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 Stimulus-specific neural encoding of a persistent, internal defensive state in the hypothalamus

2019 ◽  
Author(s):  
Ann Kennedy ◽  
Prabhat S. Kunwar ◽  
Lingyun Li ◽  
Daniel Wagenaar ◽  
David J. Anderson
Keyword(s):  
Neural Activity ◽  
Internal State ◽  
Stress Hormones ◽  
Ventromedial Hypothalamus ◽  
Persistent Activity ◽  
Neural Encoding ◽  
Population Activity ◽  
Defensive Responses ◽  
Neuroendocrine Mechanisms ◽  
Threatening Stimuli

SummaryPersistent neural activity has been described in cortical, hippocampal, and motor networks as mediating short-term working memory of transiently encountered stimuli1–4. Internal emotion states such as fear also exhibit persistence following exposure to an inciting stimulus5,6, but such persistence is typically attributed to circulating stress hormones7–9; whether persistent neural activity also plays a role has not been established. SF1+/Nr5a1+ neurons in the dorsomedial and central subdivision of the ventromedial hypothalamus (VMHdm/c) are necessary for innate and learned defensive responses to predators10–13. Optogenetic activation of VMHdmSF1 neurons elicits defensive behaviors that can outlast stimulation11,14, suggesting it induces a persistent internal state of fear or anxiety. Here we show that VMHdmSF1 neurons exhibit persistent activity lasting tens of seconds, in response to naturalistic threatening stimuli. This persistent activity was correlated with, and required for, persistent thigmotaxic (anxiety-like) behavior in an open-field assay. Microendoscopic imaging of VMHdmSF1 neurons revealed that persistence reflects dynamic temporal changes in population activity, rather than simply synchronous, slow decay of simultaneously activated neurons. Unexpectedly, distinct but overlapping VMHdmSF1 subpopulations were persistently activated by different classes of threatening stimuli. Computational modeling suggested that recurrent neural networks (RNNs) incorporating slow excitation and a modest degree of neurochemical or spatial bias can account for persistent activity that maintains stimulus identity, without invoking genetically determined “labeled lines”15. Our results provide causal evidence that persistent neural activity, in addition to well-established neuroendocrine mechanisms, can contribute to the ability of emotion states to outlast their inciting stimuli, and suggest a mechanism that could prevent over-generalization of defensive responses without the need to evolve hardwired circuits specific for each type of threat.

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.

scholarly journals Multiple forms of working memory emerge from synapse-astrocyte interactions

2021 ◽  
Author(s):  
Maurizio De Pitta ◽  
Nicolas Brunel
Keyword(s):  
Working Memory ◽  
Neural Activity ◽  
Transmitter Release ◽  
Neural Population ◽  
Time Varying ◽  
Persistent Activity ◽  
Single Neurons ◽  
Multiple Forms ◽  
Neuronal Interactions ◽  
Internal States

Competing accounts propose that working memory (WM) is subserved either by persistent activity in single neurons, or by time-varying activity across a neural population, or by activity-silent mechanisms carried out by hidden internal states of the neural population. While WM is traditionally regarded to originate exclusively from neuronal interactions, cortical networks also include astrocytes that can modulate neural activity. We propose that different mechanisms of WM can be brought forth by astrocyte-mediated modulations of synaptic transmitter release. In this account, the emergence of different mechanisms depends on the network's spontaneous activity and the geometry of the connections between synapses and astrocytes.

scholarly journals Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex

2020 ◽  
Author(s):  
Benjamin R. Cowley ◽  
Adam C. Snyder ◽  
Katerina Acar ◽  
Ryan C. Williamson ◽  
Byron M. Yu ◽  
...  
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Neural Activity ◽  
Internal State ◽  
Sensory Stimulus ◽  
Perceptual Decision Making ◽  
Population Activity ◽  
Perceptual Decision ◽  
Slow Drift ◽  
Sensory Evidence

AbstractAn animal’s decision depends not only on incoming sensory evidence but also on its fluctuating internal state. This internal state is a product of cognitive factors, such as fatigue, motivation, and arousal, but it is unclear how these factors influence the neural processes that encode the sensory stimulus and form a decision. We discovered that, over the timescale of tens of minutes during a perceptual decision-making task, animals slowly shifted their likelihood of reporting stimulus changes. They did this unprompted by task conditions. We recorded neural population activity from visual area V4 as well as prefrontal cortex, and found that the activity of both areas slowly drifted together with the behavioral fluctuations. We reasoned that such slow fluctuations in behavior could either be due to slow changes in how the sensory stimulus is processed or due to a process that acts independently of sensory processing. By analyzing the recorded activity in conjunction with models of perceptual decision-making, we found evidence for the slow drift in neural activity acting as an impulsivity signal, overriding sensory evidence to dictate the final decision. Overall, this work uncovers an internal state embedded in the population activity across multiple brain areas, hidden from typical trial-averaged analyses and revealed only when considering the passage of time within each experimental session. Knowledge of this cognitive factor was critical in elucidating how sensory signals and the internal state together contribute to the decision-making process.

scholarly journals A Biologically Plausible Mechanism to Learn Clusters of Neural Activity

2018 ◽  
Author(s):  
Adrianna R. Loback ◽  
Michael J. Berry
Keyword(s):  
Ganglion Cell ◽  
Real Time ◽  
Neural Activity ◽  
Activity Pattern ◽  
Neural Population ◽  
Retinal Ganglion ◽  
Population Activity ◽  
Visual Areas ◽  
Cortical Hierarchy ◽  
Plausible Mechanism

When correlations within a neural population are strong enough, neural activity in early visual areas is organized into a discrete set of clusters. Here, we show that a simple, biologically plausible circuit can learn and then readout in real-time the identity of experimentally measured clusters of retinal ganglion cell population activity. After learning, individual readout neurons develop cluster tuning, meaning that they respond strongly to any neural activity pattern in one cluster and weakly to all other inputs. Different readout neurons specialize for different clusters, and all input clusters can be learned, as long as the number of readout units is mildly larger than the number of input clusters. We argue that this operation can be repeated as signals flow up the cortical hierarchy.

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 Correlations enhance the behavioral readout of neural population activity in association cortex

2020 ◽  
Author(s):  
Martina Valente ◽  
Giuseppe Pica ◽  
Caroline A. Runyan ◽  
Ari S. Morcos ◽  
Christopher D. Harvey ◽  
...  
Keyword(s):  
Neural Activity ◽  
Posterior Parietal Cortex ◽  
Sensory Information ◽  
Neural Population ◽  
Association Cortex ◽  
Perceptual Discrimination ◽  
Imaging Data ◽  
Stimulus Information ◽  
Population Activity ◽  
Behavioral Choice

The spatiotemporal structure of activity in populations of neurons is critical for accurate perception and behavior. Experimental and theoretical studies have focused on “noise” correlations – trial-to-trial covariations in neural activity for a given stimulus – as a key feature of population activity structure. Much work has shown that these correlations limit the stimulus information encoded by a population of neurons, leading to the widely-held prediction that correlations are detrimental for perceptual discrimination behaviors. However, this prediction relies on an untested assumption: that the neural mechanisms that read out sensory information to inform behavior depend only on a population’s total stimulus information independently of how correlations constrain this information across neurons or time. Here we make the critical advance of simultaneously studying how correlations affect both the encoding and the readout of sensory information. We analyzed calcium imaging data from mouse posterior parietal cortex during two perceptual discrimination tasks. Correlations limited the ability to encode stimulus information, but (seemingly paradoxically) correlations were higher when mice made correct choices than when they made errors. On a single-trial basis, a mouse’s behavioral choice depended not only on the stimulus information in the activity of the population as a whole, but unexpectedly also on the consistency of information across neurons and time. Because correlations increased information consistency, sensory information was more efficiently converted into a behavioral choice in the presence of correlations. Given this enhanced-by-consistency readout, we estimated that correlations produced a behavioral benefit that compensated or overcame their detrimental information-limiting effects. These results call for a re-evaluation of the role of correlated neural activity, and suggest that correlations in association cortex can benefit task performance even if they decrease sensory information.

scholarly journals An oligarchy of NO-producing interneurons controls basal and evoked blood flow in the cortex

2019 ◽  
Author(s):  
Christina T. Echagarruga ◽  
Kyle Gheres ◽  
Patrick J. Drew
Keyword(s):  
Nitric Oxide ◽  
Blood Flow ◽  
Neural Activity ◽  
Neural Population ◽  
Neuron Activity ◽  
Arterial Diameter ◽  
Cellular Mechanisms ◽  
Population Activity ◽  
Basal Diameter ◽  
Arterial Dilation

AbstractChanges in cortical neural activity are coupled to changes in local arterial diameter and blood flow. However, the neuronal types and the signaling mechanisms that control the basal diameter of cerebral arteries or their evoked dilations are not well understood. Using chronic two-photon microscopy, electrophysiology, chemogenetics, and pharmacology in awake, head-fixed mice, we dissected the cellular mechanisms controlling the basal diameter and evoked dilation in cortical arteries. We found that modulation of overall neural activity up or down caused corresponding increases or decreases in basal arterial diameter. Surprisingly, modulation of pyramidal neuron activity had minimal effects on basal or evoked arterial dilation. Instead, the neurally-mediated component of arterial dilation was largely regulated through nitric oxide released by neuronal nitric oxide synthase (nNOS)-expressing neurons, whose activity was not reflected in electrophysiological measures of population activity. Our results show that cortical hemodynamic signals are not controlled by the average activity of the neural population, but rather the activity of a small ‘oligarchy’ of neurons.

scholarly journals Behavior-dependent directional tuning in the human visual-navigation network

2019 ◽  
Author(s):  
Matthias Nau ◽  
Tobias Navarro Schröder ◽  
Markus Frey ◽  
Christian F. Doeller
Keyword(s):  
Memory Performance ◽  
Visual Navigation ◽  
Human Cognition ◽  
Neural Population ◽  
Population Activity ◽  
Directional Tuning ◽  
Memory Processing ◽  
Depth Analysis ◽  
High Level ◽  
And Behavior

AbstractThe brain derives cognitive maps from sensory experience that guide memory formation and behavior. Despite extensive efforts, it still remains unclear how the underlying population activity relates to active behavior and memory performance. To examine these processes, we here combined 7T-fMRI with a kernel-based encoding model of virtual navigation to map world-centered directional tuning across the human cortex. First, we present an in-depth analysis of directional tuning in visual, retrosplenial, parahippocampal and medial temporal cortices. Second, we show that tuning strength, width and topology of this directional code during memory-guided navigation depend on successful encoding of the environment. Finally, we show that participants’ locomotory state influences this tuning in sensory and mnemonic regions such as the hippocampus. We demonstrate a direct link between neural population tuning and human cognition and show that high-level memory processing interacts with network-wide environmental coding in the service of behavior.

scholarly journals Limitations to Estimating Mutual Information in Large Neural Populations

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 490
Author(s):  
Jan Mölter ◽  
Geoffrey J. Goodhill
Keyword(s):  
Information Theory ◽  
Mutual Information ◽  
Neural Activity ◽  
Neural Population ◽  
Population Activity ◽  
Direct Estimate ◽  
Sensory Stimuli ◽  
Information Theoretic ◽  
Finite Samples ◽  
Neural Populations

Information theory provides a powerful framework to analyse the representation of sensory stimuli in neural population activity. However, estimating the quantities involved such as entropy and mutual information from finite samples is notoriously hard and any direct estimate is known to be heavily biased. This is especially true when considering large neural populations. We study a simple model of sensory processing and show through a combinatorial argument that, with high probability, for large neural populations any finite number of samples of neural activity in response to a set of stimuli is mutually distinct. As a consequence, the mutual information when estimated directly from empirical histograms will be equal to the stimulus entropy. Importantly, this is the case irrespective of the precise relation between stimulus and neural activity and corresponds to a maximal bias. This argument is general and applies to any application of information theory, where the state space is large and one relies on empirical histograms. Overall, this work highlights the need for alternative approaches for an information theoretic analysis when dealing with large neural populations.

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