scholarly journals Hebbian Learning in a Random Network Captures Selectivity Properties of Prefrontal Cortex

2017 ◽  
Author(s):  
Grace W. Lindsay ◽  
Mattia Rigotti ◽  
Melissa R. Warden ◽  
Earl K. Miller ◽  
Stefano Fusi
Keyword(s):  
Prefrontal Cortex ◽  
Hebbian Learning ◽  
Rhesus Macaques ◽  
Random Network ◽  
Learning Rule ◽  
Geometric Interpretation ◽  
Cognitive Behaviors ◽  
Model Match ◽  
The Impact ◽  
Random Connectivity

AbstractComplex cognitive behaviors, such as context-switching and rule-following, are thought to be supported by prefrontal cortex (PFC). Neural activity in PFC must thus be specialized to specific tasks while retaining flexibility. Nonlinear ‘mixed’ selectivity is an important neurophysiological trait for enabling complex and context-dependent behaviors. Here we investigate (1) the extent to which PFC exhibits computationally relevant properties such as mixed selectivity and (2) how such properties could arise via circuit mechanisms. We show that PFC cells recorded from male and female rhesus macaques during a complex task show a moderate level of specialization and structure that is not replicated by a model wherein cells receive random feedforward inputs. While random connectivity can be effective at generating mixed selectivity, the data shows significantly more mixed selectivity than predicted by a model with otherwise matched parameters. A simple Hebbian learning rule applied to the random connectivity, however, increases mixed selectivity and allows the model to match the data more accurately. To explain how learning achieves this, we provide analysis along with a clear geometric interpretation of the impact of learning on selectivity. After learning, the model also matches the data on measures of noise, response density, clustering, and the distribution of selectivities. Of two styles of Hebbian learning tested, the simpler and more biologically plausible option better matches the data. These modeling results give intuition about how neural properties important for cognition can arise in a circuit and make clear experimental predictions regarding how various measures of selectivity would evolve during animal training.Significance StatementPrefrontal cortex (PFC) is a brain region believed to support the ability of animals to engage in complex behavior. How neurons in this area respond to stimuli—and in particular, to combinations of stimuli (”mixed selectivity”)—is a topic of interest. Despite the fact that models with random feedforward connectivity are capable of creating computationally-relevant mixed selectivity, such a model does not match the levels of mixed selectivity seen in the data analyzed in this study. Adding simple Hebbian learning to the model increases mixed selectivity to the correct level and makes the model match the data on several other relevant measures. This study thus offers predictions on how mixed selectivity and other properties evolve with training.

scholarly journals Hebbian Learning in a Random Network Captures Selectivity Properties of the Prefrontal Cortex

2017 ◽  
Vol 37 (45) ◽  
pp. 11021-11036 ◽  
Author(s):  
Grace W. Lindsay ◽  
Mattia Rigotti ◽  
Melissa R. Warden ◽  
Earl K. Miller ◽  
Stefano Fusi
Keyword(s):  
Prefrontal Cortex ◽  
Hebbian Learning ◽  
Random Network

scholarly journals Stable thalamocortical learning between medial-dorsal thalamus and cortical attractor networks captures cognitive flexibility

2021 ◽  
Author(s):  
Siwei Qiu
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Distributed Computing ◽  
Neural Circuit ◽  
Hebbian Learning ◽  
Learning Rule ◽  
Computing System ◽  
Synaptic Scaling ◽  
Dorsal Thalamus ◽  
Medial Dorsal

AbstractPrimates and rodents are able to continually acquire, adapt, and transfer knowledge and skill, and lead to goal-directed behavior during their lifespan. For the case when context switches slowly, animals learn via slow processes. For the case when context switches rapidly, animals learn via fast processes. We build a biologically realistic model with modules similar to a distributed computing system. Specifically, we are emphasizing the role of thalamocortical learning on a slow time scale between the prefrontal cortex (PFC) and medial dorsal thalamus (MD). Previous work [1] has already shown experimental evidence supporting classification of cell ensembles in the medial dorsal thalamus, where each class encodes a different context. However, the mechanism by which such classification is learned is not clear. In this work, we show that such learning can be self-organizing in the manner of an automaton (a distributed computing system), via a combination of Hebbian learning and homeostatic synaptic scaling. We show that in the simple case of two contexts, the network with hierarchical structure can do context-based decision making and smooth switching between different contexts. Our learning rule creates synaptic competition [2] between the thalamic cells to create winner-take-all activity. Our theory shows that the capacity of such a learning process depends on the total number of task-related hidden variables, and such a capacity is limited by system size N. We also theoretically derived the effective functional connectivity as a function of an order parameter dependent on the thalamo-cortical coupling structure.Significance StatementAnimals need to adapt to dynamically changing environments and make decisions based on changing contexts. Here we propose a combination of neural circuit structure with learning mechanisms to account for such behaviors. Specifically, we built a reservoir computing network improved by a Hebbian learning rule together with a synaptic scaling learning mechanism between the prefrontal cortex and the medial-dorsal (MD) thalamus. This model shows that MD thalamus is crucial in such context-based decision making. I also make use of dynamical mean field theory to predict the effective neural circuit. Furthermore, theoretical analysis provides a prediction that the capacity of such a network increases with the network size and the total number of tasks-related latent variables.

scholarly journals A Geometric Perspective on Information Plane Analysis

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 711
Author(s):  
Mina Basirat ◽  
Bernhard C. Geiger ◽  
Peter M. Roth
Keyword(s):  
Neural Network ◽  
Mutual Information ◽  
Geometric Interpretation ◽  
Neural Network Training ◽  
Neural Network Learning ◽  
Network Learning ◽  
Plane Analysis ◽  
Network Training ◽  
Hidden Layer ◽  
The Impact

Information plane analysis, describing the mutual information between the input and a hidden layer and between a hidden layer and the target over time, has recently been proposed to analyze the training of neural networks. Since the activations of a hidden layer are typically continuous-valued, this mutual information cannot be computed analytically and must thus be estimated, resulting in apparently inconsistent or even contradicting results in the literature. The goal of this paper is to demonstrate how information plane analysis can still be a valuable tool for analyzing neural network training. To this end, we complement the prevailing binning estimator for mutual information with a geometric interpretation. With this geometric interpretation in mind, we evaluate the impact of regularization and interpret phenomena such as underfitting and overfitting. In addition, we investigate neural network learning in the presence of noisy data and noisy labels.

scholarly journals A 1-Month Ketogenic Diet Increased Mitochondrial Mass in Red Gastrocnemius Muscle, but not in the Brain or Liver of Middle-Aged Mice

Nutrients ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 2533
Author(s):  
Zeyu Zhou ◽  
Jocelyn Vidales ◽  
José A González-Reyes ◽  
Bradley Shibata ◽  
Keith Baar ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Gastrocnemius Muscle ◽  
Left Lobe ◽  
Middle Aged ◽  
Mitochondrial Content ◽  
Mitochondrial Mass ◽  
Aged Mice ◽  
Fractional Area ◽  
Ketogenic Diets ◽  
The Impact

Alterations in markers of mitochondrial content with ketogenic diets (KD) have been reported in tissues of rodents, but morphological quantification of mitochondrial mass using transmission electron microscopy (TEM), the gold standard for mitochondrial quantification, is needed to further validate these findings and look at specific regions of interest within a tissue. In this study, red gastrocnemius muscle, the prefrontal cortex, the hippocampus, and the liver left lobe were used to investigate the impact of a 1-month KD on mitochondrial content in healthy middle-aged mice. The results showed that in red gastrocnemius muscle, the fractional area of both subsarcolemmal (SSM) and intermyofibrillar (IMM) mitochondria was increased, and this was driven by an increase in the number of mitochondria. Mitochondrial fractional area or number was not altered in the liver, prefrontal cortex, or hippocampus following 1 month of a KD. These results demonstrate tissue-specific changes in mitochondrial mass with a short-term KD and highlight the need to study different muscle groups or tissue regions with TEM to thoroughly determine the effects of a KD on mitochondrial mass.

scholarly journals Developmental up-regulation of NMDA receptors in the prefrontal cortex and hippocampus of mGlu5 receptor knock-out mice

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Tiziana Imbriglio ◽  
Remy Verhaeghe ◽  
Nico Antenucci ◽  
Stefania Maccari ◽  
Giuseppe Battaglia ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Nmda Receptor ◽  
Metabotropic Glutamate Receptors ◽  
Adult Life ◽  
Developmental Time ◽  
Postnatal Life ◽  
Developmental Studies ◽  
Knock Out ◽  
Nmda Receptor Subunits ◽  
The Impact

AbstractmGlu5 metabotropic glutamate receptors are highly expressed and functional in the early postnatal life, and are known to positively modulate NMDA receptor function. Here, we examined the expression of NMDA receptor subunits and interneuron-related genes in the prefrontal cortex and hippocampus of mGlu5−/− mice and wild-type littermates at three developmental time points (PND9, − 21, and − 75). We were surprised to find that expression of all NMDA receptor subunits was greatly enhanced in mGlu5−/− mice at PND21. In contrast, at PND9, expression of the GluN2B subunit was enhanced, whereas expression of GluN2A and GluN2D subunits was reduced in both regions. These modifications were transient and disappeared in the adult life (PND75). Changes in the transcripts of interneuron-related genes (encoding parvalbumin, somatostatin, vasoactive intestinal peptide, reelin, and the two isoforms of glutamate decarboxylase) were also observed in mGlu5−/− mice across postnatal development. For example, the transcript encoding parvalbumin was up-regulated in the prefrontal cortex of mGlu5−/− mice at PND9 and PND21, whereas it was significantly reduced at PND75. These findings suggest that in mGlu5−/− mice a transient overexpression of NMDA receptor subunits may compensate for the lack of the NMDA receptor partner, mGlu5. Interestingly, in mGlu5−/− mice the behavioral response to the NMDA channel blocker, MK-801, was significantly increased at PND21, and largely reduced at PND75. The impact of adaptive changes in the expression of NMDA receptor subunits should be taken into account when mGlu5−/− mice are used for developmental studies.

Insulin resistance induced by long-term sleep deprivation in rhesus macaques can be attenuated by Bifidobacterium

2021 ◽  
Author(s):  
Ying Zhao ◽  
Yan Shu ◽  
Ning Zhao ◽  
Zili Zhou ◽  
Xiong Jia ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Circadian Rhythm ◽  
Blood Glucose ◽  
Glucose Metabolism ◽  
Sleep Deprivation ◽  
Rhesus Macaques ◽  
Intravenous Glucose ◽  
Increased Risk ◽  
The Impact

Long-term sleep deprivation (SD) is a bad lifestyle habit, especially among specific occupational practitioners, characterized by circadian rhythm misalignment and abnormal sleep/wake cycles. SD is closely associated with an increased risk of metabolic disturbance, particularly obesity and insulin resistance. The incretin hormone, glucagon-like peptide-1 (GLP-1), is a critical insulin release determinant secreted by the intestinal L-cell upon food intake. Besides, the gut microbiota participates in metabolic homeostasis and regulates GLP-1 release in a circadian rhythm manner. As a commonly recognized intestinal probiotic, Bifidobacterium has various clinical indications regarding its curative effect. However, few studies have investigated the effect of Bifidobacterium supplementation on sleep disorders. In the present study, we explored the impact of long-term SD on the endocrine metabolism of rhesus monkeys and determined the effect of Bifidobacterium supplementation on the SD-induced metabolic status. Lipids concentrations, body weight, fast blood glucose, and insulin levels increased after SD. Furthermore, after two months of long-term SD, the intravenous glucose tolerance test (iVGTT) showed that the glucose metabolism was impaired and the insulin sensitivity decreased. Moreover, one month of Bifidobacterium oral administration significantly reduced blood glucose and attenuated insulin resistance in rhesus macaques. Overall, our results suggested that Bifidobacterium might be used to alleviate SD-induced aberrant glucose metabolism and improve insulin resistance. Also, it might help in better understanding the mechanisms governing the beneficial effects of Bifidobacterium.

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