scholarly journals Fundamental bounds on learning performance in neural circuits

2018 ◽  
Author(s):  
Dhruva V. Raman ◽  
Timothy O’Leary
Keyword(s):  
Neural Circuits ◽  
Neural Circuit ◽  
Brain Size ◽  
Intrinsic Noise ◽  
Network Size ◽  
Learning Ability ◽  
Learning Performance ◽  
Optimal Size ◽  
Biologically Relevant ◽  
Learning Capacity

AbstractHow does the size of a neural circuit influence its learning performance? Intuitively, we expect the learning capacity of a neural circuit to grow with the number of neurons and synapses. Larger brains tend to be found in species with higher cognitive function and learning ability. Similarly, adding connections and units to artificial neural networks can allow them to solve more complex tasks. However, we show that in a biologically relevant setting where synapses introduce an unavoidable amount of noise, there is an optimal size of network for a given task. Beneath this optimal size, our analysis shows how adding apparently redundant neurons and connections can make tasks more learnable. Therefore large neural circuits can either devote connectivity to generating complex behaviors, or exploit this connectivity to achieve faster and more precise learning of simpler behaviors. Above the optimal network size, the addition of neurons and synaptic connections starts to impede learning performance. This suggests that overall brain size may be constrained by the need to learn efficiently with unreliable synapses, and may explain why some neurological learning deficits are associated with hyperconnectivity. Our analysis is independent of specific learning rules and uncovers fundamental relationships between learning rate, task performance, network size and intrinsic noise in neural circuits.

scholarly journals Fundamental bounds on learning performance in neural circuits

2019 ◽  
Vol 116 (21) ◽  
pp. 10537-10546 ◽  
Author(s):  
Dhruva Venkita Raman ◽  
Adriana Perez Rotondo ◽  
Timothy O’Leary
Keyword(s):  
Neural Circuits ◽  
Neural Circuit ◽  
Intrinsic Noise ◽  
Network Size ◽  
Learning Ability ◽  
Learning Performance ◽  
Optimal Size ◽  
Biologically Relevant ◽  
Learning Capacity ◽  
Optimal Network

How does the size of a neural circuit influence its learning performance? Larger brains tend to be found in species with higher cognitive function and learning ability. Intuitively, we expect the learning capacity of a neural circuit to grow with the number of neurons and synapses. We show how adding apparently redundant neurons and connections to a network can make a task more learnable. Consequently, large neural circuits can either devote connectivity to generating complex behaviors or exploit this connectivity to achieve faster and more precise learning of simpler behaviors. However, we show that in a biologically relevant setting where synapses introduce an unavoidable amount of noise, there is an optimal size of network for a given task. Above the optimal network size, the addition of neurons and synaptic connections starts to impede learning performance. This suggests that the size of brain circuits may be constrained by the need to learn efficiently with unreliable synapses and provides a hypothesis for why some neurological learning deficits are associated with hyperconnectivity. Our analysis is independent of specific learning rules and uncovers fundamental relationships between learning rate, task performance, network size, and intrinsic noise in neural circuits.

Some Behavioral Differences in Mice Genetically Selected for High and Low Brain Weight

1966 ◽  
Vol 19 (3) ◽  
pp. 675-681 ◽  
Author(s):  
Cynthia Wimer ◽  
Lee Prater
Keyword(s):  
Locomotor Activity ◽  
Water Maze ◽  
Brain Size ◽  
Genetic Selection ◽  
Brain Weight ◽  
Learning Ability ◽  
Learning Performance ◽  
Behavioral Differences ◽  
Total Brain ◽  
High Selection

Learning ability, exploratory behavior, and emotionality were measured in mice genetically selected for high and low total brain weight. The high selection lines scored significantly higher than the low lines in locomotor activity in the open field and discrimination learning performance in a water maze, and these findings were supported by correlations between brain weight and behavioral scores within unselected control lines. There is some evidence that these behavioral differences are associated with general changes in brain size produced by genetic selection.

scholarly journals Correlations between Hypothalamus-Pituitary-Adrenal Axis Parameters Depend on Age and Learning Capacity

Endocrinology ◽  
2005 ◽  
Vol 146 (3) ◽  
pp. 1372-1381 ◽  
Author(s):  
O. C. Meijer ◽  
B. Topic ◽  
P. J. Steenbergen ◽  
G. Jocham ◽  
J. P. Huston ◽  
...  
Keyword(s):  
Glucocorticoid Receptor ◽  
Hpa Axis ◽  
Heat Shock Protein 90 ◽  
Learning Ability ◽  
Learning Performance ◽  
Spatial Learning And Memory ◽  
Learning Capacity ◽  
Steroid Receptor Coactivator ◽  
Male Wistar Rats ◽  
Corticosteroid Receptor

Glucocorticoid hormones are released after activation of the hypothalamus-pituitary-adrenal (HPA) axis and in the brain can modulate synaptic plasticity and memory formation. Clear individual differences in spatial learning and memory in the water maze allowed classification of groups of young (3 months) and aged (24 months) male Wistar rats as superior and inferior learners. We tested 1) whether measures of HPA activity are associated with cognitive functions and aging and 2) whether correlations of these measures depend on age and learning performance. Basal ACTH, but not corticosterone, was increased in aged rats, with the stress-induced ACTH response exaggerated in aged-inferior learners. Aged-superior learners had lower expression of glucocorticoid receptor and CRH mRNA in the parvocellular paraventricular nucleus of the hypothalamus compared with all other groups. Hippocampal mineralocorticoid receptor and glucocorticoid receptor mRNAs differed modestly between groups, but steroid receptor coactivator and heat-shock-protein 90 mRNAs were not different. Strikingly, correlations between HPA axis markers were dependent on grouping animals according to learning performance or age. CRH mRNA correlated with ACTH only in aged animals. Parvocellular arginine vasopressin mRNA was negatively correlated to basal corticosterone, except in aged-inferior learners. Corticosteroid receptor mRNA expression showed a number of correlations with other HPA axis regulators specifically in superior learners. In summary, the relationships between HPA axis markers differ for subgroups of animals. These distinct interdependencies may reflect adjusted set-points of the HPA axis, resulting in adaptation (or maladaptation) to the environment and, possibly, an age-independent determination of learning ability.

scholarly journals No sex differences in learning in wild bumblebees

2021 ◽  
Author(s):  
Felicity Muth ◽  
Amber D Tripodi ◽  
Rene Bonilla ◽  
James P Strange ◽  
Anne S Leonard
Keyword(s):  
Sex Differences ◽  
Associative Learning ◽  
Sierra Nevada ◽  
Comparative Cognition ◽  
Natural Populations ◽  
Interspecific Variation ◽  
Learning Task ◽  
Learning Ability ◽  
Learning Performance ◽  
Males And Females

Abstract Females and males often face different sources of selection, resulting in dimorphism in morphological, physiological, and even cognitive traits. Sex differences are often studied in respect to spatial cognition, yet the different ecological roles of males and females might shape cognition in multiple ways. For example, in dietary generalist bumblebees (Bombus), the ability to learn associations is critical to female workers, who face informationally rich foraging scenarios as they collect nectar and pollen from thousands of flowers over a period of weeks to months to feed the colony. While male bumblebees likely need to learn associations as well, they only forage for themselves while searching for potential mates. It is thus less clear whether foraging males would benefit from the same associative learning performance as foraging females. In this system, as in others, cognitive performance is typically studied in lab-reared animals under captive conditions, which may not be representative of patterns in the wild. In the first test of sex and species differences in cognition using wild bumblebees, we compared the performance of Bombus vancouverensis nearcticus (formerly bifarius) and Bombus vosnesenskii of both sexes on an associative learning task at Sierra Nevada (CA) field sites. Across both species, we found that males and females did not differ in their ability to learn, although males were slower to respond to the sucrose reward. These results offer the first evidence from natural populations that male bumblebees may be equally as able to learn associations as females, supporting findings from captive colonies of commercial bees. The observed interspecific variation in learning ability opens the door to using the Bombus system to test hypotheses about comparative cognition.

scholarly journals Blended Learning Innovation Model among College Students Based on Internet

2018 ◽  
Vol 13 (10) ◽  
pp. 158
Author(s):  
Jia Zhang
Keyword(s):  
Blended Learning ◽  
Preliminary Investigation ◽  
Learning Model ◽  
Learning Ability ◽  
Learning Performance ◽  
Specific Situation ◽  
Teaching Mode ◽  
Network Teaching ◽  
Self Evaluation ◽  
Innovation Model

In general, over 70% of students can adapt to this blended learning model after experiencing the blended learning model for some time, which can satisfy the in-dividual differences of students in a better way, attain some assistance from it and help to improve learning performance and learning ability. It can be discovered from this research that the blended learning model is superior to the single and traditional teaching mode or the pure network teaching mode in the aspects of in-spiring the learning interest of students, exercising self-management capability of students and self-evaluation ability. It can be seen from the specific situation of investigation data that it is feasible to implement the blended learning model in colleges and universities even though the overall level of students’ ability in the blended learning is low. As this is a preliminary investigation into the blended learning model, specific solutions or strategies have not been provided for some problems. However, it is believed to achieve greater effects if the research is con-tinued on the practice.

scholarly journals Oscillatory integration windows in neurons

2016 ◽  
Author(s):  
Nitin Gupta ◽  
Swikriti Saran Singh ◽  
Mark Stopfer
Keyword(s):  
Neural Circuits ◽  
Neural Circuit ◽  
Field Potential ◽  
Coincidence Detection ◽  
Uniform Structure ◽  
Detection Mechanism ◽  
Oscillation Frequencies ◽  
Local Field Potential Lfp ◽  
Oscillatory Cycle

AbstractOscillatory synchrony among neurons occurs in many species and brain areas, and has been proposed to help neural circuits process information. One hypothesis states that oscillatory input creates cyclic integration windows: specific times in each oscillatory cycle when postsynaptic neurons become especially responsive to inputs. With paired local field potential (LFP) and intracellular recordings and controlled stimulus manipulations we directly tested this idea in the locust olfactory system. We found that inputs arriving in Kenyon cells (KCs) sum most effectively in a preferred window of the oscillation cycle. With a computational model, we found that the non-uniform structure of noise in the membrane potential helps mediate this process. Further experiments performed in vivo demonstrated that integration windows can form in the absence of inhibition and at a broad range of oscillation frequencies. Our results reveal how a fundamental coincidence-detection mechanism in a neural circuit functions to decode temporally organized spiking.

scholarly journals Prominent Inhibitory Projections Guide Sensorimotor Computation: An Invertebrate Perspective

2019 ◽  
Author(s):  
Samantha Hughes ◽  
Tansu Celikel
Keyword(s):  
Motor Neurons ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Sensory Information ◽  
Circuit Complexity ◽  
Inhibitory Circuits ◽  
Invertebrate Species ◽  
Gating Mechanism ◽  
Motor Actions ◽  
Sensorimotor Representations

From single-cell organisms to complex neural networks, all evolved to provide control solutions to generate context and goal-specific actions. Neural circuits performing sensorimotor computation to drive navigation employ inhibitory control as a gating mechanism, as they hierarchically transform (multi)sensory information into motor actions. Here, we focus on this literature to critically discuss the proposition that prominent inhibitory projections form sensorimotor circuits. After reviewing the neural circuits of navigation across various invertebrate species, we argue that with increased neural circuit complexity and the emergence of parallel computations inhibitory circuits acquire new functions. The contribution of inhibitory neurotransmission for navigation goes beyond shaping the communication that drives motor neurons, instead, include encoding of emergent sensorimotor representations. A mechanistic understanding of the neural circuits performing sensorimotor computations in invertebrates will unravel the minimum circuit requirements driving adaptive navigation.

Artificial Intelligence Based on Biological Neurons

2020 ◽  
pp. 25-53
Author(s):  
Rinat Galiautdinov
Keyword(s):  
Artificial Intelligence ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Next Generation ◽  
New Approach ◽  
Home Based

The chapter describes the new approach in artificial intelligence based on simulated biological neurons and creation of the neural circuits for the sphere of IoT which represent the next generation of artificial intelligence and IoT. Unlike existing technical devices for implementing a neuron based on classical nodes oriented to binary processing, the proposed path is based on simulation of biological neurons, creation of biologically close neural circuits where every device will implement the function of either a sensor or a “muscle” in the frame of the home-based live AI and IoT. The research demonstrates the developed nervous circuit constructor and its usage in building of the AI (neural circuit) for IoT.

scholarly journals The role of dietary niacin intake and the adenosine-5′-diphosphate-ribosyl cyclase enzyme CD38 in spatial learning ability: is cyclic adenosine diphosphate ribose the link between diet and behaviour?

2008 ◽  
Vol 21 (1) ◽  
pp. 42-55 ◽  
Author(s):  
Genevieve S. Young ◽  
James B. Kirkland
Keyword(s):  
Spatial Learning ◽  
Young Male ◽  
Adenosine Diphosphate ◽  
Pyridine Nucleotide ◽  
Learning Ability ◽  
Learning Performance ◽  
Male Rats ◽  
Current Evidence ◽  
Niacin Deficiency ◽  
The Brain

The pyridine nucleotide NAD+is derived from dietary niacin and serves as the substrate for the synthesis of cyclic ADP-ribose (cADPR), an intracellular Ca signalling molecule that plays an important role in synaptic plasticity in the hippocampus, a region of the brain involved in spatial learning. cADPR is formed in part via the activity of the ADP-ribosyl cyclase enzyme CD38, which is widespread throughout the brain. In the present review, current evidence of the relationship between dietary niacin and behaviour is presented following investigations of the effect of niacin deficiency, pharmacological nicotinamide supplementation and CD38 gene deletion on brain nucleotides and spatial learning ability in mice and rats. In young male rats, both niacin deficiency and nicotinamide supplementation significantly altered brain NAD+and cADPR, both of which were inversely correlated with spatial learning ability. These results were consistent across three different models of niacin deficiency (pair feeding, partially restricted feeding and niacin recovery). Similar changes in spatial learning ability were observed inCd38− / − mice, which also showed decreases in brain cADPR. These findings suggest an inverse relationship between spatial learning ability, dietary niacin intake and cADPR, although a direct link between cADPR and spatial learning ability is still missing. Dietary niacin may therefore play a role in the molecular events regulating learning performance, and further investigations of niacin intake, CD38 and cADPR may help identify potential molecular targets for clinical intervention to enhance learning and prevent or reverse cognitive decline.

scholarly journals A novel device for real-time measurement and manipulation of licking behavior in head-fixed mice

2018 ◽  
Vol 120 (6) ◽  
pp. 2975-2987 ◽  
Author(s):  
Brice Williams ◽  
Anderson Speed ◽  
Bilal Haider
Keyword(s):  
Real Time ◽  
Neural Activity ◽  
Closed Loop ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Visual Behavior ◽  
Sensory Stimuli ◽  
Neural Recordings ◽  
Control Signals ◽  
Sensory Motor

The mouse has become an influential model system for investigating the mammalian nervous system. Technologies in mice enable recording and manipulation of neural circuits during tasks where they respond to sensory stimuli by licking for liquid rewards. Precise monitoring of licking during these tasks provides an accessible metric of sensory-motor processing, particularly when combined with simultaneous neural recordings. There are several challenges in designing and implementing lick detectors during head-fixed neurophysiological experiments in mice. First, mice are small, and licking behaviors are easily perturbed or biased by large sensors. Second, neural recordings during licking are highly sensitive to electrical contact artifacts. Third, submillisecond lick detection latencies are required to generate control signals that manipulate neural activity at appropriate time scales. Here we designed, characterized, and implemented a contactless dual-port device that precisely measures directional licking in head-fixed mice performing visual behavior. We first determined the optimal characteristics of our detector through design iteration and then quantified device performance under ideal conditions. We then tested performance during head-fixed mouse behavior with simultaneous neural recordings in vivo. We finally demonstrate our device’s ability to detect directional licks and generate appropriate control signals in real time to rapidly suppress licking behavior via closed-loop inhibition of neural activity. Our dual-port detector is cost effective and easily replicable, and it should enable a wide variety of applications probing the neural circuit basis of sensory perception, motor action, and learning in normal and transgenic mouse models. NEW & NOTEWORTHY Mice readily learn tasks in which they respond to sensory cues by licking for liquid rewards; tasks that involve multiple licking responses allow study of neural circuits underlying decision making and sensory-motor integration. Here we design, characterize, and implement a novel dual-port lick detector that precisely measures directional licking in head-fixed mice performing visual behavior, enabling simultaneous neural recording and closed-loop manipulation of licking.

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