A Bioeducational Approach to Virtual Learning Environments

Knowledge carries some general characteristics related to the socio-environmental, cultural, and bio-physiological contexts. These three coordinates help us to understand under which condition knowledge is achieved/gained and they do it. Along the same line, the real or virtual learning contexts being essential and unique, the possibilities offered by the VLE which give the opportunity of programming environmental challenges, complexity, and support for subjects open up a series of educational perspectives that support individual differences even when they reproduce social platforms as virtual worlds. Programming that through adequate representations of environments, situations, problems, and specific actions are able to work on more complex neuronal patterns usually activated in the presence of real objects, especially in light of the current structures present in formal contexts of education.

A dynamical model for the basal ganglia-thalamo-cortical oscillatory activity and its implications in Parkinson’s disease

We propose to investigate brain electrophysiological alterations associated with Parkinson’s disease through a novel adaptive dynamical model of the network of the basal ganglia, the cortex and the thalamus. The model uniquely unifies the influence of dopamine in the regulation of the activity of all basal ganglia nuclei, the self-organised neuronal interdependent activity of basal ganglia-thalamo-cortical circuits and the generation of subcortical background oscillations. Variations in the amount of dopamine produced in the neurons of the substantia nigra pars compacta are key both in the onset of Parkinson’s disease and in the basal ganglia action selection. We model these dopamine-induced relationships, and Parkinsonian states are interpreted as spontaneous emergent behaviours associated with different rhythms of oscillatory activity patterns of the basal ganglia-thalamo-cortical network. These results are significant because: (1) the neural populations are built upon single-neuron models that have been robustly designed to have eletrophysiologically-realistic responses, and (2) our model distinctively links changes in the oscillatory activity in subcortical structures, dopamine levels in the basal ganglia and pathological synchronisation neuronal patterns compatible with Parkinsonian states, this still remains an open problem and is crucial to better understand the progression of the disease.

Rotational dynamics versus sequence-like responses

In a recent review, Vyas et al. commented on our previous observations regarding the presence of response sequences in the activity of cortical neuronal population and the contribution of such sequences to rotational dynamics patterns revealed with jPCA. Vyas et al. suggested that rotations generated from sequence-like responses are different from the ones arising from empirical neuronal patterns, which are highly heterogeneous across motor conditions in terms of response timing and shape. Here we extend our previous findings with new results showing that empirical population data contain plentiful neuronal responses whose shape and timing persist across arm-movement conditions. The more complex, heterogeneous responses can be also found; these response patterns also contain temporal sequences, which are evident from the analysis of cross-condition variance. Combined with simulation results, these observations show that both consistent and heterogeneous responses contribute to rotational patterns revealed with jPCA. We suggest that the users of jPCA should consider these two contributions when interpreting their results. Overall, we do not see any principal contradiction between the neural population dynamics framework and our results pertaining to sequence-like responses. Yet, questions remain regarding the conclusions that can be drawn from the analysis of low-dimensional representations of neuronal population data.

On the methods for reactivation and replay analysis

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 reactivation , and those that quantify the temporal structure of changing activity patterns, termed replay . 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’.

Embryonic Cannabidiol Exposure Does Not Affect Adult Zebrafish Swimming Performance

Cannabis is used for a variety of reasons such as relieving pain, relieving stress, and reducing nausea during chemotherapy. While cannabis originates from central and south Asia, the drug has become extremely popular in North America. In July of 2001, medicinal use of cannabis was legalized in Canada, and on October 17 2018, recreational use of cannabis was legalized nationally. Many scientific studies have shown the negative effects of cannabis in consumers and of second hand smoke exposure, including lung cancer, respiratory issues, and reduced decision making and cognitive function. Because of the rapid increase in cannabis, high concentrations have filtered into the water treatment facilities and spread into lakes and ponds through pipelines that could potentially cause harm to the fish. While there are studies that have concluded that there are alterations to the fish’s neuronal patterns and cardiac systems in zebrafish, there were no reports of how the medical ingredient of cannabis (cannabidiol or CBD) may affect the ability of a fish to swim. Proper swim behaviour is an essential survival characteristic to fish and other marine animals, but when a novel potentially toxic compound is introduced into their environment, impacts to vital biological functions in the organism may occur. This study aimed to investigate the potential effects of cannabidiol on zebrafish by evaluating their critical swimming speed (Ucrit value). Using a swim tunnel, we were able to control the environment and easily identify at what point the fish would be fatigued. Comparisons were made between three different fish tanks: one tank exposed to CBD, and the other two tanks contained a fresh water control and a solvent control. Using both our “p” and “F” stat values, we can conclude that there were no significant differences observed between the three fish tanks. In the future, we hope to analyse the neurology of the fish exposed and complete a fish respirometry measuring the oxygen consumption of CBD exposed fish.

Internally Organized Cell Assembly Trajectories

Sequences of neuronal patterns are not always imposed on brain circuits in an outside-in manner by the sensory inputs. Internally organized processes can sustain self-organized and coordinated neuronal activity even without external inputs. A prerequisite of cognition is the availability of internally generated neuronal sentences. Self-generated, sequentially evolving activity is the default state of affairs in most neuronal circuits. Neuronal activity moves perpetually, and its trajectory depends only on initial conditions. Large recurrent networks can generate an enormous number of trajectories without prior experience. On the other hand, each is available to be matched by experience to “represent” something useful for the downstream reader mechanisms. The richness of the information depends not on the numbers of generated sequences but on the reader mechanisms. It is typically the reader structure that initiates the transfer of information, coordinating the onset of messages from multiple senders.

Persistence of neuronal representations through time and damage in the hippocampus

Memories can persist for decades but how they are stably encoded in individual and groups of neurons is not known. To investigate how a familiar environment is encoded in CA1 neurons over time we implanted bilateral microendoscopes in transgenic mice to image the activity of pyramidal neurons in the hippocampus over weeks. Most of the neurons (90 %) are active every day, however, the response of neurons to specific cues changes across days. Approximately 40 % of place and time cells lose fields between two days; however, on timescales longer than two days the neuronal pattern changes at a rate of 1 % for each additional day. Despite continuous changes, field responses are more resilient, with place/time cells recovering their fields after a 10-day period of no task or following CA1 damage. Recovery of these neuronal patterns is characterized by transient changes in firing fields which ultimately converge to the original representation. Unlike individual neurons, groups of neurons with inter and intrahemispheric synchronous activity form stable place and time fields across days. Neurons whose activity was synchronous with a large group of neurons were more likely to preserve their responses to place or time across multiple days. These results support the view that although task-relevant information stored in individual neurons is relatively labile, it can persist in networks of neurons with synchronized activity spanning both hemispheres.One Sentence SummaryNeuronal representations in networks of neurons with synchronized activity are stable over weeks, even after lack of training or following damage.

Neuronal activity and learning in local cortical networks are modulated by the action-perception state

SummaryDuring sensorimotor learning, neuronal networks change to optimize the associations between action and perception. In this study, we examine how the brain harnesses neuronal patterns that correspond to the current action-perception state during learning. To this end, we recorded activity from motor cortex while monkeys either performed a familiar motor task (movement-state) or learned to control the firing rate of a target neuron using a brain-machine interface (BMI-state). Before learning, monkeys were placed in an observation-state, where no action was required. We found that neuronal patterns during the BMI-state were markedly different from the movement-state patterns. BMI-state patterns were initially similar to those in the observation-state and evolved to produce an increase in the firing rate of the target neuron. The overall activity of the non-target neurons remained similar after learning, suggesting that excitatory-inhibitory balance was maintained. Indeed, a novel neural-level reinforcement-learning network model operating in a chaotic regime of balanced excitation and inhibition predicts our results in detail. We conclude that during BMI learning, the brain can adapt patterns corresponding to the current action-perception state to gain rewards. Moreover, our results show that we can predict activity changes that occur during learning based on the pre-learning activity. This new finding may serve as a key step toward clinical brain-machine interface applications to modify impaired brain activity.

Real-time classification of experience-related ensemble spiking patterns for closed-loop applications

Communication in neural circuits across the cortex is thought to be mediated by spontaneous temporally organized patterns of population activity lasting ~50 –200 ms. Closed-loop manipulations have the unique power to reveal direct and causal links between such patterns and their contribution to cognition. Current brain–computer interfaces, however, are not designed to interpret multi-neuronal spiking patterns at the millisecond timescale. To bridge this gap, we developed a system for classifying ensemble patterns in a closed-loop setting and demonstrated its application in the online identification of hippocampal neuronal replay sequences in the rat. Our system decodes multi-neuronal patterns at 10 ms resolution, identifies within 50 ms experience-related patterns with over 70% sensitivity and specificity, and classifies their content with 95% accuracy. This technology scales to high-count electrode arrays and will help to shed new light on the contribution of internally generated neural activity to coordinated neural assembly interactions and cognition.