3,103 research outputs found
Information transmission in oscillatory neural activity
Periodic neural activity not locked to the stimulus or to motor responses is
usually ignored. Here, we present new tools for modeling and quantifying the
information transmission based on periodic neural activity that occurs with
quasi-random phase relative to the stimulus. We propose a model to reproduce
characteristic features of oscillatory spike trains, such as histograms of
inter-spike intervals and phase locking of spikes to an oscillatory influence.
The proposed model is based on an inhomogeneous Gamma process governed by a
density function that is a product of the usual stimulus-dependent rate and a
quasi-periodic function. Further, we present an analysis method generalizing
the direct method (Rieke et al, 1999; Brenner et al, 2000) to assess the
information content in such data. We demonstrate these tools on recordings from
relay cells in the lateral geniculate nucleus of the cat.Comment: 18 pages, 8 figures, to appear in Biological Cybernetic
Measuring spike train synchrony
Estimating the degree of synchrony or reliability between two or more spike
trains is a frequent task in both experimental and computational neuroscience.
In recent years, many different methods have been proposed that typically
compare the timing of spikes on a certain time scale to be fixed beforehand.
Here, we propose the ISI-distance, a simple complementary approach that
extracts information from the interspike intervals by evaluating the ratio of
the instantaneous frequencies. The method is parameter free, time scale
independent and easy to visualize as illustrated by an application to real
neuronal spike trains obtained in vitro from rat slices. In a comparison with
existing approaches on spike trains extracted from a simulated Hindemarsh-Rose
network, the ISI-distance performs as well as the best time-scale-optimized
measure based on spike timing.Comment: 11 pages, 13 figures; v2: minor modifications; v3: minor
modifications, added link to webpage that includes the Matlab Source Code for
the method (http://inls.ucsd.edu/~kreuz/Source-Code/Spike-Sync.html
Applications of Information Theory to Analysis of Neural Data
Information theory is a practical and theoretical framework developed for the
study of communication over noisy channels. Its probabilistic basis and
capacity to relate statistical structure to function make it ideally suited for
studying information flow in the nervous system. It has a number of useful
properties: it is a general measure sensitive to any relationship, not only
linear effects; it has meaningful units which in many cases allow direct
comparison between different experiments; and it can be used to study how much
information can be gained by observing neural responses in single trials,
rather than in averages over multiple trials. A variety of information
theoretic quantities are commonly used in neuroscience - (see entry
"Definitions of Information-Theoretic Quantities"). In this entry we review
some applications of information theory in neuroscience to study encoding of
information in both single neurons and neuronal populations.Comment: 8 pages, 2 figure
Neurons with stereotyped and rapid responses provide a reference frame for relative temporal coding in primate auditory cortex
The precise timing of spikes of cortical neurons relative to stimulus onset carries substantial sensory information. To access this information the sensory systems would need to maintain an internal temporal reference that reflects the precise stimulus timing. Whether and how sensory systems implement such reference frames to decode time-dependent responses, however, remains debated. Studying the encoding of naturalistic sounds in primate (Macaca mulatta) auditory cortex we here investigate potential intrinsic references for decoding temporally precise information. Within the population of recorded neurons, we found one subset responding with stereotyped fast latencies that varied little across trials or stimuli, while the remaining neurons had stimulus-modulated responses with longer and variable latencies. Computational analysis demonstrated that the neurons with stereotyped short latencies constitute an effective temporal reference for relative coding. Using the response onset of a simultaneously recorded stereotyped neuron allowed decoding most of the stimulus information carried by onset latencies and the full spike train of stimulus-modulated neurons. Computational modeling showed that few tens of such stereotyped reference neurons suffice to recover nearly all information that would be available when decoding the same responses relative to the actual stimulus onset. These findings reveal an explicit neural signature of an intrinsic reference for decoding temporal response patterns in the auditory cortex of alert animals. Furthermore, they highlight a role for apparently unselective neurons as an early saliency signal that provides a temporal reference for extracting stimulus information from other neurons
Training Multi-layer Spiking Neural Networks using NormAD based Spatio-Temporal Error Backpropagation
Spiking neural networks (SNNs) have garnered a great amount of interest for
supervised and unsupervised learning applications. This paper deals with the
problem of training multi-layer feedforward SNNs. The non-linear
integrate-and-fire dynamics employed by spiking neurons make it difficult to
train SNNs to generate desired spike trains in response to a given input. To
tackle this, first the problem of training a multi-layer SNN is formulated as
an optimization problem such that its objective function is based on the
deviation in membrane potential rather than the spike arrival instants. Then,
an optimization method named Normalized Approximate Descent (NormAD),
hand-crafted for such non-convex optimization problems, is employed to derive
the iterative synaptic weight update rule. Next, it is reformulated to
efficiently train multi-layer SNNs, and is shown to be effectively performing
spatio-temporal error backpropagation. The learning rule is validated by
training -layer SNNs to solve a spike based formulation of the XOR problem
as well as training -layer SNNs for generic spike based training problems.
Thus, the new algorithm is a key step towards building deep spiking neural
networks capable of efficient event-triggered learning.Comment: 19 pages, 10 figure
Critical Changes in Cortical Neuronal Interactions in Anesthetized and Awake Rats
Background: Neuronal interactions are fundamental for information processing, cognition and consciousness. Anesthetics reduce spontaneous cortical activity; however, neuronal reactivity to sensory stimuli is often preserved or augmented. How sensory stimulus-related neuronal interactions change under anesthesia has not been elucidated. Here we investigated visual stimulus-related cortical neuronal interactions during stepwise emergence from desflurane anesthesia. Methods: Parallel spike trains were recorded with 64-contact extracellular microelectrode arrays from the primary visual cortex of chronically instrumented, unrestrained rats (N=6) at 8%, 6%, 4%, 2% desflurane anesthesia and wakefulness. Light flashes were delivered to the retina by transcranial illumination at 5-15s randomized intervals. Information theoretical indices, integration and interaction complexity, were calculated from the probability distribution of coincident spike patterns and used to quantify neuronal interactions before and after flash stimulation. Results: Integration and complexity showed significant negative associations with desflurane concentration (N=60). Flash stimulation increased integration and complexity at all anesthetic levels (N=60); the effect on complexity was reduced in wakefulness. During stepwise withdrawal of desflurane, the largest increase in integration (74%) and post-stimulus complexity (35%) occurred prior to reaching 4% desflurane concentration – a level associated with the recovery of consciousness according to the rats\u27 righting reflex. Conclusions: Neuronal interactions in the cerebral cortex are augmented during emergence from anesthesia. Visual flash stimuli enhance neuronal interactions in both wakefulness and anesthesia; the increase in interaction complexity is attenuated as post-stimulus complexity reaches plateau. The critical changes in cortical neuronal interactions occur during transition to consciousness
PySpike - A Python library for analyzing spike train synchrony
Understanding how the brain functions is one of the biggest challenges of our
time. The analysis of experimentally recorded neural firing patterns (spike
trains) plays a crucial role in addressing this problem. Here, the PySpike
library is introduced, a Python package for spike train analysis providing
parameter-free and time-scale independent measures of spike train synchrony. It
allows to compute similarity and dissimilarity profiles, averaged values and
distance matrices. Although mainly focusing on neuroscience, PySpike can also
be applied in other contexts like climate research or social sciences. The
package is available as Open Source on Github and PyPI.Comment: 7 pages, 6 figure
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