2,909 research outputs found
The impact of spike timing variability on the signal-encoding performance of neural spiking models
It remains unclear whether the variability of neuronal spike trains in vivo arises due to biological noise sources or represents highly precise encoding of temporally varying synaptic input signals. Determining the variability of spike timing can provide fundamental insights into the nature of strategies used in the brain to represent and transmit information in the form of discrete spike trains. In this study, we employ a signal estimation paradigm to determine how variability in spike timing affects encoding of random time-varying signals. We assess this for two types of spiking models: an integrate-and-fire model with random threshold and a more biophysically realistic stochastic ion channel model. Using the coding fraction and mutual information as information-theoretic measures, we quantify the efficacy of optimal linear decoding of random inputs from the model outputs and study the relationship between efficacy and variability in the output spike train. Our findings suggest that variability does not necessarily hinder signal decoding for the biophysically plausible encoders examined and that the functional role of spiking variability depends intimately on the nature of the encoder and the signal processing task; variability can either enhance or impede decoding performance
Signal Propagation in Feedforward Neuronal Networks with Unreliable Synapses
In this paper, we systematically investigate both the synfire propagation and
firing rate propagation in feedforward neuronal network coupled in an
all-to-all fashion. In contrast to most earlier work, where only reliable
synaptic connections are considered, we mainly examine the effects of
unreliable synapses on both types of neural activity propagation in this work.
We first study networks composed of purely excitatory neurons. Our results show
that both the successful transmission probability and excitatory synaptic
strength largely influence the propagation of these two types of neural
activities, and better tuning of these synaptic parameters makes the considered
network support stable signal propagation. It is also found that noise has
significant but different impacts on these two types of propagation. The
additive Gaussian white noise has the tendency to reduce the precision of the
synfire activity, whereas noise with appropriate intensity can enhance the
performance of firing rate propagation. Further simulations indicate that the
propagation dynamics of the considered neuronal network is not simply
determined by the average amount of received neurotransmitter for each neuron
in a time instant, but also largely influenced by the stochastic effect of
neurotransmitter release. Second, we compare our results with those obtained in
corresponding feedforward neuronal networks connected with reliable synapses
but in a random coupling fashion. We confirm that some differences can be
observed in these two different feedforward neuronal network models. Finally,
we study the signal propagation in feedforward neuronal networks consisting of
both excitatory and inhibitory neurons, and demonstrate that inhibition also
plays an important role in signal propagation in the considered networks.Comment: 33pages, 16 figures; Journal of Computational Neuroscience
(published
Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses
Spiking neural networks (SNN) are artificial computational models that have
been inspired by the brain's ability to naturally encode and process
information in the time domain. The added temporal dimension is believed to
render them more computationally efficient than the conventional artificial
neural networks, though their full computational capabilities are yet to be
explored. Recently, computational memory architectures based on non-volatile
memory crossbar arrays have shown great promise to implement parallel
computations in artificial and spiking neural networks. In this work, we
experimentally demonstrate for the first time, the feasibility to realize
high-performance event-driven in-situ supervised learning systems using
nanoscale and stochastic phase-change synapses. Our SNN is trained to recognize
audio signals of alphabets encoded using spikes in the time domain and to
generate spike trains at precise time instances to represent the pixel
intensities of their corresponding images. Moreover, with a statistical model
capturing the experimental behavior of the devices, we investigate
architectural and systems-level solutions for improving the training and
inference performance of our computational memory-based system. Combining the
computational potential of supervised SNNs with the parallel compute power of
computational memory, the work paves the way for next-generation of efficient
brain-inspired systems
Statistical-Mechanical Measure of Stochastic Spiking Coherence in A Population of Inhibitory Subthreshold Neurons
By varying the noise intensity, we study stochastic spiking coherence (i.e.,
collective coherence between noise-induced neural spikings) in an inhibitory
population of subthreshold neurons (which cannot fire spontaneously without
noise). This stochastic spiking coherence may be well visualized in the raster
plot of neural spikes. For a coherent case, partially-occupied "stripes"
(composed of spikes and indicating collective coherence) are formed in the
raster plot. This partial occupation occurs due to "stochastic spike skipping"
which is well shown in the multi-peaked interspike interval histogram. The main
purpose of our work is to quantitatively measure the degree of stochastic
spiking coherence seen in the raster plot. We introduce a new spike-based
coherence measure by considering the occupation pattern and the pacing
pattern of spikes in the stripes. In particular, the pacing degree between
spikes is determined in a statistical-mechanical way by quantifying the average
contribution of (microscopic) individual spikes to the (macroscopic)
ensemble-averaged global potential. This "statistical-mechanical" measure
is in contrast to the conventional measures such as the "thermodynamic" order
parameter (which concerns the time-averaged fluctuations of the macroscopic
global potential), the "microscopic" correlation-based measure (based on the
cross-correlation between the microscopic individual potentials), and the
measures of precise spike timing (based on the peri-stimulus time histogram).
In terms of , we quantitatively characterize the stochastic spiking
coherence, and find that reflects the degree of collective spiking
coherence seen in the raster plot very well. Hence, the
"statistical-mechanical" spike-based measure may be used usefully to
quantify the degree of stochastic spiking coherence in a statistical-mechanical
way.Comment: 16 pages, 5 figures, to appear in the J. Comput. Neurosc
Revisiting chaos in stimulus-driven spiking networks: signal encoding and discrimination
Highly connected recurrent neural networks often produce chaotic dynamics,
meaning their precise activity is sensitive to small perturbations. What are
the consequences for how such networks encode streams of temporal stimuli? On
the one hand, chaos is a strong source of randomness, suggesting that small
changes in stimuli will be obscured by intrinsically generated variability. On
the other hand, recent work shows that the type of chaos that occurs in spiking
networks can have a surprisingly low-dimensional structure, suggesting that
there may be "room" for fine stimulus features to be precisely resolved. Here
we show that strongly chaotic networks produce patterned spikes that reliably
encode time-dependent stimuli: using a decoder sensitive to spike times on
timescales of 10's of ms, one can easily distinguish responses to very similar
inputs. Moreover, recurrence serves to distribute signals throughout chaotic
networks so that small groups of cells can encode substantial information about
signals arriving elsewhere. A conclusion is that the presence of strong chaos
in recurrent networks does not prohibit precise stimulus encoding.Comment: 8 figure
Consequences of converting graded to action potentials upon neural information coding and energy efficiency
Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied by information loss and a drop in energy efficiency. We investigate the biophysical causes of this loss of information and efficiency by comparing spiking neuron models, containing stochastic voltage-gated Na+ and K+ channels, with generator potential and graded potential models lacking voltage-gated Na+ channels. We identify three causes of information loss in the generator potential that are the by-product of action potential generation: (1) the voltage-gated Na+ channels necessary for action potential generation increase intrinsic noise and (2) introduce non-linearities, and (3) the finite duration of the action potential creates a ‘footprint’ in the generator potential that obscures incoming signals. These three processes reduce information rates by ~50% in generator potentials, to ~3 times that of spike trains. Both generator potentials and graded potentials consume almost an order of magnitude less energy per second than spike trains. Because of the lower information rates of generator potentials they are substantially less energy efficient than graded potentials. However, both are an order of magnitude more efficient than spike trains due to the higher energy costs and low information content of spikes, emphasizing that there is a two-fold cost of converting analogue to digital; information loss and cost inflation
Transient Resetting: A Novel Mechanism for Synchrony and Its Biological Examples
The study of synchronization in biological systems is essential for the
understanding of the rhythmic phenomena of living organisms at both molecular
and cellular levels. In this paper, by using simple dynamical systems theory,
we present a novel mechanism, named transient resetting, for the
synchronization of uncoupled biological oscillators with stimuli. This
mechanism not only can unify and extend many existing results on (deterministic
and stochastic) stimulus-induced synchrony, but also may actually play an
important role in biological rhythms. We argue that transient resetting is a
possible mechanism for the synchronization in many biological organisms, which
might also be further used in medical therapy of rhythmic disorders. Examples
on the synchronization of neural and circadian oscillators are presented to
verify our hypothesis.Comment: 17 pages, 7 figure
Comparison of Langevin and Markov channel noise models for neuronal signal generation
The stochastic opening and closing of voltage-gated ion channels produces
noise in neurons. The effect of this noise on the neuronal performance has been
modelled using either approximate or Langevin model, based on stochastic
differential equations or an exact model, based on a Markov process model of
channel gating. Yet whether the Langevin model accurately reproduces the
channel noise produced by the Markov model remains unclear. Here we present a
comparison between Langevin and Markov models of channel noise in neurons using
single compartment Hodgkin-Huxley models containing either and
, or only voltage-gated ion channels. The performance of the
Langevin and Markov models was quantified over a range of stimulus statistics,
membrane areas and channel numbers. We find that in comparison to the Markov
model, the Langevin model underestimates the noise contributed by voltage-gated
ion channels, overestimating information rates for both spiking and non-spiking
membranes. Even with increasing numbers of channels the difference between the
two models persists. This suggests that the Langevin model may not be suitable
for accurately simulating channel noise in neurons, even in simulations with
large numbers of ion channels
Shaping bursting by electrical coupling and noise
Gap-junctional coupling is an important way of communication between neurons
and other excitable cells. Strong electrical coupling synchronizes activity
across cell ensembles. Surprisingly, in the presence of noise synchronous
oscillations generated by an electrically coupled network may differ
qualitatively from the oscillations produced by uncoupled individual cells
forming the network. A prominent example of such behavior is the synchronized
bursting in islets of Langerhans formed by pancreatic \beta-cells, which in
isolation are known to exhibit irregular spiking. At the heart of this
intriguing phenomenon lies denoising, a remarkable ability of electrical
coupling to diminish the effects of noise acting on individual cells.
In this paper, we derive quantitative estimates characterizing denoising in
electrically coupled networks of conductance-based models of square wave
bursting cells. Our analysis reveals the interplay of the intrinsic properties
of the individual cells and network topology and their respective contributions
to this important effect. In particular, we show that networks on graphs with
large algebraic connectivity or small total effective resistance are better
equipped for implementing denoising. As a by-product of the analysis of
denoising, we analytically estimate the rate with which trajectories converge
to the synchronization subspace and the stability of the latter to random
perturbations. These estimates reveal the role of the network topology in
synchronization. The analysis is complemented by numerical simulations of
electrically coupled conductance-based networks. Taken together, these results
explain the mechanisms underlying synchronization and denoising in an important
class of biological models
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