Neural ensembles in navigation: From single cells to population codes

Neural Sequences Underlying Directed Turning in C. elegans

Complex behaviors like navigation rely on sequenced motor outputs that combine to generate effective movement. The brain-wide organization of the circuits that integrate sensory signals to select and execute appropriate motor sequences is not well understood. Here, we characterize the architecture of neural circuits that control C. elegans olfactory navigation. We identify error-correcting turns during navigation and use whole-brain calcium imaging and cell-specific perturbations to determine their neural underpinnings. These turns occur as motor sequences accompanied by neural sequences, in which defined neurons activate in a stereotyped order during each turn. Distinct neurons in this sequence respond to sensory cues, anticipate upcoming turn directions, and drive movement, linking key features of this sensorimotor behavior across time. The neuromodulator tyramine coordinates these sequential brain dynamics. Our results illustrate how neuromodulation can act on a defined neural architecture to generate sequential patterns of activity that link sensory cues to motor actions.

Entorhinal cortex represents task-relevant remote locations independent of CA1

Neurons can collectively represent the current sensory experience while an animal is exploring its environment or remote experiences while the animal is immobile. These remote representations can reflect learned associations1-3 and be required for learning4. Neurons in the medial entorhinal cortex (MEC) reflect the animal's current location during movement5, but little is known about what MEC neurons collectively represent during immobility. Here, we recorded thousands of neurons in superficial MEC and dorsal CA1 as mice learned to associate two pairs of rewarded locations. We found that during immobility, the MEC neural population frequently represented positions far from the animal's location, which we defined as 'non-local coding'. Cells with spatial firing fields at remote locations drove non-local coding, even as cells representing the current position remained active. While MEC non-local coding has been reported during sharp-wave ripples in downstream CA16, we observed non-local coding more often outside of ripples. In fact, CA1 activity was less coordinated with MEC during non-local coding. We further observed that non-local coding was pertinent to the task, as MEC preferentially represented remote task-relevant locations at appropriate times, while rarely representing task-irrelevant locations. Together, this work raises the possibility that MEC non-local coding could strengthen associations between locations independently from CA1.

Neuronal ensembles: Building blocks of neural circuits

Neuronal ensembles, defined as groups of neurons displaying recurring patterns of coordinated activity, represent an intermediate functional level between individual neurons and brain areas. Novel methods to measure and optically manipulate the activity of neuronal populations have provided evidence of ensembles in the neocortex and hippocampus. Ensembles can be activated intrinsically or in response to sensory stimuli and play a causal role in perception and behavior. Here we review ensemble phenomenology, developmental origin, biophysical and synaptic mechanisms, and potential functional roles across different brain areas and species, including humans. As modular units of neural circuits, ensembles could provide a mechanistic underpinning of fundamental brain processes, including neural coding, motor planning, decision-making, learning, and adaptability.

Sequential activity of CA1 hippocampal cells constitutes a temporal memory map for associative learning in mice

Sequential neural dynamics encoded by time cells play a crucial role in hippocampal function. However, the role of hippocampal sequential neural dynamics in associative learning is an open question. We used two-photon Ca2+ imaging of dorsal CA1 (dCA1) neurons in the stratum pyramidale (SP) in head-fixed mice performing a go-no go associative learning task to investigate how odor valence is temporally encoded in this area of the brain. We found that SP cells responded differentially to the rewarded or unrewarded odor. The stimuli were decoded accurately from the activity of the neuronal ensemble, and accuracy increased substantially as the animal learned to differentiate the stimuli. Decoding the stimulus from individual SP cells responding differentially revealed that decision-making took place at discrete times after stimulus presentation. Lick prediction decoded from the ensemble activity of cells in dCA1 correlated linearly with lick behavior. Our findings indicate that sequential activity of SP cells in dCA1 constitutes a temporal memory map used for decision-making in associative learning. VIDEO ABSTRACT.

Distinct manifold encoding of navigational information in the subiculum and hippocampus

The subiculum (SUB) plays a crucial role in spatial navigation and encodes navigational information differently from the hippocampal CA1 area. However, the representation of subicular population activity remains unknown. Here, we investigated the neuronal population activity recorded extracellularly from the CA1 and SUB of rats performing T-maze and open-field tasks. The trajectory of population activity in both areas was confined to low-dimensional neural manifolds homoeomorphic to external space. The manifolds conveyed position, speed, and future path information with higher decoding accuracy in the SUB than in the CA1. The manifolds exhibited common geometry across rats and regions for the CA1 and SUB and between tasks in the SUB. During post-task ripples in slow-wave sleep, population activity represented reward locations/events more frequently in the SUB than in CA1. Thus, the CA1 and SUB encode information distinctly into the neural manifolds that underlie navigational information processing during wakefulness and sleep.

Age- and sex-dependent alterations in primary somatosensory cortex neuronal calcium network dynamics during locomotion

Over the past 30 years, the calcium (Ca2+ ) hypothesis of brain aging has provided clear evidence that hippocampal neuronal Ca2+ dysregulation is a key biomarker of aging. Age-dependent Ca2+ -mediated changes in intrinsic excitability, synaptic plasticity, and activity have helped identify some of the mechanisms engaged in memory and cognitive decline based on work done mostly at the single-cell level and in the slice preparation. Recently, our lab identified age- and Ca2+ -related neuronal network dysregulation in the cortex of the anesthetized animal. Still, investigations in the awake animal are needed to test the generalizability of the Ca2+ hypothesis of brain aging. Here, we used in vigilo two-photon imaging in ambulating mice, to image GCaMP8f in the primary somatosensory cortex (S1), during ambulation and at rest. We investigated aging- and sex-related changes in neuronal networks in the C56BL/6J mouse. Following imaging, gait behavior was characterized to test for changes in locomotor stability. During ambulation, in both young adult and aged mice, an increase in network connectivity and synchronicity was noted. An age-dependent increase in synchronicity was seen in ambulating aged males only. Additionally, females displayed increases in the number of active neurons, Ca2+ transients, and neuronal activity compared to males, particularly during ambulation. These results suggest S1 Ca2+ dynamics and network synchronicity are likely contributors of locomotor stability. We believe this work raises awareness of age- and sex-dependent alterations in S1 neuronal networks, perhaps underlying the increase in falls with age.

Optogenetic cleavage of the Miro GTPase reveals the direct consequences of real-time loss of function in Drosophila

Miro GTPases control mitochondrial morphology, calcium homeostasis, and regulate mitochondrial distribution by mediating their attachment to the kinesin and dynein motor complex. It is not clear, however, how Miro proteins spatially and temporally integrate their function as acute disruption of protein function has not been performed. To address this issue, we have developed an optogenetic loss of function "Split-Miro" allele for precise control of Miro-dependent mitochondrial functions in Drosophila. Rapid optogenetic cleavage of Split-Miro leads to a striking rearrangement of the mitochondrial network, which is mediated by mitochondrial interaction with the microtubules. Unexpectedly, this treatment did not impact the ability of mitochondria to buffer calcium or their association with the endoplasmic reticulum. While Split-Miro overexpression is sufficient to augment mitochondrial motility, sustained photocleavage shows that Split-Miro is surprisingly dispensable to maintain elevated mitochondrial processivity. In adult fly neurons in vivo, Split-Miro photocleavage affects both mitochondrial trafficking and neuronal activity. Furthermore, functional replacement of endogenous Miro with Split-Miro identifies its essential role in the regulation of locomotor activity in adult flies, demonstrating the feasibility of tuning animal behaviour by real-time loss of protein function.

Neuroimaging of Fear Extinction

Extinguishing fear and defensive responses to environmental threats when they are no longer warranted is a critical learning ability that can promote healthy self-regulation and, ultimately, reduce susceptibility to or maintenance of affective-, trauma-, stressor-,and anxiety-related disorders. Neuroimaging tools provide an important means to uncover the neural mechanisms of effective extinction learning that, in turn, can abate the return of fear. Here I review the promises and pitfalls of functional neuroimaging as a method to investigate fear extinction circuitry in the healthy human brain. I discuss the extent to which neuroimaging has validated the core circuits implicated in rodent models and has expanded the scope of the brain regions implicated in extinction processes. Finally, I present new advances made possible by multivariate data analysis tools that yield more refined insights into the brain-behavior relationships involved.