3,679 research outputs found
Bursts generate a non-reducible spike pattern code
At the single-neuron level, precisely timed spikes can either constitute
firing-rate codes or spike-pattern codes that utilize the relative timing
between consecutive spikes. There has been little experimental support for the
hypothesis that such temporal patterns contribute substantially to information
transmission. By using grasshopper auditory receptors as a model system, we
show that correlations between spikes can be used to represent behaviorally
relevant stimuli. The correlations reflect the inner structure of the spike
train: a succession of burst-like patterns. We demonstrate that bursts with
different spike counts encode different stimulus features, such that about 20%
of the transmitted information corresponds to discriminating between different
features, and the remaining 80% is used to allocate these features in time. In
this spike-pattern code, the what and the when of the stimuli are encoded in
the duration of each burst and the time of burst onset, respectively. Given the
ubiquity of burst firing, we expect similar findings also for other neural
systems
Neural population coding: combining insights from microscopic and mass signals
Behavior relies on the distributed and coordinated activity of neural populations. Population activity can be measured using multi-neuron recordings and neuroimaging. Neural recordings reveal how the heterogeneity, sparseness, timing, and correlation of population activity shape information processing in local networks, whereas neuroimaging shows how long-range coupling and brain states impact on local activity and perception. To obtain an integrated perspective on neural information processing we need to combine knowledge from both levels of investigation. We review recent progress of how neural recordings, neuroimaging, and computational approaches begin to elucidate how interactions between local neural population activity and large-scale dynamics shape the structure and coding capacity of local information representations, make them state-dependent, and control distributed populations that collectively shape behavior
I, NEURON: the neuron as the collective
Purpose – In the last half-century, individual sensory neurons have been bestowed with characteristics of the whole human being, such as behavior and its oft-presumed precursor, consciousness. This anthropomorphization is pervasive in the literature. It is also absurd, given what we know about neurons, and it needs to be abolished. This study aims to first understand how it happened, and hence why it persists.
Design/methodology/approach – The peer-reviewed sensory-neurophysiology literature extends to hundreds (perhaps thousands) of papers. Here, more than 90 mainstream papers were scrutinized.
Findings – Anthropomorphization arose because single neurons were cast as “observers” who “identify”, “categorize”, “recognize”, “distinguish” or “discriminate” the stimuli, using math-based algorithms that reduce (“decode”) the stimulus-evoked spike trains to the particular stimuli inferred to elicit them. Without “decoding”, there is supposedly no perception. However, “decoding” is both unnecessary and unconfirmed. The neuronal “observer” in fact consists of the laboratory staff and the greater society that supports them. In anthropomorphization, the neuron becomes the collective.
Research limitations/implications – Anthropomorphization underlies the widespread application to neurons Information Theory and Signal Detection Theory, making both approaches incorrect.
Practical implications – A great deal of time, money and effort has been wasted on anthropomorphic Reductionist approaches to understanding perception and consciousness. Those resources should be diverted into more-fruitful approaches.
Originality/value – A long-overdue scrutiny of sensory-neuroscience literature reveals that anthropomorphization, a form of Reductionism that involves the presumption of single-neuron consciousness, has run amok in neuroscience. Consciousness is more likely to be an emergent property of the brain
Multiscale relevance and informative encoding in neuronal spike trains
Neuronal responses to complex stimuli and tasks can encompass a wide range of
time scales. Understanding these responses requires measures that characterize
how the information on these response patterns are represented across multiple
temporal resolutions. In this paper we propose a metric -- which we call
multiscale relevance (MSR) -- to capture the dynamical variability of the
activity of single neurons across different time scales. The MSR is a
non-parametric, fully featureless indicator in that it uses only the time
stamps of the firing activity without resorting to any a priori covariate or
invoking any specific structure in the tuning curve for neural activity. When
applied to neural data from the mEC and from the ADn and PoS regions of
freely-behaving rodents, we found that neurons having low MSR tend to have low
mutual information and low firing sparsity across the correlates that are
believed to be encoded by the region of the brain where the recordings were
made. In addition, neurons with high MSR contain significant information on
spatial navigation and allow to decode spatial position or head direction as
efficiently as those neurons whose firing activity has high mutual information
with the covariate to be decoded and significantly better than the set of
neurons with high local variations in their interspike intervals. Given these
results, we propose that the MSR can be used as a measure to rank and select
neurons for their information content without the need to appeal to any a
priori covariate.Comment: 38 pages, 16 figure
Particle-filtering approaches for nonlinear Bayesian decoding of neuronal spike trains
The number of neurons that can be simultaneously recorded doubles every seven
years. This ever increasing number of recorded neurons opens up the possibility
to address new questions and extract higher dimensional stimuli from the
recordings. Modeling neural spike trains as point processes, this task of
extracting dynamical signals from spike trains is commonly set in the context
of nonlinear filtering theory. Particle filter methods relying on importance
weights are generic algorithms that solve the filtering task numerically, but
exhibit a serious drawback when the problem dimensionality is high: they are
known to suffer from the 'curse of dimensionality' (COD), i.e. the number of
particles required for a certain performance scales exponentially with the
observable dimensions. Here, we first briefly review the theory on filtering
with point process observations in continuous time. Based on this theory, we
investigate both analytically and numerically the reason for the COD of
weighted particle filtering approaches: Similarly to particle filtering with
continuous-time observations, the COD with point-process observations is due to
the decay of effective number of particles, an effect that is stronger when the
number of observable dimensions increases. Given the success of unweighted
particle filtering approaches in overcoming the COD for continuous- time
observations, we introduce an unweighted particle filter for point-process
observations, the spike-based Neural Particle Filter (sNPF), and show that it
exhibits a similar favorable scaling as the number of dimensions grows.
Further, we derive rules for the parameters of the sNPF from a maximum
likelihood approach learning. We finally employ a simple decoding task to
illustrate the capabilities of the sNPF and to highlight one possible future
application of our inference and learning algorithm
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
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