Response variability and information transfer in directional neurons of the mammalian horizontal optokinetic system

2000 ◽  
Vol 17 (2) ◽  
pp. 207-215 ◽  
Author(s):  
COLIN W.G. CLIFFORD ◽  
MICHAEL R. IBBOTSON
Keyword(s):  
Refractory Period ◽  
Information Transfer ◽  
Visual Motion ◽  
Optic Tract ◽  
Poisson Model ◽  
Spike Trains ◽  
Response Variability ◽  
Time Varying ◽  
Neuronal Responses ◽  
Optokinetic System

This study is concerned with how information about the direction of visual motion is encoded by motion-sensitive neurons. Motion-sensitive neurons are usually studied using stimuli unchanging in speed and direction over several seconds. Recently, it has been suggested that neuronal responses to more naturalistic stimuli cannot be understood on the basis of experiments with constant-motion stimuli (de Ruyter van Steveninck et al., 1997). We measured the variability and information content of spike trains recorded from directional neurons in the nucleus of the optic tract (NOT) of the wallaby, Macropus eugenii, in response to constant and time-varying motion. While the NOT forms part of the mammalian optokinetic system, we have shown previously that the responses of its directional neurons resemble those of insect H1 in many respects (Ibbotson et al., 1994). We find that directional neurons in the wallaby NOT respond with lower variability and higher rates of information transmission to time-varying stimuli than to constant motion. The difference in response variability is predicted by an inhomogeneous Poisson model of neuronal spiking incorporating an absolute refractory period of 2 ms during which no subsequent spike can be fired. Refractoriness imposes structure on the spike train, reducing variability (de Ruyter van Steveninck & Bialek, 1988; Berry & Meister, 1998). A given refractory period has a greater impact when firing rates are high, as for the responses of NOT neurons to time-varying stimuli. It is in just these cases that variability in experimentally observed spike trains is lowest. Thus, differences in response variability do not necessarily imply that different models are required to predict neuronal responses to constant- and time-varying motion stimuli.

MST Neuronal Responses to Heading Direction During Pursuit Eye Movements

1999 ◽  
Vol 81 (2) ◽  
pp. 596-610 ◽  
Author(s):  
William K. Page ◽  
Charles J. Duffy
Keyword(s):  
Eye Movements ◽  
Smooth Pursuit ◽  
Visual Motion ◽  
Neuronal Population ◽  
Smooth Pursuit Eye Movements ◽  
Neuronal Responses ◽  
Temporal Area ◽  
Pursuit Eye Movements ◽  
Heading Direction ◽  
Monkey Visual Cortex

MST neuronal responses to heading direction during pursuit eye movements. As you move through the environment, you see a radial pattern of visual motion with a focus of expansion (FOE) that indicates your heading direction. When self-movement is combined with smooth pursuit eye movements, the turning of the eye distorts the retinal image of the FOE but somehow you still can perceive heading. We studied neurons in the medial superior temporal area (MST) of monkey visual cortex, recording responses to FOE stimuli presented during fixation and smooth pursuit eye movements. Almost all neurons showed significant changes in their FOE selective responses during pursuit eye movements. However, the vector average of all the neuronal responses indicated the direction of the FOE during both fixation and pursuit. Furthermore, the amplitude of the net vector increased with increasing FOE eccentricity. We conclude that neuronal population encoding in MST might contribute to pursuit-tolerant heading perception.

Coding of time-varying electric field amplitude modulations in a wave-type electric fish

1996 ◽  
Vol 75 (6) ◽  
pp. 2280-2293 ◽  
Author(s):  
R. Wessel ◽  
C. Koch ◽  
F. Gabbiani
Keyword(s):  
Mutual Information ◽  
Electric Fish ◽  
Signal To Noise Ratio ◽  
Cutoff Frequency ◽  
Spike Trains ◽  
Time Varying ◽  
Signal To Noise ◽  
Single Spike ◽  
The Mean ◽  
P Type

1. The coding of time-varying electric fields in the weakly electric fish, Eigenmannia, was investigated in a quantitative manner. The activity of single P-type electroreceptor afferents was recorded while the amplitude of an externally applied sinusoidal electric field was stochastically modulated. The amplitude modulation waveform (i.e., the stimulus) was reconstructed from the spike trains by mean square estimation. 2. From the stimulus and the reconstructions we calculated the following: 1) the signal-to-noise ratio and thus an effective temporal bandwidth of the units; 2) the coding fraction, i.e., a measure of the fraction of the time-varying stimulus encoded in single spike trains; and 3) the mutual information provided by the reconstructions about the stimulus. 3. Signal-to-noise ratios as high as 7:1 were observed and the bandwidth ranged from 0 up to 200 Hz, consistent with the limit imposed by the sampling theorem. Reducing the cutoff frequency of the stimulus increased the signal-to-noise ratio at low frequencies, indicating a nonlinearity in the receptors' response. 4. The coding fraction and the rate of mutual information transmission increased in parallel with the standard deviation (i.e., the contrast) of the stimulus as well as the mean firing rate of the units. Significant encoding occurred 20-40 Hz above the spontaneous discharge of a unit. 5. When the temporal cutoff frequency of the stimulus was increased between 80 and 400 Hz, 1) the coding fraction decreased, 2) the rate of mutual information transmission remained constant over the same frequency range, and 3) the reconstructed filter changed. This is in agreement with predictions obtained in a simplified neuronal model. 6. Our results suggest that 1) the information transmitted by single spike trains of primary electrosensory afferents to higherorder neurons in the fish brain depends on the contrast and the cutoff frequency of the stimulus as well as on the mean firing rate of the units; and 2) under optimal conditions, more than half of the information about a Gaussian stimulus that can in principle be encoded is carried in single spike trains of P-type afferents at rates up to 200 bits per second.

scholarly journals Stochastic, structural and functional factors influencing AMPA and NMDA synaptic response variability: a review

2017 ◽  
Vol 1 (3) ◽  
Author(s):  
Vito Di Maio ◽  
Francesco Ventriglia ◽  
Silvia Santillo
Keyword(s):  
Nervous System ◽  
Synaptic Transmission ◽  
Learning And Memory ◽  
Information Transfer ◽  
Basic Mechanism ◽  
Response Variability ◽  
Synaptic Response ◽  
Factors Influencing ◽  
Functional Factors ◽  
The Brain

Synaptic transmission is the basic mechanism of information transfer between neurons not only in the brain, but along all the nervous system. In this review we will briefly summarize some of the main parameters that produce stochastic variability in the synaptic response. This variability produces different effects on important brain phenomena, like learning and memory, and, alterations of its basic factors can cause brain malfunctioning.

Extracting State Transition Dynamics from Multiple Spike Trains Using Hidden Markov Models with Correlated Poisson Distribution

2010 ◽  
Vol 22 (9) ◽  
pp. 2369-2389 ◽  
Author(s):  
Kentaro Katahira ◽  
Jun Nishikawa ◽  
Kazuo Okanoya ◽  
Masato Okada
Keyword(s):  
Hidden Markov Models ◽  
Poisson Distribution ◽  
State Transition ◽  
Markov Models ◽  
Hidden Markov ◽  
Synthetic Data ◽  
Poisson Model ◽  
Spike Trains ◽  
Output Distribution ◽  
Multivariate Poisson Distribution

Neural activity is nonstationary and varies across time. Hidden Markov models (HMMs) have been used to track the state transition among quasi-stationary discrete neural states. Within this context, an independent Poisson model has been used for the output distribution of HMMs; hence, the model is incapable of tracking the change in correlation without modulating the firing rate. To achieve this, we applied a multivariate Poisson distribution with correlation terms for the output distribution of HMMs. We formulated a variational Bayes (VB) inference for the model. The VB could automatically determine the appropriate number of hidden states and correlation types while avoiding the overlearning problem. We developed an efficient algorithm for computing posteriors using the recursive relationship of a multivariate Poisson distribution. We demonstrated the performance of our method on synthetic data and real spike trains recorded from a songbird.

Decorrelation of Spiking Variability and Improved Information Transfer Through Feedforward Divisive Normalization

2012 ◽  
Vol 24 (4) ◽  
pp. 867-894 ◽  
Author(s):  
Bryan P. Tripp
Keyword(s):  
Visual Cortex ◽  
Information Transfer ◽  
Response Variability ◽  
Signal Integration ◽  
Correlated Noise ◽  
Stimulus Parameter ◽  
Divisive Normalization ◽  
Optimal Linear ◽  
Large Groups ◽  
Simple Average

Response variability is often positively correlated in pairs of similarly tuned neurons in the visual cortex. Many authors have considered correlated variability to prevent postsynaptic neurons from averaging across large groups of inputs to obtain reliable stimulus estimates. However, a simple average of variability ignores nonlinearities in cortical signal integration. This study shows that feedforward divisive normalization of a neuron's inputs effectively decorrelates their variability. Furthermore, we show that optimal linear estimates of a stimulus parameter that are based on normalized inputs are more accurate than those based on nonnormalized inputs, due partly to reduced correlations, and that these estimates improve with increasing population size up to several thousand neurons. This suggests that neurons may possess a simple mechanism for substantially decorrelating noise in their inputs. Further work is needed to reconcile this conclusion with past evidence that correlated noise impairs visual perception.

Quantum superarrivals and information transfer through a time-varying boundary

Pramana ◽  
2002 ◽  
Vol 59 (2) ◽  
pp. 321-328 ◽  
Author(s):  
AS Majumdar ◽  
Dipankar Home
Keyword(s):  
Information Transfer ◽  
Time Varying

Intermittent stimulus presentation stabilizes neuronal responses in macaque area MT

2012 ◽  
Vol 108 (8) ◽  
pp. 2101-2114 ◽  
Author(s):  
P. Christiaan Klink ◽  
Anna Oleksiak ◽  
Martin J. M. Lankheet ◽  
Richard J. A. van Wezel
Keyword(s):  
Neuronal Response ◽  
Stimulus Presentation ◽  
Sensory Stimulation ◽  
Response Variability ◽  
Spike Timing ◽  
Response Magnitude ◽  
General Effect ◽  
Neuronal Responses ◽  
Area Mt ◽  
Response Characteristics

Repeated stimulation impacts neuronal responses. Here we show how response characteristics of sensory neurons in macaque visual cortex are influenced by the duration of the interruptions during intermittent stimulus presentation. Besides effects on response magnitude consistent with neuronal adaptation, the response variability was also systematically influenced. Spike rate variability in motion-sensitive area MT decreased when interruption durations were systematically increased from 250 to 2,000 ms. Activity fluctuations between subsequent trials and Fano factors over full response sequences were both lower with longer interruptions, while spike timing patterns became more regular. These variability changes partially depended on the response magnitude, but another significant effect that was uncorrelated with adaptation-induced changes in response magnitude was also present. Reduced response variability was furthermore accompanied by changes in spike-field coherence, pointing to the possibility that reduced spiking variability results from interactions in the local cortical network. While neuronal response stabilization may be a general effect of repeated sensory stimulation, we discuss its potential link with the phenomenon of perceptual stabilization of ambiguous stimuli as a result of interrupted presentation.

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