9,156 research outputs found
Sequential Desynchronization in Networks of Spiking Neurons with Partial Reset
The response of a neuron to synaptic input strongly depends on whether or not
it has just emitted a spike. We propose a neuron model that after spike
emission exhibits a partial response to residual input charges and study its
collective network dynamics analytically. We uncover a novel desynchronization
mechanism that causes a sequential desynchronization transition: In globally
coupled neurons an increase in the strength of the partial response induces a
sequence of bifurcations from states with large clusters of synchronously
firing neurons, through states with smaller clusters to completely asynchronous
spiking. We briefly discuss key consequences of this mechanism for more general
networks of biophysical neurons
Synchronization of electrically coupled resonate-and-fire neurons
Electrical coupling between neurons is broadly present across brain areas and
is typically assumed to synchronize network activity. However, intrinsic
properties of the coupled cells can complicate this simple picture. Many cell
types with strong electrical coupling have been shown to exhibit resonant
properties, and the subthreshold fluctuations arising from resonance are
transmitted through electrical synapses in addition to action potentials. Using
the theory of weakly coupled oscillators, we explore the effect of both
subthreshold and spike-mediated coupling on synchrony in small networks of
electrically coupled resonate-and-fire neurons, a hybrid neuron model with
linear subthreshold dynamics and discrete post-spike reset. We calculate the
phase response curve using an extension of the adjoint method that accounts for
the discontinuity in the dynamics. We find that both spikes and resonant
subthreshold fluctuations can jointly promote synchronization. The subthreshold
contribution is strongest when the voltage exhibits a significant post-spike
elevation in voltage, or plateau. Additionally, we show that the geometry of
trajectories approaching the spiking threshold causes a "reset-induced shear"
effect that can oppose synchrony in the presence of network asymmetry, despite
having no effect on the phase-locking of symmetrically coupled pairs
Resonate and Fire Neuron with Fixed Magnetic Skyrmions
In the brain, the membrane potential of many neurons oscillates in a
subthreshold damped fashion and fire when excited by an input frequency that
nearly equals their eigen frequency. In this work, we investigate theoretically
the artificial implementation of such "resonate-and-fire" neurons by utilizing
the magnetization dynamics of a fixed magnetic skyrmion in the free layer of a
magnetic tunnel junction (MTJ). To realize firing of this nanomagnetic
implementation of an artificial neuron, we propose to employ voltage control of
magnetic anisotropy or voltage generated strain as an input (spike or
sinusoidal) signal, which modulates the perpendicular magnetic anisotropy
(PMA). This results in continual expansion and shrinking (i.e. breathing) of a
skyrmion core that mimics the subthreshold oscillation. Any subsequent input
pulse having an interval close to the breathing period or a sinusoidal input
close to the eigen frequency drives the magnetization dynamics of the fixed
skyrmion in a resonant manner. The time varying electrical resistance of the
MTJ layer due to this resonant oscillation of the skyrmion core is used to
drive a Complementary Metal Oxide Semiconductor (CMOS) buffer circuit, which
produces spike outputs. By rigorous micromagnetic simulation, we investigate
the interspike timing dependence and response to different excitatory and
inhibitory incoming input pulses. Finally, we show that such resonate and fire
neurons have potential application in coupled nanomagnetic oscillator based
associative memory arrays
Soma-Axon Coupling Configurations That Enhance Neuronal Coincidence Detection
Coincidence detector neurons transmit timing information by responding preferentially to concurrent synaptic inputs. Principal cells of the medial superior olive (MSO) in the mammalian auditory brainstem are superb coincidence detectors. They encode sound source location with high temporal precision, distinguishing submillisecond timing differences among inputs. We investigate computationally how dynamic coupling between the input region (soma and dendrite) and the spike-generating output region (axon and axon initial segment) can enhance coincidence detection in MSO neurons. To do this, we formulate a two-compartment neuron model and characterize extensively coincidence detection sensitivity throughout a parameter space of coupling configurations. We focus on the interaction between coupling configuration and two currents that provide dynamic, voltage-gated, negative feedback in subthreshold voltage range: sodium current with rapid inactivation and low-threshold potassium current, IKLT. These currents reduce synaptic summation and can prevent spike generation unless inputs arrive with near simultaneity. We show that strong soma-to-axon coupling promotes the negative feedback effects of sodium inactivation and is, therefore, advantageous for coincidence detection. Furthermore, the feedforward combination of strong soma-to-axon coupling and weak axon-to-soma coupling enables spikes to be generated efficiently (few sodium channels needed) and with rapid recovery that enhances high-frequency coincidence detection. These observations detail the functional benefit of the strongly feedforward configuration that has been observed in physiological studies of MSO neurons. We find that IKLT further enhances coincidence detection sensitivity, but with effects that depend on coupling configuration. For instance, in models with weak soma-to-axon and weak axon-to-soma coupling, IKLT in the axon enhances coincidence detection more effectively than IKLT in the soma. By using a minimal model of soma-to-axon coupling, we connect structure, dynamics, and computation. Although we consider the particular case of MSO coincidence detectors, our method for creating and exploring a parameter space of two-compartment models can be applied to other neurons
Intrinsically-generated fluctuating activity in excitatory-inhibitory networks
Recurrent networks of non-linear units display a variety of dynamical regimes
depending on the structure of their synaptic connectivity. A particularly
remarkable phenomenon is the appearance of strongly fluctuating, chaotic
activity in networks of deterministic, but randomly connected rate units. How
this type of intrinsi- cally generated fluctuations appears in more realistic
networks of spiking neurons has been a long standing question. To ease the
comparison between rate and spiking networks, recent works investigated the
dynami- cal regimes of randomly-connected rate networks with segregated
excitatory and inhibitory populations, and firing rates constrained to be
positive. These works derived general dynamical mean field (DMF) equations
describing the fluctuating dynamics, but solved these equations only in the
case of purely inhibitory networks. Using a simplified excitatory-inhibitory
architecture in which DMF equations are more easily tractable, here we show
that the presence of excitation qualitatively modifies the fluctuating activity
compared to purely inhibitory networks. In presence of excitation,
intrinsically generated fluctuations induce a strong increase in mean firing
rates, a phenomenon that is much weaker in purely inhibitory networks.
Excitation moreover induces two different fluctuating regimes: for moderate
overall coupling, recurrent inhibition is sufficient to stabilize fluctuations,
for strong coupling, firing rates are stabilized solely by the upper bound
imposed on activity, even if inhibition is stronger than excitation. These
results extend to more general network architectures, and to rate networks
receiving noisy inputs mimicking spiking activity. Finally, we show that
signatures of the second dynamical regime appear in networks of
integrate-and-fire neurons
Controlling the spontaneous spiking regularity via channel blocking on Newman-Watts networks of Hodgkin-Huxley neurons
We investigate the regularity of spontaneous spiking activity on Newman-Watts
small-world networks consisting of biophysically realistic Hodgkin-Huxley
neurons with a tunable intensity of intrinsic noise and fraction of blocked
voltage-gated sodium and potassium ion channels embedded in neuronal membranes.
We show that there exists an optimal fraction of shortcut links between
physically distant neurons, as well as an optimal intensity of intrinsic noise,
which warrant an optimally ordered spontaneous spiking activity. This doubly
coherence resonance-like phenomenon depends significantly, and can be
controlled via the fraction of closed sodium and potassium ion channels,
whereby the impacts can be understood via the analysis of the firing rate
function as well as the deterministic system dynamics. Potential biological
implications of our findings for information propagation across neural networks
are also discussed.Comment: 6 two-column pages, 5 figures; accepted for publication in
Europhysics Letter
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