines the population
power spectra
Adaptation Reduces Variability of the Neuronal Population Code
Sequences of events in noise-driven excitable systems with slow variables
often show serial correlations among their intervals of events. Here, we employ
a master equation for general non-renewal processes to calculate the interval
and count statistics of superimposed processes governed by a slow adaptation
variable. For an ensemble of spike-frequency adapting neurons this results in
the regularization of the population activity and an enhanced post-synaptic
signal decoding. We confirm our theoretical results in a population of cortical
neurons.Comment: 4 pages, 2 figure
Conditions for wave trains in spiking neural networks
Spatiotemporal patterns such as traveling waves are frequently observed in
recordings of neural activity. The mechanisms underlying the generation of such
patterns are largely unknown. Previous studies have investigated the existence
and uniqueness of different types of waves or bumps of activity using
neural-field models, phenomenological coarse-grained descriptions of
neural-network dynamics. But it remains unclear how these insights can be
transferred to more biologically realistic networks of spiking neurons, where
individual neurons fire irregularly. Here, we employ mean-field theory to
reduce a microscopic model of leaky integrate-and-fire (LIF) neurons with
distance-dependent connectivity to an effective neural-field model. In contrast
to existing phenomenological descriptions, the dynamics in this neural-field
model depends on the mean and the variance in the synaptic input, both
determining the amplitude and the temporal structure of the resulting effective
coupling kernel. For the neural-field model we employ liner stability analysis
to derive conditions for the existence of spatial and temporal oscillations and
wave trains, that is, temporally and spatially periodic traveling waves. We
first prove that wave trains cannot occur in a single homogeneous population of
neurons, irrespective of the form of distance dependence of the connection
probability. Compatible with the architecture of cortical neural networks, wave
trains emerge in two-population networks of excitatory and inhibitory neurons
as a combination of delay-induced temporal oscillations and spatial
oscillations due to distance-dependent connectivity profiles. Finally, we
demonstrate quantitative agreement between predictions of the analytically
tractable neural-field model and numerical simulations of both networks of
nonlinear rate-based units and networks of LIF neurons.Comment: 36 pages, 8 figures, 4 table
Fast and deep: energy-efficient neuromorphic learning with first-spike times
For a biological agent operating under environmental pressure, energy
consumption and reaction times are of critical importance. Similarly,
engineered systems also strive for short time-to-solution and low
energy-to-solution characteristics. At the level of neuronal implementation,
this implies achieving the desired results with as few and as early spikes as
possible. In the time-to-first-spike-coding framework, both of these goals are
inherently emerging features of learning. Here, we describe a rigorous
derivation of learning such first-spike times in networks of leaky
integrate-and-fire neurons, relying solely on input and output spike times, and
show how it can implement error backpropagation in hierarchical spiking
networks. Furthermore, we emulate our framework on the BrainScaleS-2
neuromorphic system and demonstrate its capability of harnessing the chip's
speed and energy characteristics. Finally, we examine how our approach
generalizes to other neuromorphic platforms by studying how its performance is
affected by typical distortive effects induced by neuromorphic substrates.Comment: 20 pages, 8 figure
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