scholarly journals Temporal code versus rate code for binary Information Sources

2016 ◽  
Vol 216 ◽  
pp. 756-762 ◽  
Author(s):  
Agnieszka Pregowska ◽  
Janusz Szczepanski ◽  
Eligiusz Wajnryb
Keyword(s):  
Information Sources ◽  
Temporal Code ◽  
Rate Code ◽  
Binary Information ◽  
Code Versus

scholarly journals How Far can Neural Correlations Reduce Uncertainty? Comparison of Information Transmission Rates for Markov and Bernoulli Processes

2019 ◽  
Vol 29 (08) ◽  
pp. 1950003 ◽  
Author(s):  
Agnieszka Pregowska ◽  
Ehud Kaplan ◽  
Janusz Szczepanski
Keyword(s):  
Information Transmission ◽  
Crucial Role ◽  
Transition Probabilities ◽  
Temporal Code ◽  
Rate Code ◽  
Neural Codes ◽  
Loss Of Information ◽  
Transmission Rates ◽  
Firing Rates ◽  
Markov Source

The nature of neural codes is central to neuroscience. Do neurons encode information through relatively slow changes in the firing rates of individual spikes (rate code) or by the precise timing of every spike (temporal code)? Here we compare the loss of information due to correlations for these two possible neural codes. The essence of Shannon’s definition of information is to combine information with uncertainty: the higher the uncertainty of a given event, the more information is conveyed by that event. Correlations can reduce uncertainty or the amount of information, but by how much? In this paper we address this question by a direct comparison of the information per symbol conveyed by the words coming from a binary Markov source (temporal code) with the information per symbol coming from the corresponding Bernoulli source (uncorrelated, rate code). In a previous paper we found that a crucial role in the relation between information transmission rates (ITRs) and firing rates is played by a parameter [Formula: see text], which is the sum of transition probabilities from the no-spike state to the spike state and vice versa. We found that in this case too a crucial role is played by the same parameter [Formula: see text]. We calculated the maximal and minimal bounds of the quotient of ITRs for these sources. Next, making use of the entropy grouping axiom, we determined the loss of information in a Markov source compared with the information in the corresponding Bernoulli source for a given word length. Our results show that in the case of correlated signals the loss of information is relatively small, and thus temporal codes, which are more energetically efficient, can replace rate codes effectively. These results were confirmed by experiments.

scholarly journals Extracting Oscillations: Neuronal Coincidence Detection with Noisy Periodic Spike Input

1998 ◽  
Vol 10 (8) ◽  
pp. 1987-2017 ◽  
Author(s):  
Richard Kempter ◽  
Wulfram Gerstner ◽  
J. Leo van Hemmen ◽  
Hermann Wagner
Keyword(s):  
Firing Rate ◽  
Response Function ◽  
Coincidence Detection ◽  
Critical Parameters ◽  
Temporal Code ◽  
Rate Code ◽  
Neuronal Dynamics ◽  
Coincidence Detector ◽  
Integrate And Fire ◽  
Integrate And Fire Neuron

How does a neuron vary its mean output firing rate if the input changes from random to oscillatory coherent but noisy activity? What are the critical parameters of the neuronal dynamics and input statistics? To answer these questions, we investigate the coincidence-detection properties of an integrate-and-fire neuron. We derive an expression indicating how coincidence detection depends on neuronal parameters. Specifically, we show how coincidence detection depends on the shape of the postsynaptic response function, the number of synapses, and the input statistics, and we demonstrate that there is an optimal threshold. Our considerations can be used to predict from neuronal parameters whether and to what extent a neuron can act as a coincidence detector and thus can convert a temporal code into a rate code.

Rate code and temporal code for frequency of whisker stimulation in rat primary and secondary somatic sensory cortex

2006 ◽  
Vol 172 (3) ◽  
pp. 370-386 ◽  
Author(s):  
Peter Melzer ◽  
Gregory C. Champney ◽  
Mark J. Maguire ◽  
Ford F. Ebner
Keyword(s):  
Sensory Cortex ◽  
Temporal Code ◽  
Rate Code ◽  
Whisker Stimulation ◽  
Somatic Sensory Cortex

scholarly journals Rate code input produces temporal code output from cockroach antennal lobes

Biosystems ◽  
2000 ◽  
Vol 58 (1-3) ◽  
pp. 151-158 ◽  
Author(s):  
William C Lemon ◽  
Wayne M Getz
Keyword(s):  
Temporal Code ◽  
Rate Code ◽  
Antennal Lobes

scholarly journals Pitch of harmonic complex tones: rate and temporal coding of envelope repetition rate in inferior colliculus of unanesthetized rabbits

2019 ◽  
Vol 122 (6) ◽  
pp. 2468-2485 ◽  
Author(s):  
Yaqing Su ◽  
Bertrand Delgutte
Keyword(s):  
Inferior Colliculus ◽  
Repetition Rate ◽  
Temporal Coding ◽  
Broadband Noise ◽  
Temporal Code ◽  
Rate Code ◽  
Harmonic Complex ◽  
Phase Lock ◽  
Wide Range ◽  
Complex Tones

Harmonic complex tones (HCTs) found in speech, music, and animal vocalizations evoke strong pitch percepts at their fundamental frequencies. The strongest pitches are produced by HCTs that contain harmonics resolved by cochlear frequency analysis, but HCTs containing solely unresolved harmonics also evoke a weaker pitch at their envelope repetition rate (ERR). In the auditory periphery, neurons phase lock to the stimulus envelope, but this temporal representation of ERR degrades and gives way to rate codes along the ascending auditory pathway. To assess the role of the inferior colliculus (IC) in such transformations, we recorded IC neuron responses to HCT and sinusoidally modulated broadband noise (SAMN) with varying ERR from unanesthetized rabbits. Different interharmonic phase relationships of HCT were used to manipulate the temporal envelope without changing the power spectrum. Many IC neurons demonstrated band-pass rate tuning to ERR between 60 and 1,600 Hz for HCT and between 40 and 500 Hz for SAMN. The tuning was not related to the pure-tone best frequency of neurons but was dependent on the shape of the stimulus envelope, indicating a temporal rather than spectral origin. A phenomenological model suggests that the tuning may arise from peripheral temporal response patterns via synaptic inhibition. We also characterized temporal coding to ERR. Some IC neurons could phase lock to the stimulus envelope up to 900 Hz for either HCT or SAMN, but phase locking was weaker with SAMN. Together, the rate code and the temporal code represent a wide range of ERR, providing strong cues for the pitch of unresolved harmonics. NEW & NOTEWORTHY Envelope repetition rate (ERR) provides crucial cues for pitch perception of frequency components that are not individually resolved by the cochlea, but the neural representation of ERR for stimuli containing many harmonics is poorly characterized. Here we show that the pitch of stimuli with unresolved harmonics is represented by both a rate code and a temporal code for ERR in auditory midbrain neurons and propose possible underlying neural mechanisms with a computational model.

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