Magnitude of Perceived Change in Natural Images May Be Linearly Proportional to Differences in Neuronal Firing Rates

2010 ◽  
Vol 23 (4) ◽  
pp. 349-372 ◽  
Author(s):  
Pei-Ying Chua ◽  
Tom Troscianko ◽  
P. George Lovell ◽  
David Tolhurst ◽  
Mazviita Chirimuuta ◽  
...  
Keyword(s):  
Neuronal Response ◽  
Neuronal Firing ◽  
Natural Scenes ◽  
Action Potential Firing ◽  
Firing Rates ◽  
V1 Neurons ◽  
Perceived Change ◽  
Potential Firing ◽  
Neuronal Firing Rate ◽  
Image Pairs

AbstractWe are studying how people perceive naturalistic suprathreshold changes in the colour, size, shape or location of items in images of natural scenes, using magnitude estimation ratings to characterise the sizes of the perceived changes in coloured photographs. We have implemented a computational model that tries to explain observers' ratings of these naturalistic differences between image pairs. We model the action-potential firing rates of millions of neurons, having linear and non-linear summation behaviour closely modelled on real V1 neurons. The numerical parameters of the model's sigmoidal transducer function are set by optimising the same model to experiments on contrast discrimination (contrast 'dippers') on monochrome photographs of natural scenes. The model, optimised on a stimulus-intensity domain in an experiment reminiscent of the Weber–Fechner relation, then produces tolerable predictions of the ratings for most kinds of naturalistic image change. Importantly, rating rises roughly linearly with the model's numerical output, which represents differences in neuronal firing rate in response to the two images under comparison; this implies that rating is proportional to the neuronal response.

scholarly journals Rapid and active stabilization of visual cortical firing rates across light–dark transitions

2019 ◽  
Vol 116 (36) ◽  
pp. 18068-18077 ◽  
Author(s):  
Alejandro Torrado Pacheco ◽  
Elizabeth I. Tilden ◽  
Sophie M. Grutzner ◽  
Brian J. Lane ◽  
Yue Wu ◽  
...  
Keyword(s):  
Visual Input ◽  
Neuronal Firing ◽  
Natural Scenes ◽  
Dark Light ◽  
Firing Rates ◽  
V1 Neurons ◽  
Natural Vision ◽  
Active Stabilization ◽  
Freely Behaving ◽  
Visual Cortical

The dynamics of neuronal firing during natural vision are poorly understood. Surprisingly, mean firing rates of neurons in primary visual cortex (V1) of freely behaving rodents are similar during prolonged periods of light and darkness, but it is unknown whether this reflects a slow adaptation to changes in natural visual input or insensitivity to rapid changes in visual drive. Here, we use chronic electrophysiology in freely behaving rats to follow individual V1 neurons across many dark–light (D-L) and light–dark (L-D) transitions. We show that, even on rapid timescales (1 s to 10 min), neuronal activity was only weakly modulated by transitions that coincided with the expected 12-/12-h L-D cycle. In contrast, a larger subset of V1 neurons consistently responded to unexpected L-D and D-L transitions, and disruption of the regular L-D cycle with 60 h of complete darkness induced a robust increase in V1 firing on reintroduction of visual input. Thus, V1 neurons fire at similar rates in the presence or absence of natural stimuli, and significant changes in activity arise only transiently in response to unexpected changes in the visual environment. Furthermore, although mean rates were similar in light and darkness, pairwise correlations were significantly stronger during natural vision, suggesting that information about natural scenes in V1 may be more strongly reflected in correlations than individual firing rates. Together, our findings show that V1 firing rates are rapidly and actively stabilized during expected changes in visual input and are remarkably stable at both short and long timescales.

scholarly journals Rapid and Active Stabilization of Visual Cortical Firing Rates Across Light-Dark Transitions

2019 ◽  
Author(s):  
Gina G Turrigiano ◽  
Alejandro Torrado Pacheco ◽  
Elizabeth I Tilden ◽  
Sophie M Gruztner ◽  
Brian Lane ◽  
...  
Keyword(s):  
Visual Input ◽  
Neuronal Firing ◽  
Natural Scenes ◽  
Dark Light ◽  
Firing Rates ◽  
V1 Neurons ◽  
Natural Vision ◽  
Active Stabilization ◽  
Freely Behaving ◽  
Visual Cortical

The dynamics of neuronal firing during natural vision are poorly understood. Surprisingly, mean firing rates of neurons in primary visual cortex (V1) of freely behaving rodents are similar during prolonged periods of light and darkness, but it is unknown whether this reflects a slow adaptation to changes in natural visual input, or insensitivity to rapid changes in visual drive. Here we use chronic electrophysiology in freely behaving rats of either sex to follow individual V1 neurons across many dark-light (D-L) and light-dark (L-D) transitions. We show that, even on rapid timescales (1s to 10 min), neuronal activity was only weakly modulated by transitions that coincided with the expected 12h/12h light-dark cycle. In contrast, a larger subset of V1 neurons consistently responded to unexpected L-D and D-L transitions, and disruption of the regular L-D cycle with 60 hours of complete darkness induced a robust increase in V1 firing upon re-introduction of visual input. Thus, V1 neurons fire at similar rates in the presence or absence of natural stimuli, and significant changes in activity arise only transiently in response to unexpected changes in the visual environment. Further, although mean rates were similar in L and D, pairwise correlations were significantly stronger during natural vision, suggesting that information about natural scenes in V1 is more readily extractable from correlations than from individual firing rates. Together, our findings show that V1 firing rates are rapidly and actively stabilized during expected changes in visual input, and are remarkably stable at both short and long timescales.

scholarly journals Gap junction mediated feed-forward inhibition ensures ultra precise temporal patterning in vocal behavior

2020 ◽  
Author(s):  
Boris P. Chagnaud ◽  
Jonathan Perelmuter ◽  
Paul Forlano ◽  
Andrew H. Bass
Keyword(s):  
Gap Junction ◽  
Activity Patterns ◽  
Super Resolution ◽  
Neuronal Firing ◽  
Action Potential Firing ◽  
Feed Forward ◽  
Temporal Precision ◽  
Potential Firing ◽  
Rapid Modulation ◽  
Glycinergic Inhibition

AbstractPrecise neuronal firing is especially important for behaviors highly dependent on the correct sequencing and timing of muscle activity patterns, such as acoustic signalling. We show that extreme temporal precision in motoneuronal firing within a hindbrain network that directly determines call duration, pulse repetition rate and fundamental frequency in a teleost fish, the Gulf toadfish, depends on gap junction-mediated, feed-forward glycinergic inhibition that generates a period of reduced probability of motoneuron activation. Super-resolution microscopy confirms glycinergic release sites contacting motoneuron somata and dendrites. Synchronous motoneuron activity can also induce action potential firing in pre-motoneurons, a feature that could figure prominently into motor timing. Gap junction-mediated, feed-forward glycinergic inhibition provides a novel means for achieving temporal precision in the millisecond range for rapid modulation of an acoustic signal and perhaps other motor behaviors.

scholarly journals Temporal coupling of field potentials and action potentials in the neocortex

2017 ◽  
Author(s):  
Brendon O. Watson ◽  
Mingxin Ding ◽  
György Buzsáki
Keyword(s):  
Action Potential ◽  
Action Potentials ◽  
Field Potential ◽  
Electromyographic Activity ◽  
Action Potential Firing ◽  
Firing Rates ◽  
Potential Firing ◽  
Temporal Coupling ◽  
Potential Activity ◽  
The Relationship

AbstractThe local field potential (LFP) is an aggregate measure of group neuronal activity and is often correlated with the action potentials of single neurons. In recent years investigators have found that action potential firing rates increase during elevations in power high-frequency band oscillations (50-200 Hz range). However action potentials also contribute to the LFP signal itself, making the spike–LFP relationship complex. Here we examine the relationship between spike rates and LFPs in varying frequency bands in rat neocortical recordings. We find that 50-180Hz oscillations correlate most consistently with high firing rates, but that other LFPs bands also carry information relating to spiking, including in some cases anti-correlations. Relatedly, we find that spiking itself and electromyographic activity contribute to LFP power in these bands. The relationship between spike rates and LFP power varies between brain states and between individual cells. Finally, we create an improved oscillation-based predictor of action potential activity by specifically utilizing information from across the entire recorded frequency spectrum of LFP. The findings illustrate both caveats and improvements to be taken into account in attempts to infer spiking activity from LFP.

scholarly journals A Membrane-Targeted Photoswitch Potently Modulates Neuronal Firing

2019 ◽  
Author(s):  
Mattia L. DiFrancesco ◽  
Francesco Lodola ◽  
Elisabetta Colombo ◽  
Luca Maragliano ◽  
Giuseppe M. Paternò ◽  
...  
Keyword(s):  
Plasma Membrane ◽  
Neural Activity ◽  
Neuronal Firing ◽  
Physiological Effects ◽  
Action Potential Firing ◽  
Polar Groups ◽  
Potential Firing ◽  
Spatio Temporal

ABSTRACTOptical technologies allowing modulation of neuronal activity at high spatio-temporal resolution are becoming paramount in neuroscience. We engineered novel light-sensitive molecules by adding polar groups to a hydrophobic backbone containing azobenzene and azepane moieties. We demonstrate that the probes stably partition into the plasma membrane, with affinity for lipid rafts, and cause thinning of the bilayer through their trans-dimerization in the dark. In neurons pulse-labeled with the compound, light induces a transient hyperpolarization followed by a delayed depolarization that triggers action potential firing. The fast hyperpolarization is attributable to a light-dependent decrease in capacitance due to membrane relaxation that follows disruption of the azobenzene dimers. The physiological effects are persistent and can be evoked in vivo after labeling the mouse somatosensory cortex. These data demonstrate the possibility to trigger neural activity in vitro and in vivo by modulating membrane capacitance, without directly affecting ion channels or local temperature.

scholarly journals The Effect of Correlated Variability on the Accuracy of a Population Code

1999 ◽  
Vol 11 (1) ◽  
pp. 91-101 ◽  
Author(s):  
L. F. Abbott ◽  
Peter Dayan
Keyword(s):  
Firing Rate ◽  
Neuronal Firing ◽  
Population Code ◽  
Firing Rates ◽  
Large Populations ◽  
Neuronal Firing Rate ◽  
General Limit ◽  
Widespread Belief ◽  
The Impact

We study the impact of correlated neuronal firing rate variability on the accuracy with which an encoded quantity can be extracted from a population of neurons. Contrary to widespread belief, correlations in the variabilities of neuronal firing rates do not, in general, limit the increase in coding accuracy provided by using large populations of encoding neurons. Furthermore, in some cases, but not all, correlations improve the accuracy of a population code.

scholarly journals Revaluation of magnetic properties of Magneto, MagR and αGFP−TRPV1/GFP−ferritin

2019 ◽  
Author(s):  
Guangfu Wang ◽  
Peng Zhang ◽  
Suresh K. Mendu ◽  
Yali Wang ◽  
Yajun Zhang ◽  
...  
Keyword(s):  
Action Potentials ◽  
Temporal Correlation ◽  
Neuronal Firing ◽  
Magnetic Control ◽  
Action Potential Firing ◽  
Magnetic Actuators ◽  
Action Mechanisms ◽  
Potential Firing ◽  
Spontaneous Action ◽  
Spontaneous Action Potentials

ABSTRACT AND INTRODUCTIONMagnetic control of neuronal activity offers many obvious advantages over electric, optogenetic and chemogenetic manipulations. A recent series of highly visible papers reported the development of magnetic actuators (i.e., Magneto, MagR and αGFP−TRPV1/GFP−ferritin) that appeared to be effective in controlling neuronal firing1–3, yet their action mechanisms seem to conflict with the principles of physics4. We found that neurons expressing Magneto, MagR and αGFP−TRPV1/GFP−ferritin did not respond to magnetic stimuli with any membrane depolarization (let alone action potential firing), although these neurons frequently generated spontaneous action potentials. Because the previous study did not establish the precise temporal correlation between magnetic stimuli and action potentials in recorded neurons1–3, the reported magnetically-evoked action potentials are likely to represent mismatched spontaneous firings.

scholarly journals Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons

2021 ◽  
Vol 17 (10) ◽  
pp. e1009528
Author(s):  
Ziniu Wu ◽  
Harold Rockwell ◽  
Yimeng Zhang ◽  
Shiming Tang ◽  
Tai Sing Lee
Keyword(s):  
Receptive Field ◽  
Cortical Neurons ◽  
Gabor Filter ◽  
Neuronal Response ◽  
Projection Pursuit ◽  
Response Prediction ◽  
Sparse Code ◽  
Natural Scenes ◽  
V1 Neurons ◽  
Front End

System identification techniques—projection pursuit regression models (PPRs) and convolutional neural networks (CNNs)—provide state-of-the-art performance in predicting visual cortical neurons’ responses to arbitrary input stimuli. However, the constituent kernels recovered by these methods are often noisy and lack coherent structure, making it difficult to understand the underlying component features of a neuron’s receptive field. In this paper, we show that using a dictionary of diverse kernels with complex shapes learned from natural scenes based on efficient coding theory, as the front-end for PPRs and CNNs can improve their performance in neuronal response prediction as well as algorithmic data efficiency and convergence speed. Extensive experimental results also indicate that these sparse-code kernels provide important information on the component features of a neuron’s receptive field. In addition, we find that models with the complex-shaped sparse code front-end are significantly better than models with a standard orientation-selective Gabor filter front-end for modeling V1 neurons that have been found to exhibit complex pattern selectivity. We show that the relative performance difference due to these two front-ends can be used to produce a sensitive metric for detecting complex selectivity in V1 neurons.

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