Encoding and decoding models

OPINION article Front. Integr. Neurosci., 18 August 2011 Volume 5 - 2011 | https://doi.org/10.3389/fnint.2011.00040

Our ability to perform a variety of difficult tasks everything from reasoning about the best chess move, to shooting a free throw, or finely dicing an onion is due to the coordinated activity of populations of neurons throughout the nervoussystem. And yet, we lack an understanding of how the brain generates the activity appropriate for achieving something as simple as pressing an elevator button. In part, this is because we do not know which neural activity patterns the brain is capable of generating, nor how that activity will change with experience. By exploring the structure and constraints on the activity patterns the brain can express, we move closer to understanding how the brain can generate the activity supportive of such a rich variety of behaviors and adaptations. Presently, in studies of arm or eye movements, we typically don't know the causal relationship between neural activity and behavior. Here we use a brain-computer interface (BCI) paradigm to study learning, because the exact relationship between neural activity and behavior is controlled by the experimenter. To generate proficient behavior, the animal must change the activity of the neurons currently being recorded. This provides us with the means to causally relate any observed structure in neural population activity with animals' performance at the task. The focus of this thesis is to characterize the structure and time course of neural population activity during learning. In the first part of this thesis, we note that just as there is more than one way to win a game of chess, the brain has many differentpatterns of neural activity it can produce to drive the same behavior. Which of these redundant options does the brain prefer? We find that the frequency with which animals used different patterns of neural population activity was remarkably similar before and after learning. This suggests that the brain's ability to take advantage of redundancy may be somewhat limited, at least within the span of a few hours. In the second part of this thesis, we asked how internal states such as our arousal, attention, and motivation interact with how we learn new tasks. We identified large, abrupt fluctuations in neural population activity in motor cortex indicative of arousal-like internal state changes, which we term \neural engagement. We find that stereotyped changes in neural engagement during learning were unrelated to goal-seeking behavior, but nevertheless influenced how quickly different tasks were learned. Overall, this thesis characterizes a variety of different constraints and influences on how populations of neurons change their activity during learning.

Traumatic brain injury (TBI) is a leading cause of disability and death and survivors often suffer from long-lasting motor impairment, cognitive deficits, anxiety disorders and epilepsy. Few experimental studies have investigated long-term sequelae after TBI and relations between behavioral changes and neural activity patterns remain elusive. We examined these issues in a murine model of TBI combining histology, behavioral analyses and single-photon emission computed tomography (SPECT) imaging of regional cerebral blood flow (CBF) as a proxy for neural activity. Adult C57Bl/6N mice were subjected to unilateral cortical impact injury and investigated at early (15-57days after lesion, dal) and late (184-225 dal) post-traumatic time points. TBI caused pronounced tissue loss of the parietal cortex and subcortical structures and enduring neurological deficits. Marked perilesional astro- and microgliosis was found at 57 dal and declined at 225 dal. Motor and gait pattern deficits occurred at early time points after TBI and improved over the time. In contrast, impaired performance in the Morris water maze test and decreased anxiety-like behavior persisted together with an increased susceptibility to pentylenetetrazole-induced seizures suggesting alterations in neural activity patterns. Accordingly, SPECT imaging of CBF indicated asymmetric hemispheric baseline neural activity patterns. In the ipsilateral hemisphere, increased baseline neural activity was found in the amygdala. In the contralateral hemisphere, homotopic to the structural brain damage, the hippocampus and distinct cortex regions displayed increased baseline neural activity. Thus, regionally elevated CBF along with behavioral alterations indicate that increased neural activity is critically involved in the long-lasting consequences of TBI.

Mathematics and science are highly integrated disciplines, but the brain association between mathematics and science remains unclear. The current study used functional magnetic resonance imaging (fMRI) scans of 34 undergraduates (17 males, mean age = 20.3±1.64 years old) while they completed mathematical, physical and chemical principles, arithmetic computation, and sentence comprehension. We examined neural activation level, neural activation pattern, and neural connectivity to investigate the neural associations between mathematics and science (including physics and chemistry). The results showed that mathematical, physical, and chemical principles elicited similar neural activation level and neural activation pattern in the visuospatial network (mainly in the middle frontal gyrus and inferior parietal lobule), which were different from those elicited by sentence comprehension; those three principles also elicited similar neural activation level and neural activation pattern in the semantic network (mainly in the middle temporal gyrus, angular gyrus, inferior frontal gyrus, and dorsomedial prefrontal cortex), in contrast to that elicited by arithmetic computation. Effective connectivity analyses showed stronger connectivity between the middle temporal gyrus and inferior parietal lobule for mathematical, physical, and chemical principles than for sentence comprehension. The results suggest that visuospatial and semantic networks were critical for processing both mathematics and science.

Alterations of mismatch responses (ie, neural activity evoked by unexpected stimuli) are often considered a potential biomarker of schizophrenia. Going beyond establishing the type of observed alterations found in diagnosed patients and related cohorts, computational methods can yield valuable insights into the underlying disruptions of neural mechanisms and cognitive function. Here, we adopt a typology of model-based approaches from computational cognitive neuroscience, providing an overview of the study of mismatch responses and their alterations in schizophrenia from four complementary perspectives: (a) connectivity models, (b) decoding models, (c) neural network models, and (d) cognitive models. Connectivity models aim at inferring the effective connectivity patterns between brain regions that may underlie mismatch responses measured at the sensor level. Decoding models use multivariate spatiotemporal mismatch response patterns to infer the type of sensory violations or to classify participants based on their diagnosis. Neural network models such as deep convolutional neural networks can be used for improved classification performance as well as for a systematic study of various aspects of empirical data. Finally, cognitive models quantify mismatch responses in terms of signaling and updating perceptual predictions over time. In addition to describing the available methodology and reviewing the results of recent computational psychiatry studies, we offer suggestions for future work applying model-based techniques to advance the study of mismatch responses in schizophrenia.

Well‐designed brand logos can be critical in effective marketing strategies. By adopting a consumer neuroscience approach and the interaction of person‐affect‐cognition‐execution model, this study analyzes the behavioral reaction and neural activation pattern during the perception of brand logos in relation to the compulsive buying tendencies of participants. Results suggest that women are more cue‐reactive toward brand logos and show activity changes in brain regions associated with cue‐reactivity in (behavioral) addictions. Conversely, men are less cue‐reactive but show increased activity changes in reward‐related regions. Women with compulsive buying tendencies may be more susceptible to brand logos, which can evoke neural activation similar to addictive patterns, while men are less cue‐reactive but show hidden neural activation associated with rewards. This study enhances understanding of (1) how marketing cues influence pre‐addictive behavior and (2) gender differences in brain activations related to cue‐reactivity in people with compulsive buying tendencies. We inform further research on implicit and neural processes on how brands are perceived by compulsive buyers, particularly among male consumers. We also emphasize the need to protect vulnerable consumer groups, such as compulsive buyers.

Dimension reduction on neural activity paves a way for unsupervised neural decoding by dissociating the measurement of internal neural state repetition from the measurement of external variable tuning. With assumptions only on the smoothness of latent dynamics and of internal tuning curves, the Poisson Gaussian-process latent variable model (P-GPLVM) (Wu et al., 2017) is a powerful tool to discover the low-dimensional latent structure for high-dimensional spike trains. However, when given novel neural data, the original model lacks a method to infer their latent trajectories in the learned latent space, limiting its ability for estimating the internal state repetition. Here, we extend the P-GPLVM to enable the latent variable inference of new data constrained by previously learned smoothness and mapping information. We also describe a principled approach for the constrained latent variable inference for temporally-compressed patterns of activity, such as those found in population burst events (PBEs) during hippocampal sharp-wave ripples, as well as metrics for assessing whether the inferred new latent variables are congruent with a previously learned manifold in the latent space. Applying these approaches to hippocampal ensemble recordings during active maze exploration, we replicate the result that P-GPLVM learns a latent space encoding the animal’s position. We further demonstrate that this latent space can differentiate one maze context from another. By inferring the latent variables of new neural data during running, certain internal neural states are observed to repeat, which is in accordance with the similarity of experiences encoded by its nearby neural trajectories in the training data manifold. Finally, repetition of internal neural states can be estimated for neural activity during PBEs as well, allowing the identification for replay events of versatile behaviors and more general experiences. Thus, our extension of the P-GPLVM framework for unsupervised analysis of neural activity can be used to answer critical questions related to scientific discovery.

Growing awareness of the ethical implications of neuroscience in the early years of the 21st century led to the emergence of the new academic field of “neuroethics,” which studies the ethical implications of developments in the neurosciences. However, despite the acceleration and evolution of neuroscience research on nonhuman animals, the unique ethical issues connected with neuroscience research involving nonhuman animals remain underdiscussed. This is a significant oversight given the central place of animal models in neuroscience. To respond to these concerns, the Center for Neuroscience and Society and the Center for the Interaction of Animals and Society at the University of Pennsylvania hosted a workshop on the “Neuroethics of Animal Research” in Philadelphia, Pennsylvania. At the workshop, expert speakers and attendees discussed ethical issues arising from neuroscience research involving nonhuman animals, including the use of animal models in the study of pain and psychiatric conditions, animal brain-machine interfaces, animal–animal chimeras, cerebral organoids, and the relevance of neuroscience to debates about personhood. This paper highlights important emerging ethical issues based on the discussions at the workshop. This paper includes recommendations for research in the United States from the authors based on the discussions at the workshop, loosely following the format of the 2 Gray Matters reports on neuroethics published by the Presidential Commission for the Study of Bioethical Issues.

Developing thalamic projections cross the various segments of the diencephalon and telencephalon to reach appropriate cortical areas without the influence of external sensory input in an autonomous fashion. This initial map requires no major fiber reorganization between the thalamus and cortex: a simple 90 degree rotation can explain the early topographic relations between thalamic volume and the cortical sheet. However, this initially simple layout may be modified close to the cortical target. It has been proposed that the early interactions between thalamic projections and subplate neurons could play a major role in establishing the ultimate innervation pattern. We review evidence for early functional interactions between thalamic projections and cortex in wild-type rat and in the reeler-like mutant Shaking Rat Kawasaki (SRK). In the reeler mouse or SRK the thalamocortical projections ascend to the cortical surface comprising early born cells before descending to their ultimate target cells equivalent to layer 4. This suggests that the initial thalamocortical trajectory has a limited role in final cortical map formation. The normal periphery related pattern is assumed by the thalamic fibers since they are capable of correcting defects in trajectory closer to their targets. A candidate for effecting the realignment of these projections is the neural activity pattern transmitted to the immature cortex via thalamic projections. There is current in vivo and in vitro evidence for the role of neural activity in area and lamina-specific thalamic targeting. In the Snap25 mouse all neurons lack action potential-mediated neurotransmitter release. In spite of this, the embryonic development of thalamic projections follows a normal pattern until birth. There is rich evidence for changes in cortical map formation if the sensory input is changed or the size of the cortical sheet is altered and followed beyond postnatal life. The challenge is to relate the complex events of cortical remodeling to area specific thalamic fiber invasion and modality-specific sensory activation patterns.

Most of our movement consists of sequences of discrete actions at regular intervals-including speech, walking, playing music, or even chewing. Despite this, few models of the motor system address how the brain determines the interval at which to trigger actions. This paper offers a theoretical analysis of the problem of timing movements. We consider a scenario in which we must align an alternating movement with a regular external (auditory) stimulus. We assume that our brains employ generative world models that include internal clocks of various speeds. These allow us to associate a temporally regular sensory input with an internal clock, and actions with parts of that clock cycle. We treat this as process of inferring which clock best explains sensory input. This offers a way in which temporally discrete choices might emerge from a continuous process. This is not straightforward, particularly if each of those choices unfolds during a time that has a (possibly unknown) duration. We develop a route for translation to neurology, in the context of Parkinson's disease-a disorder that characteristically slows down movements. The effects are often elicited in clinic by alternating movements. We find that it is possible to reproduce behavioural and electrophysiological features associated with parkinsonism by disrupting specific parameters-that determine the priors for inferences made by the brain. We observe three core features of Parkinson's disease: amplitude decrement, festination, and breakdown of repetitive movements. Our simulations provide a mechanistic interpretation of how pathology and therapeutics might influence behaviour and neural activity.

A major task in the history of neurophysiology has been to relate patterns of neural activity to ongoing external stimuli. More recently, this approach has branched out to relating current neural activity patterns to external stimuli or experiences that occurred in the past or future. Here, we aim to review the large body of methodological approaches used towards this goal, and to assess the assumptions each makes with reference to the statistics of neural data that are commonly observed. These methods primarily fall into two categories, those that quantify zero-lag relationships without examining temporal evolution, termed <i>reactivation</i>, and those that quantify the temporal structure of changing activity patterns, termed <i>replay</i>. However, no two studies use the exact same approach, which prevents an unbiased comparison between findings. These observations should instead be validated by multiple and, if possible, previously established tests. This will help the community to speak a common language and will eventually provide tools to study, more generally, the organization of neuronal patterns in the brain.This article is part of the Theo Murphy meeting issue ‘Memory reactivation: replaying events past, present and future’.

In this paper, mathematical models for the auditory system were derived to characterize the impact of anesthesia on auditory systems and mechanism of hearing damage. The auditory system was represented by a model with external sound stimuli as the input and the neuron firing rates as the output, and represented as a black-box input-output system. Two parallel subsystem models were developed: an ARX model for the auditory system under external stimuli and an ARMA model for the spontaneous activities of the neurons in primary auditory cortex. The models provide a quantitative characterization of anesthesia’s impacts and describe the mechanism of hearing loss on auditory transmission channels.KeywordsFiring RateAuditory SystemARMA ModelBroadband NoisePrimary Auditory CortexThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

&lt;p&gt;Patients with congenital analgesia who lack the ability to sense temperature generally face low survival rates, highlighting a critical need to understand the underlying mechanisms of heat sensation. While previous research has focused on modeling neural responses to stimuli, the specific mechanisms by which heat-sensitive neurons respond to external temperature changes remain unclear. This gap in knowledge is particularly relevant, as identifying how these neurons react to diverse stimuli can provide insight into sensory deficits linked to congenital analgesia. In this study, we developed a model of heat-sensitive neurons based on the FitzHugh-Nagumo (FHN) neural circuit to investigate neuronal response patterns to external heat stimuli. Two distinct stimulus patterns, each combined with Gaussian white noise, were applied to the model to induce varied firing modes. By calculating the Hamilton energy for each firing mode, we quantified the impact of each external stimulus on neuronal activity. A correlation function was further defined to explore how different stimuli influence the selection of firing modes. Simulation results demonstrate that heat-sensitive neurons show a preferential response to stimuli that induce spike discharge over stimuli that induce r-clonic patterns, as seen in changes to the periodic attractor contours. When exposed to Chua's circuit stimulus, chaotic emission patterns reveal significant shifts in attractor contour, indicating a strong response to spike, r-clonic, and periodic stimuli. These findings suggest that external stimuli capable of inducing spike-and-wave or r-clonic patterns are sensitively detected by thermosensitive neurons, leading to heightened Hamilton energy release and increased regularity in neural activity. This study enhances our understanding of thermosensitive neuronal dynamics under complex stimuli, shedding light on potential response mechanisms relevant to sensory dysfunction in congenital analgesia and advancing the broader field of neural response modeling.&lt;/p&gt;

Reward function appears to be modulated by the circadian system, but little is known about the neural basis of this interaction. Previous research suggests that the neural reward response may be different in the afternoon; however, the direction of this effect is contentious. Reward response may follow the diurnal rhythm in self-reported positive affect, peaking in the early afternoon. An alternative is that daily reward response represents a type of prediction error, with neural reward activation relatively high at times of day when rewards are unexpected (i.e., early and late in the day). The present study measured neural reward activation in the context of a validated reward task at 10.00 h, 14.00 h, and 19.00 h in healthy human males. A region of interest BOLD fMRI protocol was used to investigate the diurnal waveform of activation in reward-related brain regions. Multilevel modeling found, as expected, a highly significant quadratic time-of-day effect focusing on the left putamen (p < 0.001). Consistent with the "prediction error" hypothesis, activation was significantly higher at 10.00 h and 19.00 h compared with 14.00 h. It is provisionally concluded that the putamen may be particularly important in endogenous priming of reward motivation at different times of day, with the pattern of activation consistent with circadian-modulated reward expectancies in neural pathways (i.e., greater activation to reward stimuli at unexpected times of day). This study encourages further research into circadian modulation of reward and underscores the methodological importance of accounting for time of day in fMRI protocols.SIGNIFICANCE STATEMENT This is one of the first studies to use a repeated-measures imaging procedure to explore the diurnal rhythm of reward activation. Although self-reported reward (most often operationalized as positive affect) peaks in the afternoon, the present findings indicate that neural activation is lowest at this time. We conclude that the diurnal neural activation pattern may reflect a prediction error of the brain, where rewards at unexpected times (10.00 h and 19.00 h) elicit higher activation in reward brain regions than at expected (14.00 h) times. These data also have methodological significance, suggesting that there may be a time of day influence, which should be accounted for in neural reward studies.

This study aimed to assess how a Zentangle intervention influences cognitive focus, emotional well-being, stress levels, and neural activity patterns across brain regions and frequency bands. A cohort of 30 healthy adults, all without prior Zentangle experience, participated in this study. Electroencephalography (EEG) was used to measure their brain activity, and self-reported data were collected through questionnaires to assess subjects' concentration levels, emotional calm, and stress and anxiety. Participants reported enhanced cognitive focus and emotional well-being, evidenced by increased self-reported concentration and emotional calmness, and reduced stress and anxiety levels during the intervention. EEG analyses revealed notable changes in neural activity patterns, including decreased delta power and increased theta, alpha, beta, and gamma bands. Functional connectivity analysis also highlighted alterations in the brain's functional connectivity, suggesting potential effects on neural communication and information processing. This study provides compelling evidence of Zentangle's impact on EEG data, aligning it with equanimity and tranquility consistent with previous mindfulness research. These findings underscore Zentangle as an effective mindfulness practice, potentially enhancing cognitive focus and emotional well-being, and emerging as a valuable intervention for improving mental health and overall well-being.