Edgar Bermudez-Contreras

SUMMARY: Human primary visual cortex (V1) responds more strongly, or resonates, when exposed to ∼10, ∼15-20, ∼40-50 Hz rhythmic flickering light. Full-field flicker also evokes perception of hallucinatory geometric patterns, which mathematical models explain as standing-wave formations emerging from periodic forcing at resonant frequencies of the simulated neural network. However, empirical evidence for such flicker-induced standing waves in the visual cortex was missing. We recorded cortical responses to flicker in awake mice using high spatial resolution widefield imaging in combination with high temporal resolution glutamate-sensing fluorescent reporter (iGluSnFR). The temporal frequency tuning curves in the mouse V1 were similar to those observed in humans, showing a banded structure with multiple resonance peaks (8 Hz, 15 Hz, 33 Hz). Spatially, all flicker frequencies evoked responses in V1 corresponding to retinotopic stimulus location, but some evoked additional peaks. These flicker-induced cortical patterns displayed standing wave characteristics and matched linear wave equation solutions in an area restricted to the visual cortex. Taken together, the interaction of periodic traveling waves with cortical area boundaries leads to spatiotemporal activity patterns that may affect perception.

In response to sensory stimulation, the cortex exhibits an early transient response followed by late and slower activation. Recent studies suggest that the early component represents features of the stimulus while the late component is associated with stimulus perception. Although very informative, these studies only focus on the amplitude of the evoked responses to study its relationship with sensory perception. In this work, we expand upon the study of how patterns of evoked and spontaneous activity are modified by experience at the mesoscale level using voltage and extracellular glutamate transient recordings over widespread regions of mouse dorsal neocortex. We find that repeated tactile or auditory stimulation selectively modifies the spatiotemporal patterns of cortical activity, mainly of the late evoked response in anesthetized mice injected with amphetamine and also in awake mice. This modification lasted up to 60 min and results in an increase in the amplitude of the late response after repeated stimulation and in an increase in the similarity between the spatiotemporal patterns of the late early evoked response. This similarity increase occurs only for the evoked responses of the sensory modality that received the repeated stimulation. Thus, this selective long-lasting spatiotemporal modification of the cortical activity patterns might provide evidence that evoked responses are a cortex-wide phenomenon. This work opens new questions about how perception-related cortical activity changes with sensory experience across the cortex.

Sleep consists of two basic stages: non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. NREM sleep is characterized by slow high-amplitude cortical EEG signals, while REM sleep is characterized desynchronized cortical rhythms. While, until recently, it has been widely believed that cortical activity during REM sleep is globally desynchronized, recent electrophysiological studies showed slow waves (SW) in some cortical areas during REM sleep. Electrophysiological techniques, however, have been unable to resolve the regional structure of these activities, due to relatively sparse sampling. We mapped functional gradients in cortical activity during REM sleep using mesoscale imaging in mice, and observed local SW patterns occurring mainly in somatomotor and auditory cortical regions, with minimum presence within the default mode network. The role of the cholinergic system in local desynchronization during REM sleep was also explored by calcium imaging of cholinergic terminal activity within the mouse cortex. Terminal activity was weaker in regions exhibiting SW activity more frequently during REM sleep. We also analyzed Allen Mouse Brain Connectivity dataset and found that these regions have weaker cholinergic projections from the basal forebrain.

The automation of monitoring and analysis of mouse behaviour in a homecage can be obtained from continuous video records with machine learning and computer vision. The approach of recreating a mouse’s “real world” behavior and laboratory test behavior in the “small world” of a laboratory cage can provide insights into phenotypical expression of mouse genotypes, development and aging, and neurological disease. Algorithms identify behavioral acts (walk, rear), actions (sleep duration, distance travelled), organized patterns of movement (home base activity and excursions) over extended periods of time. In addition, performance on specific tests can be incorporated within a mouse’s living arrangement. Here we review approaches to engineering a small world and state of the art machine learning analyses for automated study of mouse homecage behavior. We highlight advantages and limitations of these approaches as a supplement to acute behavioral testing methodology.

Despite the recent advancements and popularity of deep learning that has resulted from the advent of numerous industrial applications, artificial neural networks (ANNs) still lack crucial features from their biological counterparts that could improve their performance and their potential to advance our understanding of how the brain works. One avenue that has been proposed to change this is to strengthen the interaction between artificial intelligence (AI) research and neuroscience. Since their historical beginnings, ANNs and AI, in general, have developed in close alignment with both neuroscience and psychology. In addition to deep learning, reinforcement learning (RL) is another approach that is strongly linked to AI and neuroscience to understand how learning is implemented in the brain. In a recently published article, Botvinick et al. (Neuron, 107:603–616, 2020) explain why deep reinforcement learning (DRL) is important for neuroscience as a framework to study learning, representations and decision making. Here, I summarise Botvinick et al.’s main arguments and frame them in the context of the study of learning, memory and spatial navigation. I believe that applying this approach to study spatial navigation can provide useful insights for the understanding of how the brain builds, processes and stores representations of the outside world to extract knowledge.

In response to sensory stimulation, the cortex exhibits an early transient response followed by a late and slower activation pattern. Recent studies suggest that the early component represents features of the stimulus while the late component is associated with stimulus perception. Although very informative, these studies only focus on the amplitude of the evoked responses to study its relationship with sensory perception. In this work we expand upon the study of how patterns of evoked and spontaneous activity are modified by experience at mesoscale level using voltage and extracellular glutamate transient recordings over widespread regions of mice dorsal neocortex. We find that repeated tactile or auditory stimulation selectively modifies the spatiotemporal patterns of cortical activity, mainly of the late evoked response in anesthetized mice injected with amphetamine and also in awake mice. This modification lasted up to 60 minutes and results not only in an increase in amplitude of the late response after repeated stimulation, but also in an increase in the similarity between the spatiotemporal patterns of the late and the early evoked response patterns in anesthetized mice. This similarity increase occurs only for the evoked responses of the sensory modality that received the repeated stimulation. Thus, this selective long-lasting spatiotemporal modification of the cortical activity patterns might provide evidence that evoked responses are a cortex-wide phenomenon. This work opens new questions about how perception-related cortical activity changes with sensory experience across the cortex.

Recent advances in artificial intelligence (AI) and neuroscience are impressive. In AI, this includes the development of computer programs that can beat a grandmaster at GO or outperform human radiologists at cancer detection. A great deal of these technological developments are directly related to progress in artificial neural networks—initially inspired by our knowledge about how the brain carries out computation. In parallel, neuroscience has also experienced significant advances in understanding the brain. For example, in the field of spatial navigation, knowledge about the mechanisms and brain regions involved in neural computations of cognitive maps—an internal representation of space—recently received the Nobel Prize in medicine. Much of the recent progress in neuroscience has partly been due to the development of technology used to record from very large populations of neurons in multiple regions of the brain with exquisite temporal and spatial resolution in behaving animals. With the advent of the vast quantities of data that these techniques allow us to collect there has been an increased interest in the intersection between AI and neuroscience, many of these intersections involve using AI as a novel tool to explore and analyze these large data sets. However, given the common initial motivation point—to understand the brain—these disciplines could be more strongly linked. Currently much of this potential synergy is not being realized. We propose that spatial navigation is an excellent area in which these two disciplines can converge to help advance what we know about the brain. In this review, we first summarize progress in the neuroscience of spatial navigation and reinforcement learning. We then turn our attention to discuss how spatial navigation has been modeled using descriptive, mechanistic, and normative approaches and the use of AI in such models. Next, we discuss how AI can advance neuroscience, how neuroscience can advance AI, and the limitations of these approaches. We finally conclude by highlighting promising lines of research in which spatial navigation can be the point of intersection between neuroscience and AI and how this can contribute to the advancement of the understanding of intelligent behavior.