scholarly journals Identifying the most influential features of neural population responses for information encoding and behavior

2019 ◽  
Author(s):  
Ramon Nogueira ◽  
Nicole E. Peltier ◽  
Akiyuki Anzai ◽  
Gregory C. DeAngelis ◽  
Julio Martínez-Trujillo ◽  
...  
Keyword(s):  
Brain Regions ◽  
Neural Population ◽  
Behavioral Performance ◽  
Information Encoding ◽  
Inverse Population ◽  
Population Responses ◽  
Amount Of Information ◽  
Neuronal Populations ◽  
And Behavior ◽  
Global Activity

SummaryIdentifying the features of population responses that are relevant to the amount of information encoded by neuronal populations is a crucial step toward understanding the neural code. Statistical features such as tuning properties, individual and shared response variability, and global activity modulations could all affect the amount of information encoded and modulate behavioral performance. We show that two features in particular affect information: the modulation of population responses across conditions and the projection of the inverse population variability along the modulation axis. We demonstrate that fluctuations of these two quantities are correlated with fluctuations of behavioral performance in various tasks and brain regions. In contrast, fluctuations in mean correlations among neurons and global activity have negligible or inconsistent effects on the amount of information encoded and behavioral performance. Our results are consistent with predictions of a model that optimally decodes population responses, which suggests that in our behavioral tasks the readout of information is near-optimal.

Reason for optimism: How a shifting focus on neural population codes is moving cognitive neuroscience beyond phrenology

2016 ◽  
Vol 39 ◽  
Author(s):  
Carolyn Parkinson ◽  
Thalia Wheatley
Keyword(s):  
Cognitive Neuroscience ◽  
Brain Function ◽  
Pattern Analysis ◽  
Brain Regions ◽  
Neural Population ◽  
Multivariate Pattern Analysis ◽  
Population Responses ◽  
Population Codes ◽  
Multivariate Pattern ◽  
Insight Into

AbstractMultivariate pattern analysis can address many of the challenges for cognitive neuroscience highlighted in After Phrenology (Anderson 2014) by illuminating the information content of brain regions and by providing insight into whether functional overlap reflects the recruitment of common or distinct computational mechanisms. Further, failing to consider submaximal but reliable population responses can lead to an overly modular account of brain function.

scholarly journals Learning is shaped by abrupt changes in neural engagement

2020 ◽  
Author(s):  
Jay A. Hennig ◽  
Emily R. Oby ◽  
Matthew D. Golub ◽  
Lindsay A. Bahureksa ◽  
Patrick T. Sadtler ◽  
...  
Keyword(s):  
Neural Activity ◽  
Internal State ◽  
Neural Population ◽  
Behavioral Performance ◽  
Population Activity ◽  
Large Fluctuations ◽  
Abrupt Changes ◽  
Internal States ◽  
Task Goals ◽  
And Behavior

AbstractInternal states such as arousal, attention, and motivation are known to modulate brain-wide neural activity, but how these processes interact with learning is not well understood. During learning, the brain must modify the neural activity it produces to improve behavioral performance. How do internal states affect the evolution of this learning process? Using a brain-computer interface (BCI) learning paradigm in non-human primates, we identified large fluctuations in neural population activity in motor cortex (M1) indicative of arousal-like internal state changes. These fluctuations drove population activity along dimensions we term neural engagement axes. Neural engagement increased abruptly at the start of learning, and then gradually retreated. In a BCI, the causal relationship between neural activity and behavior is known. This allowed us to understand how these changes impacted behavioral performance for different task goals. We found that neural engagement interacted with learning, helping to explain why animals learned some task goals more quickly than others.

scholarly journals Understanding Emotions: Origins and Roles of the Amygdala

Biomolecules ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 823
Author(s):  
Goran Šimić ◽  
Mladenka Tkalčić ◽  
Vana Vukić ◽  
Damir Mulc ◽  
Ena Španić ◽  
...  
Keyword(s):  
Sensory Information ◽  
Anterior Cingulate ◽  
Central Nucleus ◽  
Subcortical Structures ◽  
Neuronal Populations ◽  
Efferent Projections ◽  
Short And Long Term ◽  
Understanding Emotions ◽  
Fight Or Flight Response ◽  
And Behavior

Emotions arise from activations of specialized neuronal populations in several parts of the cerebral cortex, notably the anterior cingulate, insula, ventromedial prefrontal, and subcortical structures, such as the amygdala, ventral striatum, putamen, caudate nucleus, and ventral tegmental area. Feelings are conscious, emotional experiences of these activations that contribute to neuronal networks mediating thoughts, language, and behavior, thus enhancing the ability to predict, learn, and reappraise stimuli and situations in the environment based on previous experiences. Contemporary theories of emotion converge around the key role of the amygdala as the central subcortical emotional brain structure that constantly evaluates and integrates a variety of sensory information from the surroundings and assigns them appropriate values of emotional dimensions, such as valence, intensity, and approachability. The amygdala participates in the regulation of autonomic and endocrine functions, decision-making and adaptations of instinctive and motivational behaviors to changes in the environment through implicit associative learning, changes in short- and long-term synaptic plasticity, and activation of the fight-or-flight response via efferent projections from its central nucleus to cortical and subcortical structures.

scholarly journals Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

2021 ◽  
Vol 11 (7) ◽  
pp. 885
Author(s):  
Maher Abujelala ◽  
Rohith Karthikeyan ◽  
Oshin Tyagi ◽  
Jing Du ◽  
Ranjana K. Mehta
Keyword(s):  
Machine Learning ◽  
Environmental Conditions ◽  
Brain Activity ◽  
Memory Task ◽  
Classification Problem ◽  
Brain Regions ◽  
Training Data ◽  
Information Encoding ◽  
Machine Learning Approach ◽  
Encoding And Retrieval

The nature of firefighters` duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees’ safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of 0.844 and an accuracy of 79.10% if the training and testing data are obtained at similar environmental conditions. However, the algorithm’s performance dropped to an F-1 score of 0.723 and accuracy of 60.61% when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.

Social Status and Arginine Vasotocin Neuronal Phenotypes in a Cichlid Fish

2015 ◽  
Vol 85 (3) ◽  
pp. 203-213 ◽  
Author(s):  
Olinda Almeida ◽  
Rui F. Oliveira
Keyword(s):  
Social Status ◽  
Social Behaviour ◽  
Cichlid Fish ◽  
Brain Regions ◽  
Cell Number ◽  
Arginine Vasotocin ◽  
Cichlid Species ◽  
Neuronal Populations ◽  
Soma Size ◽  
Species Specific

The nonapeptide arginine vasotocin (AVT) and its mammalian homologue arginine vasopressin play a key role in the regulation of social behaviour across vertebrates. In teleost fishes, three AVT neuronal populations have been described in the preoptic area (POA): the parvocellular (pPOA), the magnocellular (mPOA) and the gigantocellular (gPOA). Neurons from each of these areas project both to the pituitary and to other brain regions, where AVT is supposed to regulate neural circuits underlying social behaviour. However, in the fish species studied so far, there is considerable variation in which AVT neuronal populations are involved in behavioural modulation and in the direction of the effect. In this study, the association between AVT neuronal phenotypes and social status was investigated in the Mozambique tilapia (Oreochromis mossambicus). This species is an African female mouth-brooding cichlid fish in which males form breeding aggregations in which dominant males establish territories and subordinate males to act as floaters. With respect to sex differences in AVT neuronal phenotypes, females have a larger number of AVT neurons in the pPOA and mPOA. Within males, AVT appeared associated with social subordination, as indicated by the larger cell body areas of AVT neurons in mPOA and gPOA nuclei of non-territorial males. There were also positive correlations between submissive behaviour and the soma size of AVT cells in all three nuclei and AVT cell number in the mPOA. In summary, the results provide evidence for an involvement of AVT in the modulation of social behaviour in tilapia, but it was not possible to identify specific roles for specific AVT neuronal populations. The results presented here also contrast with those previously published for another cichlid species with a similar mating system, which highlights the species-specific nature of the pattern of association between AVT and social behaviour even within the same taxonomic family.

scholarly journals AMPed-up adolescents: the role of age in the abuse of amphetamines and its consequences on cognition and prefrontal cortex development

2020 ◽  
Author(s):  
Sara Ruth Westbrook ◽  
Lauren Carrica ◽  
Asia Banks ◽  
Joshua Michael Gulley
Keyword(s):  
Prefrontal Cortex ◽  
Animal Studies ◽  
Drug Effects ◽  
Epidemiological Studies ◽  
Brain Regions ◽  
Vulnerability Factors ◽  
Brain And Behavior ◽  
And Behavior ◽  
Detrimental Impact

Adolescent use of amphetamine and its closely related, methylated version methamphetamine, is alarmingly high in those who use drugs for nonmedical purposes. This raises serious concerns about the potential for this drug use to have a long-lasting, detrimental impact on the normal development of the brain and behavior that is ongoing during adolescence. In this review, we explore recent findings from both human and laboratory animal studies that investigate the consequences of amphetamine and methamphetamine exposure during this stage of life. We highlight studies that assess sex differences in adolescence, as well as those that are designed specifically to address the potential unique effects of adolescent exposure by including groups at other life stages (typically young adulthood). We consider epidemiological studies on age and sex as vulnerability factors for developing problems with the use of amphetamines, as well as human and animal laboratory studies that tap into age differences in use, its short-term effects on behavior, and the long-lasting consequences of this exposure on cognition. We also focus on studies of drug effects in the prefrontal cortex, which is known to be critically important for cognition and is among the later maturing brain regions. Finally, we discuss important issues that should be addressed in future studies so that the field can further our understanding of the mechanisms underlying adolescent use of amphetamines and its outcomes on the developing brain and behavior.

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