scholarly journals Revealing nonlinear neural decoding by analyzing choices

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qianli Yang ◽  
Edgar Walker ◽  
R. James Cotton ◽  
Andreas S. Tolias ◽  
Xaq Pitkow
Keyword(s):  
Cortical Neurons ◽  
Quantitative Relationship ◽  
Neural Decoding ◽  
Sensory Data ◽  
Behavioral Choices ◽  
Population Codes ◽  
Relevant Variables ◽  
Task Irrelevant ◽  
Decoding Efficiency ◽  
The Brain

AbstractSensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. The neurons that encode these relevant signals typically constitute a nonlinear population code. Here we present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information. Our theory obeys fundamental mathematical limitations on information content inherited from the sensory periphery, describing redundant codes when there are many more cortical neurons than primary sensory neurons. The theory predicts that if the brain uses its nonlinear population codes optimally, then more informative patterns should be more correlated with choices. More specifically, the theory predicts a simple, easily computed quantitative relationship between fluctuating neural activity and behavioral choices that reveals the decoding efficiency. This relationship holds for optimal feedforward networks of modest complexity, when experiments are performed under natural nuisance variation. We analyze recordings from primary visual cortex of monkeys discriminating the distribution from which oriented stimuli were drawn, and find these data are consistent with the hypothesis of near-optimal nonlinear decoding.

scholarly journals Revealing nonlinear neural decoding by analyzing choices

2018 ◽  
Author(s):  
Qianli Yang ◽  
Edgar Walker ◽  
R. James Cotton ◽  
Andreas S. Tolias ◽  
Xaq Pitkow
Keyword(s):  
Cortical Neurons ◽  
Quantitative Relationship ◽  
Neural Decoding ◽  
Sensory Data ◽  
Behavioral Choices ◽  
Population Codes ◽  
Relevant Variables ◽  
Task Irrelevant ◽  
Decoding Efficiency ◽  
The Brain

Sensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. The neurons that encode these relevant signals typically constitute a nonlinear population code. Here we present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information. Our theory obeys fundamental mathematical limitations on information content inherited from the sensory periphery, identifying redundant codes when there are many more cortical neurons than primary sensory neurons. The theory predicts that if the brain uses its nonlinear population codes optimally, then more informative patterns should be more correlated with choices. More specifically, the theory predicts a simple, easily computed quantitative relationship between fluctuating neural activity and behavioral choices that reveals the decoding efficiency. We analyze recordings from primary visual cortex of monkeys discriminating the distribution from which oriented stimuli were drawn, and find these data are consistent with the hypothesis of near-optimal nonlinear decoding.

Introduction

1998 ◽  
Author(s):  
Christof Koch
Keyword(s):  
Membrane Potential ◽  
Nerve Cells ◽  
Neuronal Firing ◽  
Western World ◽  
Long Term Memory ◽  
Sensory Data ◽  
Relevant Variables ◽  
Elementary Circuit ◽  
Circuit Designer ◽  
The Brain

The brain computes! This is accepted as a truism by the majority of neuroscientists engaged in discovering the principles employed in the design and operation of nervous systems. What is meant here is that any brain takes the incoming sensory data, encodes them into various biophysical variables, such as the membrane potential or neuronal firing rates, and subsequently performs a very large number of ill-specified operations, frequently termed computations, on these variables to extract relevant features from the input. The outcome of some of these computations can be stored for later access and will, ultimately, control the motor output of the animal in appropriate ways. The present book is dedicated to understanding in detail the biophysical mechanisms responsible for these computations. Its scope is the type of information processing underlying perception and motor control, occurring at the millisecond to fraction of a second time scale. When you look at a pair of stereo images trying to fuse them into a binocular percept, your brain is busily computing away trying to find the “best” solution. What are the computational primitives at the neuronal and subneuronal levels underlying this impressive performance, unmatched by any machine? Naively put and using the language of the electronic circuit designer, the book asks: “What are the diodes and the transistors of the brain?” and “What sort of operations do these elementary circuit elements implement?” Contrary to received opinion, nerve cells are considerably more complex than suggested by work in the neural network community. Like morons, they are reduced to computing nothing but a thresholded sum of their inputs. We know, for instance, that individual nerve cells in the locust perform an operation akin to a multiplication. Given synapses, ionic channels, and membranes, how is this actually carried out? How do neurons integrate, delay, or change their output gain? What are the relevant variables that carry information? The membrane potential? The concentration of intracellular Ca2+ ions? What is their temporal resolution? And how large is the variability of these signals that determines how accurately they can encode information? And what variables are used to store the intermediate results of these computations? And where does long-term memory reside? Natural philosophers and scientists in the western world have always compared the brain to the most advanced technology of the day.

scholarly journals Synaptic diversity naturally arises from neural decoding of heterogeneous populations

2021 ◽  
Author(s):  
Jacob L. Yates ◽  
Benjamin Scholl
Keyword(s):  
Dendritic Spines ◽  
Cortical Neurons ◽  
In Silico ◽  
Excitatory Synapses ◽  
Neural Decoding ◽  
Functional Heterogeneity ◽  
Synaptic Inputs ◽  
Population Codes ◽  
Optimal Decoding

Abstract The synaptic inputs to single cortical neurons exhibit substantial diversity in their sensory-driven activity. What this diversity reflects is unclear, and appears counter-productive in generating selective somatic responses to specific stimuli. We propose that synaptic diversity arises because neurons decode information from upstream populations. Focusing on a single sensory variable, orientation, we construct a probabilistic decoder that estimates the stimulus orientation from the responses of a realistic, hypothetical input population of neurons. We provide a straightforward mapping from the decoder weights to real excitatory synapses, and find that optimal decoding requires diverse input weights. Analytically derived weights exhibit diversity whenever upstream input populations consist of noisy, correlated, and heterogeneous neurons, as is typically found in vivo. In fact, in silico weight diversity was necessary to accurately decode orientation and matched the functional heterogeneity of dendritic spines imaged in vivo. Our results indicate that synaptic diversity is a necessary component of information transmission and reframes studies of connectivity through the lens of probabilistic population codes. These results suggest that the mapping from synaptic inputs to somatic selectivity may not be directly interpretable without considering input covariance and highlights the importance of population codes in pursuit of the cortical connectome.

Effect of elevated potassium on the ion content of mouse astrocytes and neurons

1992 ◽  
Vol 70 (S1) ◽  
pp. S263-S268 ◽  
Author(s):  
H. Steve White ◽  
Sien Yao Chow ◽  
Y. C. Yen-Chow ◽  
Dixon M. Woodbury
Keyword(s):  
Cortical Neurons ◽  
Cell Types ◽  
Mouse Cell ◽  
Ion Concentration ◽  
Cellular Heterogeneity ◽  
Resting Membrane Potential ◽  
Extracellular Potassium ◽  
Individual Response ◽  
Extracellular Potassium Concentration ◽  
The Brain

Potassium is tightly regulated within the extracellular compartment of the brain. Nonetheless, it can increase 3- to 4-fold during periods of intense seizure activity and 10- to 20-fold under certain pathological conditions such as spreading depression. Within the central nervous system, neurons and astrocytes are both affected by shifts in the extracellular concentration of potassium. Elevated potassium can lead to a redistribution of other ions (e.g., calcium, sodium, chloride, hydrogen, etc.) within the cellular compartment of the brain. Small shifts in the extracellular potassium concentration can markedly affect acid–base homeostasis, energy metabolism, and volume regulation of these two brain cells. Since normal neuronal function is tightly coupled to the ability of the surrounding glial cells to regulate ionic shifts within the brain and since both cell types can be affected by shifts in the extracellular potassium, it is important to characterize their individual response to an elevation of this ion. This review describes the results of side-by-side studies conducted on cortical neurons and astrocytes, which assessed the effect of elevated potassium on their resting membrane potential, intracellular volume, and their intracellular concentration of potassium, sodium, and chloride. The results obtained from these studies suggest that there exists a marked cellular heterogeneity between neurons and astrocytes in their response to an elevation in the extracellular potassium concentration.Key words: astrocytes, neurons, ion concentration, neuronal–glial interactions, mouse, cell culture.

scholarly journals Brain signatures of surprise in EEG and MEG data

2020 ◽  
Author(s):  
Zahra Mousavi ◽  
Mohammad Mahdi Kiani ◽  
Hamid Aghajan
Keyword(s):  
Sensory Modality ◽  
Ideal Observer ◽  
Neural Signals ◽  
Sensory Data ◽  
Temporal Features ◽  
Recorded Data ◽  
Recent Trends ◽  
Temporal Sample ◽  
Past Experiences ◽  
The Brain

AbstractThe brain is constantly anticipating the future of sensory inputs based on past experiences. When new sensory data is different from predictions shaped by recent trends, neural signals are generated to report this surprise. Existing models for quantifying surprise are based on an ideal observer assumption operating under one of the three definitions of surprise set forth as the Shannon, Bayesian, and Confidence-corrected surprise. In this paper, we analyze both visual and auditory EEG and auditory MEG signals recorded during oddball tasks to examine which temporal components in these signals are sufficient to decode the brain’s surprise based on each of these three definitions. We found that for both recording systems the Shannon surprise is always significantly better decoded than the Bayesian surprise regardless of the sensory modality and the selected temporal features used for decoding.Author summaryA regression model is proposed for decoding the level of the brain’s surprise in response to sensory sequences using selected temporal components of recorded EEG and MEG data. Three surprise quantification definitions (Shannon, Bayesian, and Confidence-corrected surprise) are compared in offering decoding power. Four different regimes for selecting temporal samples of EEG and MEG data are used to evaluate which part of the recorded data may contain signatures that represent the brain’s surprise in terms of offering a high decoding power. We found that both the middle and late components of the EEG response offer strong decoding power for surprise while the early components are significantly weaker in decoding surprise. In the MEG response, we found that the middle components have the highest decoding power while the late components offer moderate decoding powers. When using a single temporal sample for decoding surprise, samples of the middle segment possess the highest decoding power. Shannon surprise is always better decoded than the other definitions of surprise for all the four temporal feature selection regimes. Similar superiority for Shannon surprise is observed for the EEG and MEG data across the entire range of temporal sample regimes used in our analysis.

scholarly journals 6B4 proteoglycan/phosphacan is a repulsive substratum but promotes morphological differentiation of cortical neurons

Development ◽  
1996 ◽  
Vol 122 (2) ◽  
pp. 647-658
Author(s):  
N. Maeda ◽  
M. Noda
Keyword(s):  
Cell Adhesion ◽  
Neurite Outgrowth ◽  
Cortical Neurons ◽  
Tyrosine Phosphatase ◽  
Neuronal Cell ◽  
Morphological Differentiation ◽  
Cell Types ◽  
Thalamic Neurons ◽  
Neurite Extension ◽  
The Brain

6B4 proteoglycan/phosphacan is one of the major phosphate-buffered saline-soluble chondroitin sulfate proteoglycans of the brain. Recently, this molecule has been demonstrated to be an extracellular variant of the proteoglycan-type protein tyrosine phosphatase, PTPzeta (RPTPbeta). The influence of the 6B4 proteoglycan, adsorbed onto the substratum, on cell adhesion and neurite outgrowth was studied using dissociated neurons from the cerebral cortex and thalamus. 6B4 proteoglycan adsorbed onto plastic tissue culture dishes did not support neuronal cell adhesion, but rather exerted repulsive effects on cortical and thalamic neurons. When neurons were densely seeded on patterned substrata consisting of a grid-like structure of alternating poly-L-lysine and 6B4 proteoglycan-coated poly-L-lysine domains, they were concentrated on the poly-L-lysine domains. However, 6B4 proteoglycan did not retard the differentiation of neurons but rather promoted neurite outgrowth and development of the dendrites of cortical neurons, when neurons were sparsely seeded on poly-L-lysine-conditioned coverslips continuously coated with 6B4 proteoglycan. This effect of 6B4 proteoglycan on the neurite extension of cortical neurons was apparent even on coverslips co-coated with fibronectin or tenascin. By contrast, the neurite extension of thalamic neurons was not modified by 6B4 proteoglycan. Chondroitinase ABC or keratanase digestion of 6B4 proteoglycan did not affect its neurite outgrowth promoting activity, but a polyclonal antibody against 6B4 proteoglycan completely suppressed this activity, suggesting that a protein moiety is responsible for the activity. 6B4 proteoglycan transiently promoted tyrosine phosphorylation of an 85x10(3) Mr protein in the cortical neurons, which correlated with the induction of neurite outgrowth. These results suggest that 6B4 proteoglycan/phosphacan modulates morphogenesis and differentiation of neurons dependent on its spatiotemporal distribution and the cell types in the brain.

scholarly journals Neuronal Correlates of Multiple Top–Down Signals during Covert Tracking of Moving Objects in Macaque Prefrontal Cortex

2012 ◽  
Vol 24 (10) ◽  
pp. 2043-2056 ◽  
Author(s):  
Ayano Matsushima ◽  
Masaki Tanaka
Keyword(s):  
Prefrontal Cortex ◽  
Object Tracking ◽  
Visual Processing ◽  
Moving Objects ◽  
Top Down ◽  
Selective Suppression ◽  
Internal Object ◽  
Object Segregation ◽  
Task Irrelevant ◽  
The Brain

Resistance to distraction is a key component of executive functions and is strongly linked to the prefrontal cortex. Recent evidence suggests that neural mechanisms exist for selective suppression of task-irrelevant information. However, neuronal signals related to selective suppression have not yet been identified, whereas nonselective surround suppression, which results from attentional enhancement for relevant stimuli, has been well documented. This study examined single neuron activities in the lateral PFC when monkeys covertly tracked one of randomly moving objects. Although many neurons responded to the target, we also found a group of neurons that exhibited a selective response to the distractor that was visually identical to the target. Because most neurons were insensitive to an additional distractor that explicitly differed in color from the target, the brain seemed to monitor the distractor only when necessary to maintain internal object segregation. Our results suggest that the lateral PFC might provide at least two top–down signals during covert object tracking: one for enhancement of visual processing for the target and the other for selective suppression of visual processing for the distractor. These signals might work together to discriminate objects, thereby regulating both the sensitivity and specificity of target choice during covert object tracking.

scholarly journals Scapinin-induced Inhibition of Axon Elongation Is Attenuated by Phosphorylation and Translocation to the Cytoplasm

2011 ◽  
Vol 286 (22) ◽  
pp. 19724-19734 ◽  
Author(s):  
Hovik Farghaian ◽  
Yu Chen ◽  
Ada W. Y. Fu ◽  
Amy K. Y. Fu ◽  
Jacque P. K. Ip ◽  
...  
Keyword(s):  
Cortical Neurons ◽  
Brain Cortex ◽  
Inhibitory Effect ◽  
Adult Brain ◽  
Actin Binding ◽  
Mutant Form ◽  
Axon Elongation ◽  
Rat Cortical Neurons ◽  
The Brain

Scapinin is an actin- and PP1-binding protein that is exclusively expressed in the brain; however, its function in neurons has not been investigated. Here we show that expression of scapinin in primary rat cortical neurons inhibits axon elongation without affecting axon branching, dendritic outgrowth, or polarity. This inhibitory effect was dependent on its ability to bind actin because a mutant form that does not bind actin had no effect on axon elongation. Immunofluorescence analysis showed that scapinin is predominantly located in the distal axon shaft, cell body, and nucleus of neurons and displays a reciprocal staining pattern to phalloidin, consistent with previous reports that it binds actin monomers to inhibit polymerization. We show that scapinin is phosphorylated at a highly conserved site in the central region of the protein (Ser-277) by Cdk5 in vitro. Expression of a scapinin phospho-mimetic mutant (S277D) restored normal axon elongation without affecting actin binding. Instead, phosphorylated scapinin was sequestered in the cytoplasm of neurons and away from the axon. Because its expression is highest in relatively plastic regions of the adult brain (cortex, hippocampus), scapinin is a new regulator of neurite outgrowth and neuroplasticity in the brain.

scholarly journals Modulator of apoptosis-1 is a potential therapeutic target in acute ischemic injury

2018 ◽  
Vol 39 (12) ◽  
pp. 2406-2418 ◽  
Author(s):  
Su Jing Chan ◽  
Hui Zhao ◽  
Kazuhide Hayakawa ◽  
Chou Chai ◽  
Chong Teik Tan ◽  
...  
Keyword(s):  
Cortical Neurons ◽  
Infarct Volume ◽  
Neuronal Loss ◽  
Ischemic Injury ◽  
Glucose Deprivation ◽  
Artery Occlusion ◽  
In Vivo Models ◽  
The Brain

Modulator of apoptosis 1 (MOAP-1) is a Bax-associating protein highly enriched in the brain. In this study, we examined the role of MOAP-1 in promoting ischemic injuries following a stroke by investigating the consequences of MOAP-1 overexpression or deficiency in in vitro and in vivo models of ischemic stroke. MOAP-1 overexpressing SH-SY5Y cells showed significantly lower cell viability following oxygen and glucose deprivation (OGD) treatment when compared to control cells. Consistently, MOAP-1−/− primary cortical neurons were observed to be more resistant against OGD treatment than the MOAP-1+/+ primary neurons. In the mouse transient middle cerebral artery occlusion (tMCAO) model, ischemia triggered MOAP-1/Bax association, suggested activation of the MOAP-1-dependent apoptotic cascade. MOAP-1−/− mice were found to exhibit reduced neuronal loss and smaller infarct volume 24 h after tMCAO when compared to MOAP-1+/+ mice. Correspondingly, MOAP-1−/− mice also showed better integrity of neurological functions as demonstrated by their performance in the rotarod test. Therefore, both in vitro and in vivo data presented strongly support the conclusion that MOAP-1 is an important apoptotic modulator in ischemic injury. These results may suggest that a reduction of MOAP-1 function in the brain could be a potential therapeutic approach in the treatment of acute stroke.

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