scholarly journals Neural burst codes disguised as rate codes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ezekiel Williams ◽  
Alexandre Payeur ◽  
Albert Gidon ◽  
Richard Naud
Keyword(s):  
Information Transmission ◽  
Information Transfer ◽  
Cell Types ◽  
Neural Code ◽  
Spike Timing ◽  
Coding Strategies ◽  
Spike Patterns ◽  
Interval Distribution ◽  
Rule Out ◽  
Firing Statistics

AbstractThe burst coding hypothesis posits that the occurrence of sudden high-frequency patterns of action potentials constitutes a salient syllable of the neural code. Many neurons, however, do not produce clearly demarcated bursts, an observation invoked to rule out the pervasiveness of this coding scheme across brain areas and cell types. Here we ask how detrimental ambiguous spike patterns, those that are neither clearly bursts nor isolated spikes, are for neuronal information transfer. We addressed this question using information theory and computational simulations. By quantifying how information transmission depends on firing statistics, we found that the information transmitted is not strongly influenced by the presence of clearly demarcated modes in the interspike interval distribution, a feature often used to identify the presence of burst coding. Instead, we found that neurons having unimodal interval distributions were still able to ascribe different meanings to bursts and isolated spikes. In this regime, information transmission depends on dynamical properties of the synapses as well as the length and relative frequency of bursts. Furthermore, we found that common metrics used to quantify burstiness were unable to predict the degree with which bursts could be used to carry information. Our results provide guiding principles for the implementation of coding strategies based on spike-timing patterns, and show that even unimodal firing statistics can be consistent with a bivariate neural code.

scholarly journals Burst coding despite unimodal interval distributions

2021 ◽  
Author(s):  
Ezekiel Williams ◽  
Alexandre Payeur ◽  
Albert Gidon ◽  
Richard Naud
Keyword(s):  
Information Transmission ◽  
Cell Types ◽  
Neural Code ◽  
Spike Timing ◽  
Coding Strategies ◽  
Transmission Of Information ◽  
Interspike Interval Distribution ◽  
Interval Distribution ◽  
Rule Out ◽  
Firing Statistics

The burst coding hypothesis posits that the occurrence of sudden high-frequency patterns of action potentials constitutes a salient syllable of the neural code. Many neurons, however, do not produce clearly demarcated bursts, an observation invoked to rule out the pervasiveness of this coding scheme across brain areas and cell types. Here we ask how identifiable spike-timing patterns have to be to preserve potent transmission of information. Should we expect that neurons avoid ambiguous patterns that are neither clearly bursts nor isolated spikes? We addressed these questions using information theory and computational simulations. By quantifying how information transmission depends on firing statistics, we found that the information transmitted is not strongly influenced by the presence of clearly demarcated modes in the interspike interval distribution, a feature often used to identify the presence of burst coding. Instead, we found that neurons having unimodal interval distributions were still able to ascribe different meanings to bursts and isolated spikes. In this regime, information transmission depends on properties of the synapses as well as the length and relative frequency of bursts. Furthermore, we found that common metrics used to quantify burstiness were also unable to predict the degree with which bursts could be used to carry information. Our results provide guiding principles for the implementation of coding strategies based on spike-timing patterns, and show that even unimodal firing statistics can be consistent with a bivariate neural code.

Role of Precise Spike Timing in Coding of Dynamic Vibrissa Stimuli in Somatosensory Thalamus

2007 ◽  
Vol 98 (4) ◽  
pp. 1871-1882 ◽  
Author(s):  
Marcelo A. Montemurro ◽  
Stefano Panzeri ◽  
Miguel Maravall ◽  
Andrea Alenda ◽  
Michael R. Bale ◽  
...  
Keyword(s):  
Firing Rate ◽  
Barrel Cortex ◽  
Neural Code ◽  
Spike Timing ◽  
Unit Recording ◽  
Temporal Correlations ◽  
Independent Information ◽  
Temporally Correlated ◽  
Spike Patterns

Rats discriminate texture by whisking their vibrissae across the surfaces of objects. This process induces corresponding vibrissa vibrations, which must be accurately represented by neurons in the somatosensory pathway. In this study, we investigated the neural code for vibrissa motion in the ventroposterior medial (VPm) nucleus of the thalamus by single-unit recording. We found that neurons conveyed a great deal of information (up to 77.9 bits/s) about vibrissa dynamics. The key was precise spike timing, which typically varied by less than a millisecond from trial to trial. The neural code was sparse, the average spike being remarkably informative (5.8 bits/spike). This implies that as few as four VPm spikes, coding independent information, might reliably differentiate between 106 textures. To probe the mechanism of information transmission, we compared the role of time-varying firing rate to that of temporally correlated spike patterns in two ways: 93.9% of the information encoded by a neuron could be accounted for by a hypothetical neuron with the same time-dependent firing rate but no correlations between spikes; moreover, ≥93.4% of the information in the spike trains could be decoded even if temporal correlations were ignored. Taken together, these results suggest that the essence of the VPm code for vibrissa motion is firing rate modulation on a submillisecond timescale. The significance of such a code may be that it enables a small number of neurons, firing only few spikes, to convey distinctions between very many different textures to the barrel cortex.

scholarly journals Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks

2019 ◽  
Author(s):  
Dongqi Han ◽  
Erik De Schutter ◽  
Sungho Hong
Keyword(s):  
Information Processing ◽  
Information Transmission ◽  
Information Transfer ◽  
Signal Propagation ◽  
Cell Types ◽  
Neural Systems ◽  
Intrinsic Properties ◽  
Cellular Mechanisms ◽  
Feedforward Networks ◽  
Stable Signal

AbstractFeedforward networks (FFN) are ubiquitous structures in neural systems and have been studied to understand mechanisms of reliable signal and information transmission. In many FFNs, neurons in one layer have intrinsic properties that are distinct from those in their pre-/postsynaptic layers, but how this affects network-level information processing remains unexplored. Here we show that layer-to-layer heterogeneity arising from lamina-specific cellular properties facilitates signal and information transmission in FFNs. Specifically, we found that signal transformations, made by neighboring layers of neurons on an input-driven spike signal, are complementary to each other. This mechanism boosts information transfer carried by a propagating spike signal, and thereby supports reliable spike signal and information transmission in a deep FFN. Our study suggests that distinct cell types in neural circuits have complementary computational functions and facilitate information processing on the whole.Significance StatementNeural systems have many cell types that differ in properties such as size, shape, cellular mechanisms, etc. Furthermore, neurons often propagate signals to other neurons that have properties very different from their own. We investigated what this phenomenon implies in neural information processing by using computational network models, inspired by a recent experimental study on the olfactory neural pathway of fruit flies. We found that different types of neurons can perform complementary functions in a network, which boosts information transfer on the whole and supports robust, stable signal propagation in a “deep” network with many layers. Our study demonstrates that diverse cell types with different intrinsic properties are crucial for optimal signal and information transfer in neural networks.

MODELING OF FIBER-OPTIC COMMUNICATION CHANNEL FOR QUANTUM CRYPTOGRAPHIC SYSTEMS

2019 ◽  
pp. 90-95
Author(s):  
V. A. Minaev ◽  
I. D. Korolev ◽  
O. A. Kulish ◽  
A. V. Mazin
Keyword(s):  
Information Transmission ◽  
Information Transfer ◽  
Fiber Optic ◽  
Single Photon ◽  
Optical Devices ◽  
Energy Losses ◽  
Integrated Optical ◽  
Integrated Optical Devices ◽  
Fiber Optic Communication ◽  
Optic Communication

The existing methods of information delivery to the strategic and tactical management of many government agencies are expensive, not always reliable and efficient. Therefore, quantum cryptographic systems (QCS) have been actively developed in recent years. However, there are problems with the use of the QCS associated with the reliability of information transfer. First, the existing fiber-optic communication channels (FOCC) are not designed to transmit single-photon signals, which leads to the complexity of their cryptographic protection. The second is insufficiently methodically developed calculation of energy losses and errors in the evaluation of the characteristics of information transfer in FOCC QCS. In article the analysis of the energy loss factors in the classical fiber-optic channel is carried out and the additive loss formula is discussed in detail. Then we consider the fiber-optic channel of quantum information transmission with the use of integrated optical devices. The additive formula of optical losses in such a channel is discussed. The features of losses in integrated optical devices are shown. The features of quantum cryptographic system of information transmission are considered. As a result, the model of FOCC QCS taking into account energy losses is presented, which allows competently in theoretical terms and visualize the passage of information through modern quantum cryptographically secure telecommunications while providing control in government structures.

scholarly journals Deep brain stimulation of the subthalamic nucleus reestablishes neuronal information transmission in the 6-OHDA rat model of parkinsonism

2014 ◽  
Vol 111 (10) ◽  
pp. 1949-1959 ◽  
Author(s):  
Alan D. Dorval ◽  
Warren M. Grill
Keyword(s):  
Deep Brain Stimulation ◽  
Basal Ganglia ◽  
Information Transmission ◽  
Brain Stimulation ◽  
Rat Model ◽  
Information Transfer ◽  
Firing Pattern ◽  
Signal Propagation ◽  
Neuronal Firing ◽  
Deep Brain

Pathophysiological activity of basal ganglia neurons accompanies the motor symptoms of Parkinson's disease. High-frequency (>90 Hz) deep brain stimulation (DBS) reduces parkinsonian symptoms, but the mechanisms remain unclear. We hypothesize that parkinsonism-associated electrophysiological changes constitute an increase in neuronal firing pattern disorder and a concomitant decrease in information transmission through the ventral basal ganglia, and that effective DBS alleviates symptoms by decreasing neuronal disorder while simultaneously increasing information transfer through the same regions. We tested these hypotheses in the freely behaving, 6-hydroxydopamine-lesioned rat model of hemiparkinsonism. Following the onset of parkinsonism, mean neuronal firing rates were unchanged, despite a significant increase in firing pattern disorder (i.e., neuronal entropy), in both the globus pallidus and substantia nigra pars reticulata. This increase in neuronal entropy was reversed by symptom-alleviating DBS. Whereas increases in signal entropy are most commonly indicative of similar increases in information transmission, directed information through both regions was substantially reduced (>70%) following the onset of parkinsonism. Again, this decrease in information transmission was partially reversed by DBS. Together, these results suggest that the parkinsonian basal ganglia are rife with entropic activity and incapable of functional information transmission. Furthermore, they indicate that symptom-alleviating DBS works by lowering the entropic noise floor, enabling more information-rich signal propagation. In this view, the symptoms of parkinsonism may be more a default mode, normally overridden by healthy basal ganglia information. When that information is abolished by parkinsonian pathophysiology, hypokinetic symptoms emerge.

Cortical Representation of Auditory Space: Information-Bearing Features of Spike Patterns

2002 ◽  
Vol 87 (4) ◽  
pp. 1749-1762 ◽  
Author(s):  
Shigeto Furukawa ◽  
John C. Middlebrooks
Keyword(s):  
Sound Source ◽  
Neuronal Response ◽  
Source Location ◽  
Spike Timing ◽  
Spike Count ◽  
Cortical Representation ◽  
Auditory Space ◽  
Transmitted Information ◽  
Related Information ◽  
Spike Patterns

Previous studies have demonstrated that the spike patterns of cortical neurons vary systematically as a function of sound-source location such that the response of a single neuron can signal the location of a sound source throughout 360° of azimuth. The present study examined specific features of spike patterns that might transmit information related to sound-source location. Analysis was based on responses of well-isolated single units recorded from cortical area A2 in α-chloralose-anesthetized cats. Stimuli were 80-ms noise bursts presented from loudspeakers in the horizontal plane; source azimuths ranged through 360° in 20° steps. Spike patterns were averaged across samples of eight trials. A competitive artificial neural network (ANN) identified sound-source locations by recognizing spike patterns; the ANN was trained using the learning vector quantization learning rule. The information about stimulus location that was transmitted by spike patterns was computed from joint stimulus-response probability matrices. Spike patterns were manipulated in various ways to isolate particular features. Full-spike patterns, which contained all spike-count information and spike timing with 100-μs precision, transmitted the most stimulus-related information. Transmitted information was sensitive to disruption of spike timing on a scale of more than ∼4 ms and was reduced by an average of ∼35% when spike-timing information was obliterated entirely. In a condition in which all but the first spike in each pattern were eliminated, transmitted information decreased by an average of only ∼11%. In many cases, that condition showed essentially no loss of transmitted information. Three unidimensional features were extracted from spike patterns. Of those features, spike latency transmitted ∼60% more information than that transmitted either by spike count or by a measure of latency dispersion. Information transmission by spike patterns recorded on single trials was substantially reduced compared with the information transmitted by averages of eight trials. In a comparison of averaged and nonaveraged responses, however, the information transmitted by latencies was reduced by only ∼29%, whereas information transmitted by spike counts was reduced by 79%. Spike counts clearly are sensitive to sound-source location and could transmit information about sound-source locations. Nevertheless, the present results demonstrate that the timing of the first poststimulus spike carries a substantial amount, probably the majority, of the location-related information present in spike patterns. The results indicate that any complete model of the cortical representation of auditory space must incorporate the temporal characteristics of neuronal response patterns.

scholarly journals Spike Timing and Information Transmission at Retinogeniculate Synapses

2010 ◽  
Vol 30 (41) ◽  
pp. 13558-13566 ◽  
Author(s):  
D. L. Rathbun ◽  
D. K. Warland ◽  
W. M. Usrey
Keyword(s):  
Information Transmission ◽  
Spike Timing

Contrast Adaptation in a Nonadapting LGN Model

2007 ◽  
Vol 98 (3) ◽  
pp. 1287-1296 ◽  
Author(s):  
Kate S. Gaudry ◽  
Pamela Reinagel
Keyword(s):  
Information Transmission ◽  
Detailed Comparison ◽  
Spike Timing ◽  
Physiological Data ◽  
Model Parameters ◽  
Transient Effects ◽  
Full Field ◽  
Contrast Gain ◽  
Contrast Adaptation ◽  
Contrast Normalization

Sensory neurons appear to adapt their gain to match the variance of signals along the dimension they encode, a property we shall call “contrast normalization.” Contrast normalization has been the subject of extensive physiological and theoretical study. We previously found that neurons in the lateral geniculate nucleus (LGN) exhibit contrast normalization in their responses to full-field flickering white-noise stimuli, and that neurons with the strongest contrast normalization best preserved information transmission across a range of contrasts. We have also shown that both of these properties could be reproduced by nonadapting model cells. Here we present a detailed comparison of this nonadapting model to physiological data from the LGN. First, the model cells recapitulated other contrast dependencies of LGN responses: decreasing stimulus contrast resulted in an increase in spike-timing jitter and spike-number variability. Second, we find that the extent of contrast normalization in this model depends on model parameters related to refractoriness and to noise. Third, we show that the model cells exhibit rapid, transient changes in firing rate just after changes in contrast, and that this is sufficient to produce the transient changes in information transmission that have been reported in other neurons. It is known that intrinsic properties of neurons change during contrast adaptation. Nevertheless the model demonstrates that the spiking nonlinearity of neurons can produce many of the temporal aspects of contrast gain control, including normalization to input variance and transient effects of contrast change.

scholarly journals The Astrocyte as a Gatekeeper of Synaptic Information Transfer

2007 ◽  
Vol 19 (2) ◽  
pp. 303-326 ◽  
Author(s):  
Vladislav Volman ◽  
Eshel Ben-Jacob ◽  
Herbert Levine
Keyword(s):  
Synaptic Transmission ◽  
Information Flow ◽  
Information Transfer ◽  
Calcium Concentration ◽  
Spike Timing ◽  
Biophysical Model ◽  
Testable Hypothesis ◽  
Cells In Culture

We present a simple biophysical model for the coupling between synaptic transmission and the local calcium concentration on an enveloping astrocytic domain. This interaction enables the astrocyte to modulate the information flow from presynaptic to postsynaptic cells in a manner dependent on previous activity at this and other nearby synapses. Our model suggests a novel, testable hypothesis for the spike timing statistics measured for rapidly firing cells in culture experiments.

scholarly journals Sparse bursts optimize information transmission in a multiplexed neural code

2018 ◽  
Vol 115 (27) ◽  
pp. E6329-E6338 ◽  
Author(s):  
Richard Naud ◽  
Henning Sprekeler
Keyword(s):  
Firing Rate ◽  
Cortical Neurons ◽  
Neural Code ◽  
Spike Timing ◽  
Neural Ensemble ◽  
Multiple Input ◽  
Cortical Hierarchy ◽  
Connectivity Patterns ◽  
Rate Coding

Many cortical neurons combine the information ascending and descending the cortical hierarchy. In the classical view, this information is combined nonlinearly to give rise to a single firing-rate output, which collapses all input streams into one. We analyze the extent to which neurons can simultaneously represent multiple input streams by using a code that distinguishes spike timing patterns at the level of a neural ensemble. Using computational simulations constrained by experimental data, we show that cortical neurons are well suited to generate such multiplexing. Interestingly, this neural code maximizes information for short and sparse bursts, a regime consistent with in vivo recordings. Neurons can also demultiplex this information, using specific connectivity patterns. The anatomy of the adult mammalian cortex suggests that these connectivity patterns are used by the nervous system to maintain sparse bursting and optimal multiplexing. Contrary to firing-rate coding, our findings indicate that the physiology and anatomy of the cortex may be interpreted as optimizing the transmission of multiple independent signals to different targets.

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