scholarly journals Costs and benefits of using rhythmic rate codes

2021 ◽  
Author(s):  
Erik J. Peterson ◽  
Bradley Voytek
Keyword(s):  
Nervous System ◽  
Population Size ◽  
Synaptic Connection ◽  
Poisson Model ◽  
Neural Oscillations ◽  
Costs And Benefits ◽  
Rate Code ◽  
Firing Rates ◽  
Alternative Approach ◽  
Connection Type

AbstractNeural oscillations are one of the most well-known macroscopic phenomena observed in the nervous system, and the benefits of oscillatory coding have been the topic of frequent analysis. Many of these studies focused on communication between populations which were already oscillating, and sought to understand how synchrony and communication interact. In this paper, take an alternative approach. We focus on measuring the costs, and benefits, of moving to an from an aperiodic code to a rhythmic one. We utilize a Linear-Nonlinear Poisson model, and assume a rate code. We report that no one factor seems to predict the costs, or benefits, of translating into a rhythmic code. Instead the synaptic connection type, strength, population size, and stimulus and oscillation firing rates interact in nonlinear ways. We suggest a number of experiments that might be used to confirm these predictions.Author summaryIt’s good to oscillate, sometimes.

Variability in somatosensory cortical neuron discharge: effects on capacity to signal different stimulus conditions using a mean rate code

1978 ◽  
Vol 41 (2) ◽  
pp. 338-349 ◽  
Author(s):  
R. C. Schreiner ◽  
G. K. Essick ◽  
B. L. Whitsel
Keyword(s):  
Firing Rate ◽  
Linear Equation ◽  
Cortical Neuron ◽  
Single Neuron ◽  
Rate Code ◽  
Firing Rates ◽  
Discrimination Criterion ◽  
The Mean ◽  
Change In Mean ◽  
The Relationship

1. The present study is based on the demonstration (8, 9) that the relationship between mean interval (MI) and standard deviation (SD) for stimulus-driven activity recorded from SI neurons is well fitted by the linear equation SD = a X MI + b and on the observations that the values of the slope (a) and y intercept (b) parameters of this relationship are independent of stimulus conditions and may vary widely from one neuron to the next (8). 2. A criterion for the discriminability of two different mean firing rates requiring that the mean intervals of their respective interspike interval (ISI) distributions be separated by a fixed interval (expressed in SD units) is developed and, on the basis of this criterion, a graphical display of the capacity of a neuron with a known SD-MI relationship to reflect a change in stimulus conditions with a change in mean firing rate is derived. Using this graphical approach, it is shown that the parameters of the SD-MI relationship for a single neuron determine a range of firing frequencies, within which that neuron exhibits the greatest capacity to signal differences in stimulus conditions using a frequency code. 3. The discrimination criterion is modified to incorporate the changes in the symmetry of the ISI distribution observed to accompany changes in mean firing rate. It is shown that, although the observed symmetry changes do influence the capacity of a cortical neuron to signal a change in stimulus conditions with a change in mean firing rate, they do not alter the range of firing rates (determined by the parameters of the SD-MI relationship) within which the capacity for discrimination is maximal. 4. The maximal number of firing levels that can be distinguished by a somatosensory cortical neuron (using the same discrimination criterion described above) discharging within a specified range of mean frequencies also is demonstrated to depend on the parameters of the linear equation which relates SD to MI. 5. Two approaches based on the t test for differences between two means are developed in an attempt to ascertain the minimum separation of the mean intervals of the ISI distributions necessary for two different mean firing rates to be discriminated with 80% certainty.

scholarly journals Conserved features of the primate face code

2018 ◽  
Vol 115 (3) ◽  
pp. 584-588 ◽  
Author(s):  
Charles F. Stevens
Keyword(s):  
Maximum Entropy ◽  
Exponential Distribution ◽  
Olfactory System ◽  
Fruit Fly ◽  
Human Face ◽  
Rate Code ◽  
Firing Rates ◽  
The Face

A recent paper demonstrated that the pattern of firing rates across ∼100 neurons in the anterior medial face patch is closely related to which human face (of 2,000) had been presented to a monkey [Chang L, Tsao DY (2017) Cell 169:1013–1028]. In addition, the firing rates for these neurons can be predicted for a novel human face. Although it is clear from this work that the firing rates of these face patch neurons encode faces, the properties of the face code have not yet been fully described. Based on an analysis of 98 neurons responding to 2,000 faces, I conclude that the anterior medial face patch uses a combinatorial rate code, one with an exponential distribution of neuron rates that has a mean rate conserved across faces. Thus, the face code is maximally informative (technically, maximum entropy) and is very similar to the code used by the fruit fly olfactory system.

scholarly journals An uncertainty principle for neural coding: Conjugate representations of position and velocity are mapped onto firing rates and co-firing rates of neural spike trains

2019 ◽  
Author(s):  
Ryan Grgurich ◽  
Hugh T. Blair
Keyword(s):  
Firing Rate ◽  
Uncertainty Principle ◽  
Neural Coding ◽  
Spike Trains ◽  
Grid Cells ◽  
Rate Code ◽  
Biologically Inspired ◽  
Firing Rates ◽  
Neural Populations ◽  
Similar Information

AbstractThe hippocampal system contains neural populations that encode an animal’s position and velocity as it navigates through space. Here, we show that such populations can embed two codes within their spike trains: a firing rate code (R) conveyed by within-cell spike intervals, and a co-firing rate code (Ṙ) conveyed by between-cell spike intervals. These two codes behave as conjugates of one another, obeying an analog of the uncertainty principle from physics: information conveyed in R comes at the expense of information in Ṙ, and vice versa. An exception to this trade-off occurs when spike trains encode a pair of conjugate variables, such as position and velocity, which do not compete for capacity across R and Ṙ. To illustrate this, we describe two biologically inspired methods for decoding R and Ṙ, referred to as sigma and sigma-chi decoding, respectively. Simulations of head direction (HD) and grid cells show that if firing rates are tuned for position (but not velocity), then position is recovered by sigma decoding, whereas velocity is recovered by sigma-chi decoding. Conversely, simulations of oscillatory interference among theta-modulated “speed cells” show that if co-firing rates are tuned for position (but not velocity), then position is recovered by sigma-chi decoding, whereas velocity is recovered by sigma decoding. Between these two extremes, information about both variables can be distributed across both channels, and partially recovered by both decoders. These results suggest that neurons with different spatial and temporal tuning properties—such as speed versus grid cells—might not encode different information, but rather, distribute similar information about position and velocity in different ways across R and Ṙ. Such conjugate coding of position and velocity may influence how hippocampal populations are interconnected to form functional circuits, and how biological neurons integrate their inputs to decode information from firing rates and spike correlations.

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.

Information Transfer Through Stochastic Transmission of a Linear Combination of Rates

2013 ◽  
Vol 25 (9) ◽  
pp. 2265-2302 ◽  
Author(s):  
Ioannis Smyrnakis ◽  
Stelios Smirnakis
Keyword(s):  
Linear Combination ◽  
Information Transfer ◽  
Firing Process ◽  
Afferent Neurons ◽  
Rate Code ◽  
Transfer Rates ◽  
Firing Rates ◽  
Information Transfer Rate ◽  
The Individual ◽  
High Firing

In this work, the Shannon information transfer rate due to the transmission of a linear combination of the firing rates of a number of afferent neurons is examined. The transmission of this linear combination (transfer statistic) takes place through a stochastic firing process, while a rate code is assumed. Constraints are imposed on the transmission process by the requirement that the coefficient of variation for the transfer statistic is small and by the relative variance of the individual terms in the calculation of the statistic. In the regime of no noise or signal correlations among the input neurons, simulations suggest that information transfer for fixed overall input is favored when there are few high-firing neurons, as opposed to more lower-firing neurons. Signal correlations among low-firing neurons can result in aggregates of high firing rates, improving in this way information transfer and calculational robustness. Under reasonable rate code assumptions, information transfer rates obtained are of the order 3 to 10 bit/sec.

scholarly journals The role of the central nervous system in glucose homeostasis

2012 ◽  
Vol 19 (2) ◽  
pp. 207-214 ◽  
Author(s):  
Cristina Muntean ◽  
Maria Mota ◽  
Simona Popa ◽  
Adina Mitrea
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Glucose Homeostasis ◽  
Adrenal Glands ◽  
Firing Rates ◽  
Claude Bernard ◽  
The Central Nervous System ◽  
Extracellular Level ◽  
The One

Abstract Central nervous system, mainly the hypothalamus and the brainstem are importantkeys in glucose homeostasis. Not only do they use glucose as primary fuel for theirfunctioning but they are part of intricate neuronal circuits involved in glucose uptakeand production as was first shown by Claude Bernard. Moreoverelectrophysiological analysis of hypothalamus revealed the existence of glucosensingneurons whose firing rates are controlled by glucose extracellular level. Furtherinformation was obtained regarding the importance of leptin, insulin and free fattyacids as afferent signals received by these neural structures. As for the main efferentpathways, autonomic system is the one connecting CNS with the effector organs (theliver, the pancreas and the adrenal glands).

scholarly journals The Tenets of Teneurin: Conserved Mechanisms Regulate Diverse Developmental Processes in the Drosophila Nervous System

2018 ◽  
Author(s):  
Alison T DePew ◽  
Michael A Aimino ◽  
Timothy J Mosca
Keyword(s):  
Nervous System ◽  
Axon Guidance ◽  
Molecular Mechanisms ◽  
Synapse Formation ◽  
Synaptic Connection ◽  
Single Family ◽  
Correct Order ◽  
Membrane Stabilization ◽  
Cytoskeletal Regulation ◽  
Partner Matching

To successfully integrate a neuron into a circuit, a myriad of developmental events must occur correctly and in the correct order. Neurons must be born and grow out towards a destination, responding to guidance cues to direct their path. Once arrived, each neuron must segregate to the correct sub-region before sorting through a milieu of incorrect partners to identify the correct partner with which they can connect. Finally, the neuron must make a synaptic connection with their correct partner; a connection that needs to be broadly maintained throughout the life of the animal while remaining responsive to modes of plasticity and pruning. Though many intricate molecular mechanisms have been discovered to regulate each step, recent work showed that a single family of proteins, the Teneurins, regulates a host of these developmental steps in Drosophila - an example of biological adaptive reuse. Teneurins first influence axon guidance during early development. Once neurons arrive in their target regions, Teneurins enable partner matching and synapse formation in both the central and peripheral nervous systems. Despite these diverse processes and systems, the Teneurins use conserved mechanisms to achieve these goals, as defined by two tenets: 1) transsynaptic interactions with each other and 2) membrane stabilization via an interaction and regulation of the cytoskeleton. These processes are further distinguished by 1) the nature of the transsynaptic interaction - homophilic interactions (between the same Teneurins) to engage partner matching and heterophilic interactions (between different Teneurins) to enable synaptic connectivity and the proper apposition of pre- and postsynaptic sites and 2) the location of cytoskeletal regulation (presynaptic cytoskeletal regulation in the CNS and postsynaptic regulation of the cytoskeleton at the NMJ). Thus, both the roles and the mechanisms governing them are conserved across processes and synapses. In this review, we will highlight the contributions of Drosophila synaptic biology to our understanding of the Teneurins and discuss the mechanistic conservation that allows the Teneurins to achieve common neurodevelopmental goals. Finally, we will posit the next steps for understanding how this remarkably versatile family of proteins functions to control multiple distinct events in the creation of a nervous system.

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