On the relation between encoding and decoding of neuronal spikes

@article{Koyama2012OnTR,
  title={On the relation between encoding and decoding of neuronal spikes},
  author={Shinsuke Koyama},
  journal={BMC Neuroscience},
  year={2012},
  volume={12},
  pages={P177 - P177}
}
Neural coding is a field of study that concerns how sensory information is represented in the brain by networks of neurons. The link between external stimulus and neural response can be studied from two parallel points of view. The first, neural encoding, refers to the mapping from stimulus to response. It focuses primarily on understanding how neurons respond to a wide variety of stimuli and constructing models that accurately describe the stimulus-response relationship. Neural decoding refers… 
Spatial and Temporal Organization of Composite Receptive Fields in the Songbird Auditory Forebrain
TLDR
The anatomical organization and temporal activation patterns of auditory CRFs in European starlings exposed to natural vocal communication signals are investigated and it is estimated that a nearly complete representation of any conspecific song, regardless of length, can be obtained by evaluating populations as small as 100 neurons.
Spike encoding for pattern recognition: Comparing cerebellum granular layer encoding and BSA algorithms
TLDR
A new spike encoding model inspired from cerebellum granular layer is developed and compared it with BSA encoding techniques and the efficiency of the encoded dataset is compared with different datasets and with standard machine learning algorithms.
Simultaneous silence organizes structured higher-order interactions in neural populations
TLDR
It is reported that simultaneous silence (SS) of neurons concisely summarizes neural HOIs and is suggested to be a ubiquitous feature of HOIs that constrain neural activity patterns and can influence information processing.
Time domain analysis of microstrip line network simulating a natural neuron
TLDR
The main result of this research is the proving that the dendritic tree has a resonant response; phenomenon observed in recent years by neuroscience researchers.
Modeling of bio-nano communication networks for the human body
TLDR
A conceptual network model is proposed to express the signal transmission from the Peripheral Nervous System to the Central Nerve System and a network architecture based on the properties of calcium signaling is envisaged, which will facilitate disease monitoring and treatment.
Erratum: Simultaneous silence organizes structured higher-order interactions in neural populations
TLDR
This research presents a novel and scalable approach called “Smart Cassandra” that combines reinforcement learning and reinforcement learning to solve the challenge of Alzheimer’s disease.
Dissociating the role of interneurons in sensory processing
................................................................................................................................................................. vi Resumo

References

SHOWING 1-10 OF 33 REFERENCES
Spikes: Exploring the Neural Code
TLDR
Spikes provides a self-contained review of relevant concepts in information theory and statistical decision theory about the representation of sensory signals in neural spike trains and a quantitative framework is used to pose precise questions about the structure of the neural code.
Prediction and Decoding of Retinal Ganglion Cell Responses with a Probabilistic Spiking Model
TLDR
The fitted model predicts the detailed time structure of responses to novel stimuli, accurately capturing the interaction between the spiking history and sensory stimulus selectivity, and can be used to derive an explicit, maximum-likelihood decoding rule for neural spike trains.
Capacity of a Single Spiking Neuron Channel
TLDR
Numerical results are in a plausible range based on biological evidence to date and theoretical studies prove that the distribution of inputs, which achieves channel capacity, is a discrete distribution with finite mass points for temporal and rate coding under a reasonable assumption.
Ruling out and ruling in neural codes
TLDR
The results show that standard coarse coding (spike count coding) is insufficient; finer, more information-rich codes are necessary.
Mismatched Decoding in the Brain
TLDR
A general framework for investigating to what extent the decoding process in the brain can be simplified is developed and an information theoretic quantity, I*, is introduced, which was derived by extending the mutual information in terms of communication rate across a channel.
Temporal encoding in nervous systems: A rigorous definition
We propose a rigorous definition for the termtemporal encoding as it is applied to schemes for the representation of information withinpatterns of neuronal action potentials, and distinguish temporal
A Spike-Train Probability Model
TLDR
This work proposes a simple model, which is to assume that the time at which a neuron fires is determined probabilistically by, and only by, two quantities: the experimental clock time and the elapsed time since the previous spike.
Synergy, Redundancy, and Independence in Population Codes, Revisited
TLDR
It is shown that synergy and ΔIshuffled are confounded measures: they can be zero when correlations are clearly important for decoding and positive when they are not; in contrast, ΔI is not confounded, and has an information theoretic interpretation.
Optimal Short-Term Population Coding: When Fisher Information Fails
TLDR
It is demonstrated that the optimal width of gaussian tuning curves depends on the available decoding time T, and it turns out that the shape of a Fisher-optimal coding scheme is not unique, and this ambiguity is solved by taking the minimum mean square error into account.
...
...