scholarly journals A neural population mechanism for rapid learning

2017 ◽  
Author(s):  
Matthew G. Perich ◽  
Juan A. Gallego ◽  
Lee E. Miller
Keyword(s):  
Brain Area ◽  
Population Level ◽  
Neural Population ◽  
Reaching Movement ◽  
Neural Populations ◽  
Show Evidence ◽  
Preparatory Activity ◽  
Plastic Changes ◽  
The Brain

AbstractLong-term learning of language, mathematics, and motor skills likely requires plastic changes in the cortex, but behavior often requires faster changes, sometimes based even on single errors. Here, we show evidence of one mechanism by which the brain can rapidly develop new motor output, seemingly without altering the functional connectivity between or within cortical areas. We recorded simultaneously from hundreds of neurons in the premotor (PMd) and primary motor (M1) cortices, and computed models relating these neural populations throughout adaptation to reaching movement perturbations. We found a signature of learning in the “null subspace” of PMd with respect to M1. Earlier experiments have shown that null subspace activity allows the motor cortex to alter preparatory activity without directly influencing M1. In our experiments, the null subspace planning activity evolved with the adaptation, yet the “potent” mapping that captures information sent to M1 was preserved. Our results illustrate a population-level mechanism within the motor cortices to adjust the output from one brain area to its downstream structures that could be exploited throughout the brain for rapid, online behavioral adaptation.

scholarly journals The Geometry of Information Coding in Correlated Neural Populations

2021 ◽  
Vol 44 (1) ◽  
Author(s):  
Rava Azeredo da Silveira ◽  
Fred Rieke
Keyword(s):  
Neural Coding ◽  
Neural Population ◽  
Annual Review ◽  
Publication Date ◽  
Information Coding ◽  
Population Code ◽  
New Approach ◽  
Neural Populations ◽  
The Brain ◽  
Theoretical Developments

Neurons in the brain represent information in their collective activity. The fidelity of this neural population code depends on whether and how variability in the response of one neuron is shared with other neurons. Two decades of studies have investigated the influence of these noise correlations on the properties of neural coding. We provide an overview of the theoretical developments on the topic. Using simple, qualitative, and general arguments, we discuss, categorize, and relate the various published results. We emphasize the relevance of the fine structure of noise correlation, and we present a new approach to the issue. Throughout this review, we emphasize a geometrical picture of how noise correlations impact the neural code. Expected final online publication date for the Annual Review of Neuroscience, Volume 44 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals New neural activity patterns emerge with long-term learning

2019 ◽  
Vol 116 (30) ◽  
pp. 15210-15215 ◽  
Author(s):  
Emily R. Oby ◽  
Matthew D. Golub ◽  
Jay A. Hennig ◽  
Alan D. Degenhart ◽  
Elizabeth C. Tyler-Kabara ◽  
...  
Keyword(s):  
Neural Activity ◽  
Activity Patterns ◽  
Causal Link ◽  
Neural Population ◽  
Population Activity ◽  
The Brain ◽  
Neural Population Activity

Learning has been associated with changes in the brain at every level of organization. However, it remains difficult to establish a causal link between specific changes in the brain and new behavioral abilities. We establish that new neural activity patterns emerge with learning. We demonstrate that these new neural activity patterns cause the new behavior. Thus, the formation of new patterns of neural population activity can underlie the learning of new skills.

scholarly journals Midkine Is a Novel Regulator of Amphetamine-Induced Striatal Gliosis and Cognitive Impairment: Evidence for a Stimulus-Dependent Regulation of Neuroinflammation by Midkine

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Marta Vicente-Rodríguez ◽  
Rosalía Fernández-Calle ◽  
Esther Gramage ◽  
Carmen Pérez-García ◽  
María P. Ramos ◽  
...  
Keyword(s):  
Brain Area ◽  
Memory Task ◽  
Long Term Effects ◽  
Y Maze ◽  
Amphetamine Treatment ◽  
Microglial Response ◽  
First Time ◽  
The Brain

Midkine (MK) is a cytokine that modulates amphetamine-induced striatal astrogliosis, suggesting a possible role of MK in neuroinflammation induced by amphetamine. To test this hypothesis, we studied astrogliosis and microglial response induced by amphetamine (10 mg/kg i.p. four times, every 2 h) in different brain areas of MK−/− mice and wild type (WT) mice. We found that amphetamine-induced microgliosis and astrocytosis are enhanced in the striatum of MK−/− mice in a region-specific manner. Surprisingly, LPS-induced astrogliosis in the striatum was blocked in MK−/− mice. Since striatal neuroinflammation induced by amphetamine-type stimulants correlates with the cognitive deficits induced by these drugs, we also tested the long-term effects of periadolescent amphetamine treatment (3 mg/kg i.p. daily for 10 days) in a memory task in MK−/− and WT mice. Significant deficits in the Y-maze test were only observed in amphetamine-pretreated MK−/− mice. The data demonstrate for the first time that MK is a novel modulator of neuroinflammation depending on the inflammatory stimulus and the brain area considered. The data indicate that MK limits amphetamine-induced striatal neuroinflammation. In addition, our data demonstrate that periadolescent amphetamine treatment in mice results in transient disruption of learning and memory processes in absence of endogenous MK.

scholarly journals Scaling of information in large neural populations reveals signatures of information-limiting correlations

2020 ◽  
Author(s):  
MohammadMehdi Kafashan ◽  
Anna Jaffe ◽  
Selmaan N. Chettih ◽  
Ramon Nogueira ◽  
Iñigo Arandia-Romero ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Stimulus ◽  
Brain Area ◽  
Correlated Noise ◽  
Neuronal Populations ◽  
Total Information ◽  
Neural Populations ◽  
The Brain ◽  
Stimulus Direction

AbstractHow is information distributed across large neuronal populations within a given brain area? One possibility is that information is distributed roughly evenly across neurons, so that total information scales linearly with the number of recorded neurons. Alternatively, the neural code might be highly redundant, meaning that total information saturates. Here we investigated how information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex (V1). We found that information scales sublinearly, due to the presence of correlated noise in these populations. Using recent theoretical advances, we compartmentalized noise correlations into information-limiting and nonlimiting components, and then extrapolated to predict how information grows when neural populations are even larger. We predict that tens of thousands of neurons are required to encode 95% of the information about visual stimulus direction, a number much smaller than the number of neurons in V1. Overall, these findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most information from smaller subpopulations.

scholarly journals Behavioral prioritization enhances working memory precision and neural population gain

2021 ◽  
Author(s):  
Aspen H. Yoo ◽  
Alfredo Bolaños ◽  
Grace E. Hallenbeck ◽  
Masih Rahmati ◽  
Thomas C. Sprague ◽  
...  
Keyword(s):  
Working Memory ◽  
Brain Area ◽  
Brain Regions ◽  
Delay Period ◽  
Common Finding ◽  
Neural Population ◽  
Association Cortex ◽  
Absolute Amount ◽  
Neural Populations ◽  
Memory Precision

ABSTRACTHumans allocate visual working memory (WM) resource according to behavioral relevance, resulting in more precise memories for more important items. Theoretically, items may be maintained by feature-tuned neural populations, where the relative gain of the populations encoding each item determines precision. To test this hypothesis, we compared the amplitudes of delay-period activity in the different parts of retinotopic maps representing each of several WM items, predicting amplitude would track with behavioral priority. Using fMRI, we scanned participants while they remembered the location of multiple items over a WM delay, then reported the location of one probed item using a memory-guided saccade. Importantly, items were not equally probable to be probed (0.6, 0.3, 0.1, 0.0), which was indicated with a pre-cue. We analyzed fMRI activity in ten visual field maps in occipital, parietal, and frontal cortex known to be important for visual WM. In early visual cortex, but not association cortex, the amplitude of BOLD activation within voxels corresponding to the retinotopic location of visual WM items increased with the priority of the item. Interestingly, these results were contrasted with a common finding that higher-level brain regions had greater delay-period activity, demonstrating a dissociation between the absolute amount of activity in a brain area, and the activity of different spatially-selective populations within it. These results suggest that the distribution of WM resources according to priority sculpts the relative gains of neural populations that encode items, offering a neural mechanism for how prioritization impacts memory precision.

scholarly journals Behavioral Prioritization Enhances Working Memory Precision and Neural Population Gain

2021 ◽  
pp. 1-14
Author(s):  
Aspen H. Yoo ◽  
Alfredo Bolaños ◽  
Grace E. Hallenbeck ◽  
Masih Rahmati ◽  
Thomas C. Sprague ◽  
...  
Keyword(s):  
Working Memory ◽  
Brain Area ◽  
Brain Regions ◽  
Delay Period ◽  
Common Finding ◽  
Neural Population ◽  
Association Cortex ◽  
Absolute Amount ◽  
Neural Populations ◽  
Memory Precision

Abstract Humans allocate visual working memory (WM) resource according to behavioral relevance, resulting in more precise memories for more important items. Theoretically, items may be maintained by feature-tuned neural populations, where the relative gain of the populations encoding each item determines precision. To test this hypothesis, we compared the amplitudes of delay period activity in the different parts of retinotopic maps representing each of several WM items, predicting the amplitudes would track behavioral priority. Using fMRI, we scanned participants while they remembered the location of multiple items over a WM delay and then reported the location of one probed item using a memory-guided saccade. Importantly, items were not equally probable to be probed (0.6, 0.3, 0.1, 0.0), which was indicated with a precue. We analyzed fMRI activity in 10 visual field maps in occipital, parietal, and frontal cortex known to be important for visual WM. In early visual cortex, but not association cortex, the amplitude of BOLD activation within voxels corresponding to the retinotopic location of visual WM items increased with the priority of the item. Interestingly, these results were contrasted with a common finding that higher-level brain regions had greater delay period activity, demonstrating a dissociation between the absolute amount of activity in a brain area and the activity of different spatially selective populations within it. These results suggest that the distribution of WM resources according to priority sculpts the relative gains of neural populations that encode items, offering a neural mechanism for how prioritization impacts memory precision.

scholarly journals Self-Healing Neural Codes

2021 ◽  
Author(s):  
M. E. Rule ◽  
T. O’Leary
Keyword(s):  
Self Healing ◽  
Error Feedback ◽  
Population Response ◽  
Neural Representations ◽  
Neural Populations ◽  
Population Codes ◽  
The Brain ◽  
Homeostatic Mechanisms ◽  
Continuous Representations

AbstractNeural representations change, even in the absence of overt learning. To preserve stable behavior and memories, the brain must track these changes. Here, we explore homeostatic mechanisms that could allow neural populations to track drift in continuous representations without external error feedback. We build on existing models of Hebbian homeostasis, which have been shown to stabilize representations against synaptic turnover and allow discrete neuronal assemblies to track representational drift. We show that a downstream readout can use its own activity to detect and correct drift, and that such a self-healing code could be implemented by plausible synaptic rules. Population response normalization and recurrent dynamics could stabilize codes further. Our model reproduces aspects of drift observed in experiments, and posits neurally plausible mechanisms for long-term stable readouts from drifting population codes.

scholarly journals The interaction between neural populations: Additive versus diffusive coupling

2021 ◽  
Author(s):  
Marinho Antunes Lopes ◽  
Khalid Hamandi ◽  
Jiaxiang Zhang ◽  
Jen Creaser
Keyword(s):  
Brain Network ◽  
Coupling Function ◽  
Neural Population ◽  
Escape Time ◽  
Future Studies ◽  
Scale Free ◽  
Diffusive Coupling ◽  
Neural Populations ◽  
Global And Local ◽  
The Brain

Models of networks of populations of neurons commonly assume that the interactions between neural populations are via additive or diffusive coupling. When using the additive coupling, a population's activity is affected by the sum of the activities of neighbouring populations. In contrast, when using the diffusive coupling a neural population is affected by the sum of the differences between its activity and the activity of its neighbours. These two coupling functions have been used interchangeably for similar applications. Here, we show that the choice of coupling can lead to strikingly different brain network dynamics. We focus on a model of seizure transitions that has been used both with additive and diffusive coupling in the literature. We consider networks with two and three nodes, and large random and scale-free networks with 64 nodes. We further assess functional networks inferred from magnetoencephalography (MEG) from people with epilepsy and healthy controls. To characterize the seizure dynamics on these networks, we use the escape time, the brain network ictogenicity (BNI) and the node ictogenicity (NI), which are measures of the network's global and local ability to generate seizures. Our main result is that the level of ictogenicity of a network is strongly dependent on the coupling function. We find that people with epilepsy have higher additive BNI than controls, as hypothesized, while the diffusive BNI provides the opposite result. Moreover, individual nodes that are more likely to drive seizures with one type of coupling are more likely to prevent seizures with the other coupling function. Our results on the MEG networks and evidence from the literature suggest that the additive coupling may be a better modelling choice than the diffusive coupling, at least for BNI and NI studies. Thus, we highlight the need to motivate and validate the choice of coupling in future studies.

scholarly journals Networked information interactions of epileptic EEG based on symbolic transfer entropy

2019 ◽  
Author(s):  
Wenpo Yao ◽  
Jun Wang
Keyword(s):  
Healthy Subjects ◽  
Information Transfer ◽  
Probability Distributions ◽  
Brain Area ◽  
Brain Regions ◽  
Transfer Entropy ◽  
Symbolic Transfer Entropy ◽  
Electrical Activities ◽  
The Brain

AbstractIdentifying networked information exchanges among brain regions is important for understanding the brain structure. We employ symbolic transfer entropy to facilitate the construction of networked information interactions for EEGs of 22 epileptics and 22 healthy subjects. The epileptic patients during seizure-free interval have lower information transfer in each individual and whole brain regions than the healthy subjects. Among all of the brain regions, the information flows out of and into the brain area of O1 of the epileptic EEGs are significantly lower than those of the healthy (p<0.0005), and the information flow from F7 to F8 (p<0.00001) is particularly promising to discriminate the two groups of EEGs. Moreover, Shannon entropy of probability distributions of information exchanges suggests that the healthy EEGs have higher complexity and irregularity than the epileptic brain electrical activities. By characterizing the brain networked information interactions, our findings highlight the long-term reduced information exchanges, degree of brain interactivities and informational complexity of the epileptic EEG.

scholarly journals Scaling of sensory information in large neural populations shows signatures of information-limiting correlations

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
MohammadMehdi Kafashan ◽  
Anna W. Jaffe ◽  
Selmaan N. Chettih ◽  
Ramon Nogueira ◽  
Iñigo Arandia-Romero ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Stimulus ◽  
Brain Area ◽  
Sensory Information ◽  
Correlated Noise ◽  
Neuronal Populations ◽  
Total Information ◽  
Neural Populations ◽  
The Brain

AbstractHow is information distributed across large neuronal populations within a given brain area? Information may be distributed roughly evenly across neuronal populations, so that total information scales linearly with the number of recorded neurons. Alternatively, the neural code might be highly redundant, meaning that total information saturates. Here we investigate how sensory information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex. We show that information scales sublinearly due to correlated noise in these populations. We compartmentalized noise correlations into information-limiting and nonlimiting components, then extrapolate to predict how information grows with even larger neural populations. We predict that tens of thousands of neurons encode 95% of the information about visual stimulus direction, much less than the number of neurons in primary visual cortex. These findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most sensory information from smaller subpopulations.

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