scholarly journals Perceptual detection depends on spike count integration

2019 ◽  
Author(s):  
Jackson J. Cone ◽  
Morgan L. Bade ◽  
Nicolas Y. Masse ◽  
Elizabeth A. Page ◽  
David J. Freedman ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cerebral Cortex ◽  
Visual Cortex ◽  
Recurrent Neural Networks ◽  
Neural Circuit ◽  
Visual Detection ◽  
Spike Count ◽  
Neuronal Responses ◽  
Neuronal Populations ◽  
And Behavior

AbstractWhenever the retinal image changes some neurons in visual cortex increase their rate of firing, while others decrease their rate of firing. Linking specific sets of neuronal responses with perception and behavior is essential for understanding mechanisms of neural circuit computation. We trained mice to perform visual detection tasks and used optogenetic perturbations to increase or decrease neuronal spiking primary visual cortex (V1). Perceptual reports were always enhanced by increments in V1 spike counts and impaired by decrements, even when increments and decrements were delivered to the same neuronal populations. Moreover, detecting changes in cortical activity depended on spike count integration rather than instantaneous changes in spiking. Recurrent neural networks trained in the task similarly relied on increments in neuronal activity when activity was costly. This work clarifies neuronal decoding strategies employed by cerebral cortex to translate cortical spiking into percepts that can be used to guide behavior.

scholarly journals An Intracortical Implantable Brain-Computer Interface for Telemetric Real-Time Recording and Manipulation of Neuronal Circuits for Closed-Loop Intervention

2021 ◽  
Vol 15 ◽  
Author(s):  
Hamed Zaer ◽  
Ashlesha Deshmukh ◽  
Dariusz Orlowski ◽  
Wei Fan ◽  
Pierre-Hugues Prouvot ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Real Time ◽  
Neuronal Activity ◽  
Closed Loop ◽  
Neural Circuit ◽  
Radio Communication ◽  
Computer Interfaces ◽  
Cerebral Mri ◽  
High Bandwidth ◽  
And Behavior

Recording and manipulating neuronal ensemble activity is a key requirement in advanced neuromodulatory and behavior studies. Devices capable of both recording and manipulating neuronal activity brain-computer interfaces (BCIs) should ideally operate un-tethered and allow chronic longitudinal manipulations in the freely moving animal. In this study, we designed a new intracortical BCI feasible of telemetric recording and stimulating local gray and white matter of visual neural circuit after irradiation exposure. To increase the translational reliance, we put forward a Göttingen minipig model. The animal was stereotactically irradiated at the level of the visual cortex upon defining the target by a fused cerebral MRI and CT scan. A fully implantable neural telemetry system consisting of a 64 channel intracortical multielectrode array, a telemetry capsule, and an inductive rechargeable battery was then implanted into the visual cortex to record and manipulate local field potentials, and multi-unit activity. We achieved a 3-month stability of the functionality of the un-tethered BCI in terms of telemetric radio-communication, inductive battery charging, and device biocompatibility for 3 months. Finally, we could reliably record the local signature of sub- and suprathreshold neuronal activity in the visual cortex with high bandwidth without complications. The ability to wireless induction charging combined with the entirely implantable design, the rather high recording bandwidth, and the ability to record and stimulate simultaneously put forward a wireless BCI capable of long-term un-tethered real-time communication for causal preclinical circuit-based closed-loop interventions.

scholarly journals The Effect of Attention on Neuronal Responses to High and Low Contrast Stimuli

2010 ◽  
Vol 104 (2) ◽  
pp. 960-971 ◽  
Author(s):  
Joonyeol Lee ◽  
John H. R. Maunsell
Keyword(s):  
Cerebral Cortex ◽  
Visual Cortex ◽  
Rhesus Monkeys ◽  
Receptive Fields ◽  
Visual Area ◽  
Neuronal Responses ◽  
Attentional Modulation ◽  
Low Contrast ◽  
Middle Temporal ◽  
Visual Area Mt

It remains unclear how attention affects the tuning of individual neurons in visual cerebral cortex. Some observations suggest that attention preferentially enhances responses to low contrast stimuli, whereas others suggest that attention proportionally affects responses to all stimuli. Resolving how attention affects responses to different stimuli is essential for understanding the mechanism by which it acts. To explore the effects of attention on stimuli of different contrasts, we recorded from individual neurons in the middle temporal visual area (MT) of rhesus monkeys while shifting their attention between preferred and nonpreferred stimuli within their receptive fields. This configuration results in robust attentional modulation that makes it possible to readily distinguish whether attention acts preferentially on low contrast stimuli. We found no evidence for greater enhancement of low contrast stimuli. Instead, the strong attentional modulations were well explained by a model in which attention proportionally enhances responses to stimuli of all contrasts. These data, together with observations on the effects of attention on responses to other stimulus dimensions, suggest that the primary effect of attention in visual cortex may be to simply increase the strength of responses to all stimuli by the same proportion.

scholarly journals Learning and attention reveal a general relationship between neuronal variability and perception

2017 ◽  
Author(s):  
Amy M. Ni ◽  
Douglas A. Ruff ◽  
Joshua J. Alberts ◽  
Jen Symmonds ◽  
Marlene R. Cohen
Keyword(s):  
Visual Cortex ◽  
Neuronal Population ◽  
Population Coding ◽  
Task Demands ◽  
Spike Count ◽  
Current Debate ◽  
Visually Guided ◽  
Population Activity ◽  
Neuronal Populations ◽  
And Performance

The trial-to-trial response variability that is shared between pairs of neurons (termed spike count correlations1 or rSC) has been the subject of many recent studies largely because it might limit the amount of information that can be encoded by neuronal populations. Spike count correlations are flexible and change depending on task demands2-7. However, the relationship between correlated variability and information coding is a matter of current debate2-14. This debate has been difficult to resolve because testing the theoretical predictions would require simultaneous recordings from an experimentally unfeasible number of neurons. We hypothesized that if correlated variability limits population coding, then spike count correlations in visual cortex should a) covary with subjects’ performance on visually guided tasks and b) lie along the dimensions in neuronal population space that contain information that is used to guide behavior. We focused on two processes that are known to improve visual performance: visual attention, which allows observers to focus on important parts of a visual scene15-17, and perceptual learning, which slowly improves observers’ ability to discriminate specific, well-practiced stimuli18-20. Both attention and learning improve performance on visually guided tasks, but the two processes operate on very different timescales and are typically studied using different perceptual tasks. Here, by manipulating attention and learning in the same task, subjects, trials, and neuronal populations, we show that there is a single, robust relationship between correlated variability in populations of visual neurons and performance on a change-detection task. We also propose an explanation for the mystery of how correlated variability might affect performance: it is oriented along the dimensions of population space used by the animal to make perceptual decisions. Our results suggest that attention and learning affect the same aspects of the neuronal population activity in visual cortex, which may be responsible for learning- and attention-related improvements in behavioral performance. More generally, our study provides a framework for leveraging the activity of simultaneously recorded populations of neurons, cognitive factors, and perceptual decisions to understand the neuronal underpinnings of behavior.

scholarly journals Distinct waking states for strong evoked responses in primary visual cortex and optimal visual detection performance

2018 ◽  
Author(s):  
Garrett T. Neske ◽  
David A. McCormick
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Detection ◽  
Detection Performance ◽  
Neuronal Membrane ◽  
Evoked Responses ◽  
Perceptual Decision Making ◽  
Full Spectrum ◽  
Sensory Stimuli ◽  
And Behavior

AbstractVariability in cortical neuronal responses to sensory stimuli and in perceptual decision making performance is substantial. Moment-to-moment fluctuations in waking state or arousal can account for much of this variability. Yet, the nature of this variability across the full spectrum of waking states is often not completely characterized, leaving the characteristics of the optimal state for sensory processing unresolved. Using pupillometry in concert with extracellular multiunit and intracellular whole-cell recordings, we found that the magnitude and reliability of visually evoked responses in primary visual cortex (V1) of awake, passively behaving male mice increase as a function of arousal and are largest during sustained locomotion periods. During these high-arousal, sustained locomotion periods, cortical neuronal membrane potential was at its most depolarized and least variable. Contrastingly, behavioral performance of mice on two distinct visual detection tasks was generally best at a range of intermediate arousal levels, but worst during locomotion. These results suggest that large, reliable responses to visual stimuli in V1 occur at a distinct arousal level from that associated with optimal visual detection performance. Our results clarify the relation between neuronal responsiveness and the continuum of waking states, and suggest new complexities in the relation between primary sensory cortical activity and behavior.

scholarly journals Neuronal population mechanisms of lightness perception

2018 ◽  
Vol 120 (5) ◽  
pp. 2296-2310 ◽  
Author(s):  
Douglas A. Ruff ◽  
David H. Brainard ◽  
Marlene R. Cohen
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Neuronal Population ◽  
Neuronal Responses ◽  
Lightness Perception ◽  
Neuronal Mechanism ◽  
Area V4 ◽  
Neuronal Populations ◽  
Types Of Information ◽  
The Way

The way that humans and animals perceive the lightness of an object depends on its physical luminance as well as its surrounding context. While neuronal responses throughout the visual pathway are modulated by context, the relationship between neuronal responses and lightness perception is poorly understood. We searched for a neuronal mechanism of lightness by recording responses of neuronal populations in monkey primary visual cortex (V1) and area V4 to stimuli that produce a lightness illusion in humans, in which the lightness of a disk depends on the context in which it is embedded. We found that the way individual units encode the luminance (or equivalently for our stimuli, contrast) of the disk and its context is extremely heterogeneous. This motivated us to ask whether the population representation in either V1 or V4 satisfies three criteria: 1) disk luminance is represented with high fidelity, 2) the context surrounding the disk is also represented, and 3) the representations of disk luminance and context interact to create a representation of lightness that depends on these factors in a manner consistent with human psychophysical judgments of disk lightness. We found that populations of units in both V1 and V4 fulfill the first two criteria but that we cannot conclude that the two types of information in either area interact in a manner that clearly predicts human psychophysical measurements: the interpretation of our population measurements depends on how subsequent areas read out lightness from the population responses. NEW & NOTEWORTHY A core question in visual neuroscience is how the brain extracts stable representations of object properties from the retinal image. We searched for a neuronal mechanism of lightness perception by determining whether the responses of neuronal populations in primary visual cortex and area V4 could account for a lightness illusion measured using human psychophysics. Our results suggest that comparing psychophysics with population recordings will yield insight into neuronal mechanisms underlying a variety of perceptual phenomena.

scholarly journals Neuronal population mechanisms of lightness perception

2018 ◽  
Author(s):  
Douglas A. Ruff ◽  
David H. Brainard ◽  
Marlene R. Cohen
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Neuronal Population ◽  
Neuronal Responses ◽  
Lightness Perception ◽  
Neuronal Mechanism ◽  
Area V4 ◽  
Neuronal Populations ◽  
Types Of Information ◽  
The Way

AbstractThe way that humans and animals perceive the lightness of an object depends on its physical luminance as well as its surrounding context. While neuronal responses throughout the visual pathway are modulated by context, the relationship between neuronal responses and lightness perception is poorly understood. We searched for a neuronal mechanism of lightness by recording responses of neuronal populations in monkey primary visual cortex (V1) and area V4 to stimuli that produce a lightness illusion in humans, in which the lightness of a disk depends on the context in which it is embedded. We found that the way individual units encode the luminance (or equivalently for our stimuli, contrast) of the disk and its context is extremely heterogeneous. This motivated us to ask whether the population representation in either V1 or V4 satisfies three criteria: 1) disk luminance is represented with high fidelity, 2) the context surrounding the disk is also represented, and 3) the representations of disk luminance and context interact to create a representation of lightness that depends on these factors in a manner consistent with human psychophysical judgments of disk lightness. We found that populations of units in both V1 and V4 fulfill the first two criteria, but that we cannot conclude that the two types of information in either area interact in a manner that clearly predicts human psychophysical measurements: the interpretation of our population measurements depends on how subsequent areas read out lightness from the population responses.New & NoteworthyA core question in visual neuroscience is how the brain extracts stable representations of object properties from the retinal image. We searched for a neuronal mechanism of lightness perception by determining whether the responses of neuronal populations in primary visual cortex and area V4 could account for a lightness illusion measured using human psychophysics. Our results suggest that comparing psychophysics with population recordings will yield insight into neuronal mechanisms underlying a variety of perceptual phenomena.

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

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