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 Burst activity plays no role for the field-to-field variability and rate remapping of grid cells

2020 ◽  
Author(s):  
Michaela Poth ◽  
Andreas V.M. Herz
Keyword(s):  
Firing Rate ◽  
Time Scales ◽  
Neural Coding ◽  
Hexagonal Lattice ◽  
Grid Cell ◽  
Grid Cells ◽  
Firing Patterns ◽  
Firing Rates ◽  
Coding Schemes ◽  
Short Time

AbstractGrid cells in rodent medial entorhinal cortex are thought to play a key role for spatial navigation. When the animal is freely moving in an open arena the firing fields of each grid cell tend to form a hexagonal lattice spanning the environment. Firing rates vary from field to field and change under contextual modifications, whereas the field locations do not shift under such “rate remapping”. The observed differences in firing rate could reflect overall activity changes or changes in the detailed spike-train statistics. As these two alternatives imply distinct neural coding schemes, we investigated whether temporal firing patterns vary from field to field and whether they change under rate remapping. Focusing on short time scales, we found that the proportion of bursts compared to all discharge events is similar in all firing fields of a given grid cell and does not change under rate remapping. Mean firing rates with and without bursts are proportional for each cell. However, this ratio varies across cells. Additionally, we looked at how rate remapping relates to entorhinal theta-frequency oscillations. Theta-phase coding was preserved despite firing-rate changes from rate remapping but we did not observe differences between the first and second half of the theta cycle, as had been reported for CA1. Our results indicate that both, the heterogeneity between firing fields and rate remapping, are not due to altered firing patterns on short time scales but reflect location-specific changes at the firing-rate level.

scholarly journals Multimodal Medullary Neurons and Correlational Linkages of the Respiratory Network

1999 ◽  
Vol 82 (1) ◽  
pp. 188-201 ◽  
Author(s):  
Zhongzeng Li ◽  
Kendall F. Morris ◽  
David M. Baekey ◽  
Roger Shannon ◽  
Bruce G. Lindsey
Keyword(s):  
Firing Rate ◽  
Nucleus Tractus Solitarius ◽  
Motor Pattern ◽  
Sensory Modality ◽  
Spike Trains ◽  
Negative Feedback Regulation ◽  
Firing Rates ◽  
Respiratory Network ◽  
Cross Correlation Analysis ◽  
Stimulation Of

This study addresses the hypothesis that multiple sensory systems, each capable of reflexly altering breathing, jointly influence neurons of the brain stem respiratory network. Carotid chemoreceptors, baroreceptors, and foot pad nociceptors were stimulated sequentially in 33 Dial-urethan–anesthetized or decerebrate vagotomized adult cats. Neuronal impulses were monitored with microelectrode arrays in the rostral and caudal ventral respiratory group (VRG), nucleus tractus solitarius (NTS), and n. raphe obscurus. Efferent phrenic nerve activity was recorded. Spike trains of 889 neurons were analyzed with cycle-triggered histograms and tested for respiratory-modulated firing rates. Responses to stimulus protocols were assessed with peristimulus time and cumulative sum histograms. Cross-correlation analysis was used to test for nonrandom temporal relationships between spike trains. Spike-triggered averages of efferent phrenic activity and antidromic stimulation methods provided evidence for functional associations of bulbar neurons with phrenic motoneurons. Spike train cross-correlograms were calculated for 6,471 pairs of neurons. Significant correlogram features were detected for 425 pairs, including 189 primary central peaks or troughs, 156 offset peaks or troughs, and 80 pairs with multiple peaks and troughs. The results provide evidence that correlational medullary assemblies include neurons with overlapping memberships in groups responsive to different sets of sensory modalities. The data suggest and support several hypotheses concerning cooperative relationships that modulate the respiratory motor pattern. 1) Neurons responsive to a single tested modality promote or limit changes in firing rate of multimodal target neurons. 2) Multimodal neurons contribute to changes in firing rate of neurons responsive to a single tested modality. 3) Multimodal neurons may promote responses during stimulation of one modality and “limit” changes in firing rates during stimulation of another sensory modality. 4) Caudal VRG inspiratory neurons have inhibitory connections that provide negative feedback regulation of inspiratory drive and phase duration.

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.

Internal Pallidal Neuronal Activity During Mild Drug-Related Dyskinesias in Parkinson's Disease: Decreased Firing Rates and Altered Firing Patterns

2007 ◽  
Vol 97 (4) ◽  
pp. 2627-2641 ◽  
Author(s):  
J. I. Lee ◽  
L. Verhagen Metman ◽  
S. Ohara ◽  
P. M. Dougherty ◽  
J. H. Kim ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Firing Rate ◽  
Spike Trains ◽  
Neuronal Firing ◽  
Low Doses ◽  
Significant Group ◽  
Firing Patterns ◽  
Firing Rates ◽  
Before And After

The neuronal basis of hyperkinetic movement disorders has long been unclear. We now test the hypothesis that changes in the firing pattern of neurons in the globus pallidus internus (GPi) are related to dyskinesias induced by low doses of apomorphine in patients with advanced Parkinson's disease (PD). During pallidotomy for advanced PD, the activity of single neurons was studied both before and after administration of apomorphine at doses just adequate to induce dyskinesias (21 neurons, 17 patients). After the apomorphine injection, these spike trains demonstrated an initial fall in firing from baseline. In nine neurons, the onset of on was simultaneous with that of dyskinesias. In these spike trains, the initial fall in firing rate preceded and was larger than the fall at the onset of on with dyskinesias. Among the three neurons in which the onset of on occurred before that of dyskinesias, the firing rate did not change at the time of onset of dyskinesias. After injection of apomorphine, dyskinesias during on with dyskinesias often fluctuated between transient periods with dyskinesias and those without. Average firing rates were not different between these two types of transient periods. Transient periods with dyskinesias were characterized by interspike interval (ISI) independence, stationary spike trains, and higher variability of ISIs. A small but significant group of neurons demonstrated recurring ISI patterns during transient periods of on with dyskinesias. These results suggest that mild dyskinesias resulting from low doses of apomorphine are related to both low GPi neuronal firing rates and altered firing patterns.

scholarly journals Ambiguity and nonidentifiability in the statistical analysis of neural codes

2015 ◽  
Vol 112 (20) ◽  
pp. 6455-6460 ◽  
Author(s):  
Asohan Amarasingham ◽  
Stuart Geman ◽  
Matthew T. Harrison
Keyword(s):  
Statistical Analysis ◽  
Firing Rate ◽  
Statistical Modeling ◽  
Neural Coding ◽  
Experimental Studies ◽  
Spike Timing ◽  
Statistical Interpretation ◽  
Neural Codes ◽  
Firing Rates ◽  
Timing Variability

Many experimental studies of neural coding rely on a statistical interpretation of the theoretical notion of the rate at which a neuron fires spikes. For example, neuroscientists often ask, “Does a population of neurons exhibit more synchronous spiking than one would expect from the covariability of their instantaneous firing rates?” For another example, “How much of a neuron’s observed spiking variability is caused by the variability of its instantaneous firing rate, and how much is caused by spike timing variability?” However, a neuron’s theoretical firing rate is not necessarily well-defined. Consequently, neuroscientific questions involving the theoretical firing rate do not have a meaning in isolation but can only be interpreted in light of additional statistical modeling choices. Ignoring this ambiguity can lead to inconsistent reasoning or wayward conclusions. We illustrate these issues with examples drawn from the neural-coding literature.

scholarly journals Local differences in neuronal activation level decorrelate spatially coherent global fluctuations in gamma rhythms

2017 ◽  
Author(s):  
Kaushik J. Lakshminarasimhan ◽  
Nikos K. Logothetis ◽  
Georgios A. Keliris
Keyword(s):  
Firing Rate ◽  
Field Potential ◽  
Statistical Technique ◽  
Rate Code ◽  
Firing Rates ◽  
Activation Level ◽  
Gamma Rhythms ◽  
Global Fluctuations ◽  
Neuronal Coherence ◽  
Local Field Potential Lfp

AbstractNeuronal coherence is thought to constitute a unique substrate for information transmission distinct from firing rate. However, since the spatial scale of extracellular oscillations typically exceeds that of firing rates, it is unclear whether coherence complements or compromises the rate code. We examined responses in the macaque primary visual cortex and found that fluctuations in gamma-band (~40Hz) neuronal coherence correlated more with firing rate than oscillations in the local-field-potential (LFP). Although the spatial extent of LFP rhythms was broader, that of neuronal coherence was indistinguishable from firing rates. To identify the mechanism, we developed a statistical technique to isolate the rhythmic component of the spiking process and found that above results are explained by an activation-dependent increase in neuronal sensitivity to gamma-rhythmic input. Such adaptive changes in sensitivity to rhythmic inputs might constitute a fundamental homeostatic mechanism that prevents globally coherent inputs from undermining spatial resolution of the neural code.

scholarly journals A deep convolutional visual encoding model of neuronal responses in the LGN

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Eslam Mounier ◽  
Bassem Abdullah ◽  
Hani Mahdi ◽  
Seif Eldawlatly
Keyword(s):  
Firing Rate ◽  
Visual Information ◽  
Visual Stimulation ◽  
Neuronal Population ◽  
Neuronal Firing ◽  
Electrode Arrays ◽  
Deep Convolutional Neural Networks ◽  
Firing Rates ◽  
Spatiotemporal Representation

AbstractThe Lateral Geniculate Nucleus (LGN) represents one of the major processing sites along the visual pathway. Despite its crucial role in processing visual information and its utility as one target for recently developed visual prostheses, it is much less studied compared to the retina and the visual cortex. In this paper, we introduce a deep learning encoder to predict LGN neuronal firing in response to different visual stimulation patterns. The encoder comprises a deep Convolutional Neural Network (CNN) that incorporates visual stimulus spatiotemporal representation in addition to LGN neuronal firing history to predict the response of LGN neurons. Extracellular activity was recorded in vivo using multi-electrode arrays from single units in the LGN in 12 anesthetized rats with a total neuronal population of 150 units. Neural activity was recorded in response to single-pixel, checkerboard and geometrical shapes visual stimulation patterns. Extracted firing rates and the corresponding stimulation patterns were used to train the model. The performance of the model was assessed using different testing data sets and different firing rate windows. An overall mean correlation coefficient between the actual and the predicted firing rates of 0.57 and 0.7 was achieved for the 10 ms and the 50 ms firing rate windows, respectively. Results demonstrate that the model is robust to variability in the spatiotemporal properties of the recorded neurons outperforming other examined models including the state-of-the-art Generalized Linear Model (GLM). The results indicate the potential of deep convolutional neural networks as viable models of LGN firing.

scholarly journals Pseudosparse neural coding in the visual system of primates

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Sidney R. Lehky ◽  
Keiji Tanaka ◽  
Anne B. Sereno
Keyword(s):  
Neural Coding ◽  
Macaque Monkey ◽  
Implicit Assumption ◽  
Data Set ◽  
Efficient Coding ◽  
Population Responses ◽  
Lateral Intraparietal Cortex ◽  
Neural Populations ◽  
Neurophysiological Data ◽  
The Mean

AbstractWhen measuring sparseness in neural populations as an indicator of efficient coding, an implicit assumption is that each stimulus activates a different random set of neurons. In other words, population responses to different stimuli are, on average, uncorrelated. Here we examine neurophysiological data from four lobes of macaque monkey cortex, including V1, V2, MT, anterior inferotemporal cortex, lateral intraparietal cortex, the frontal eye fields, and perirhinal cortex, to determine how correlated population responses are. We call the mean correlation the pseudosparseness index, because high pseudosparseness can mimic statistical properties of sparseness without being authentically sparse. In every data set we find high levels of pseudosparseness ranging from 0.59–0.98, substantially greater than the value of 0.00 for authentic sparseness. This was true for synthetic and natural stimuli, as well as for single-electrode and multielectrode data. A model indicates that a key variable producing high pseudosparseness is the standard deviation of spontaneous activity across the population. Consistently high values of pseudosparseness in the data demand reconsideration of the sparse coding literature as well as consideration of the degree to which authentic sparseness provides a useful framework for understanding neural coding in the cortex.

Correlation between neural spike trains increases with firing rate

Nature ◽  
2007 ◽  
Vol 448 (7155) ◽  
pp. 802-806 ◽  
Author(s):  
Jaime de la Rocha ◽  
Brent Doiron ◽  
Eric Shea-Brown ◽  
Krešimir Josić ◽  
Alex Reyes
Keyword(s):  
Firing Rate ◽  
Spike Trains ◽  
Neural Spike ◽  
Neural Spike Trains
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