# Analog and digital codes in the brain.

@article{Mochizuki2013AnalogAD, title={Analog and digital codes in the brain.}, author={Yasuhiro Mochizuki and Shigeru Shinomoto}, journal={Physical review. E, Statistical, nonlinear, and soft matter physics}, year={2013}, volume={89 2}, pages={ 022705 } }

It has long been debated whether information in the brain is coded at the rate of neuronal spiking or at the precise timing of single spikes. Although this issue is essential to the understanding of neural signal processing, it is not easily resolved because the two mechanisms are not mutually exclusive. We suggest revising this coding issue so that one hypothesis is uniquely selected for a given spike train. To this end, we decide whether the spike train is likely to transmit a continuously…

## 26 Citations

### Accuracy of rate coding: When shorter time window and higher spontaneous activity help.

- Computer SciencePhysical review. E
- 2017

An analysis based on the number of observed spikes assuming the stochastic perfect integrate-and-fire model with a change point, representing the stimulus onset, shows that the Fisher information is nonmonotonic with respect to the length of the observation period, and observes that the signal can be enhanced by noise.

### Information Transmission Using Non-Poisson Regular Firing

- Computer ScienceNeural Computation
- 2013

This study investigates a possible benefit of non-Poisson spiking for information transmission by studying the minimal rate fluctuation that can be detected by a Bayesian estimator and finds that the threshold for detection is reduced in proportion to the coefficient of variation of interspike intervals.

### Efficient information transfer by Poisson neurons.

- Computer ScienceMathematical biosciences and engineering : MBE
- 2016

This contribution employs the classical information-theoretic results to analyze the efficiency of such a transmission from different perspectives, emphasising the neurobiological viewpoint, and addresses both the ultimate limits and achievable bounds on performance at rates below capacity with fixed decoding error probability.

### Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS)

- Computer SciencebioRxiv
- 2017

A new method for estimating firing rate based on kernel smoothing technique that considers the bandwidth as a random variable with prior distribution that is adaptively updated under a Bayesian framework is developed.

### Solving a classification task by spiking neurons with STDP and temporal coding

- Computer ScienceBICA
- 2017

### The Phonetics-Phonology Relationship in the Neurobiology of Language

- BiologybioRxiv
- 2017

This work develops a preliminary proposal where discretization and phonological abstraction are the result of a continuous process that converts spectro-temporal (acoustic) states into neurophysiological states such that some properties of the former undergo changes interacting with the latter until a new equilibrium is reached.

### Analog-Digital Approach in Human Brain Modeling

- Computer Science2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)
- 2017

In current collaboration, some results of study of activity of human brain are incorporated as a base of building of hybrid computational system and foundation to the approach of running it.

### Inferring the collective dynamics of neuronal populations from single-trial spike trains using mechanistic models

- BiologybioRxiv
- 2019

A statistically principled approach based on a population of doubly-stochastic integrate-and-fire neurons, taking into account basic biophysics is presented, providing statistical inference tools for a class of reasonably constrained, mechanistic models.

### A Memristor-Based Spiking Neural Network With High Scalability and Learning Efficiency

- Computer ScienceIEEE Transactions on Circuits and Systems II: Express Briefs
- 2020

A novel SNN using memristor-based inhibitory synapses to realize the mechanisms of lateral inhibition and homeostasis with low hardware complexity is proposed and achieves a ~ 2 times higher learning efficiency with comparable accuracy.

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