Circuit Mechanisms of Remote Memory

Memories of emotionally-salient events are long-lasting, guiding behavior from minutes to years after learning. The prelimbic cortex (PL) is required for fear memory retrieval across time and is densely interconnected with many subcortical and cortical areas involved in recent and remote memory recall, including the temporal association area (TeA). While the behavioral expression of a memory may remain constant over time, the neural activity mediating memory-guided behavior is dynamic. In PL, different neurons underlie recent and remote memory retrieval and remote memory-encoding neurons have preferential functional connectivity with cortical association areas, including TeA. TeA plays a preferential role in remote compared to recent memory retrieval, yet how TeA circuits drive remote memory retrieval remains poorly understood. Here we used a combination of activity-dependent neuronal tagging, viral circuit mapping and miniscope imaging to investigate the role of the PL-TeA circuit in fear memory retrieval across time in mice. We show that PL memory ensembles recruit PL-TeA neurons across time, and that PL-TeA neurons have enhanced encoding of salient cues and behaviors at remote timepoints. This recruitment depends upon ongoing synaptic activity in the learning-activated PL ensemble. Our results reveal a novel circuit encoding remote memory and provide insight into the principles of memory circuit reorganization across time.

Untitled Seminar

Molecular Characterization of Retinal Cell Types: Insights into Evolutionary Origins and Regional Specializations

Multisensory perception, learning, and memory

Note the later start time!

Co-allocation to overlapping dendritic branches in the retrosplenial cortex integrates memories across time

Events occurring close in time are often linked in memory, providing an episodic timeline and a framework for those memories. Recent studies suggest that memories acquired close in time are encoded by overlapping neuronal ensembles, but whether dendritic plasticity plays a role in linking memories is unknown. Using activity-dependent labeling and manipulation, as well as longitudinal one- and two-photon imaging of RSC somatic and dendritic compartments, we show that memory linking is not only dependent on ensemble overlap in the retrosplenial cortex, but also on branch-specific dendritic allocation mechanisms. These results demonstrate a causal role for dendritic mechanisms in memory integration and reveal a novel set of rules that govern how linked, and independent memories are allocated to dendritic compartments.

The Learning Salon

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

The Open-Source UCLA Miniscope Project

The Miniscope Project -- an open-source collaborative effort—was created to accelerate innovation of miniature microscope technology and to increase global access to this technology. Currently, we are working on advancements ranging from optogenetic stimulation and wire-free operation to simultaneous optical and electrophysiological recording. Using these systems, we have uncovered mechanisms underlying temporal memory linking and investigated causes of cognitive deficits in temporal lobe epilepsy. Through innovation and optimization, this work aims to extend the reach of neuroscience research and create new avenues of scientific inquiry.

Sleep features that change your mind

Cluster Headache: Improving Therapy for the Worst Pain Experienced by Humans

Cluster headache is a brain disorder dominated clinically by dreadful episodes of excruciating pain with a circadian pattern and most often focused in bouts with circannual periodicity. As we have understood its neurobiology new therapies, including those directed at calcitonin gene-related peptide, are helpful improve the lives of sufferers.

Zero-shot visual reasoning with probabilistic analogical mapping

There has been a recent surge of interest in the question of whether and how deep learning algorithms might be capable of abstract reasoning, much of which has centered around datasets based on Raven’s Progressive Matrices (RPM), a visual analogy problem set commonly employed to assess fluid intelligence. This has led to the development of algorithms that are capable of solving RPM-like problems directly from pixel-level inputs. However, these algorithms require extensive direct training on analogy problems, and typically generalize poorly to novel problem types. This is in stark contrast to human reasoners, who are capable of solving RPM and other analogy problems zero-shot — that is, with no direct training on those problems. Indeed, it’s this capacity for zero-shot reasoning about novel problem types, i.e. fluid intelligence, that RPM was originally designed to measure. I will present some results from our recent efforts to model this capacity for zero-shot reasoning, based on an extension of a recently proposed approach to analogical mapping we refer to as Probabilistic Analogical Mapping (PAM). Our RPM model uses deep learning to extract attributed graph representations from pixel-level inputs, and then performs alignment of objects between source and target analogs using gradient descent to optimize a graph-matching objective. This extended version of PAM features a number of new capabilities that underscore the flexibility of the overall approach, including 1) the capacity to discover solutions that emphasize either object similarity or relation similarity, based on the demands of a given problem, 2) the ability to extract a schema representing the overall abstract pattern that characterizes a problem, and 3) the ability to directly infer the answer to a problem, rather than relying on a set of possible answer choices. This work suggests that PAM is a promising framework for modeling human zero-shot reasoning.

Probabilistic Analogical Mapping with Semantic Relation Networks

Hongjing Lu will present a new computational model of Probabilistic Analogical Mapping (PAM, in collaboration with Nick Ichien and Keith Holyoak) that finds systematic correspondences between inputs generated by machine learning. The model adopts a Bayesian framework for probabilistic graph matching, operating on semantic relation networks constructed from distributed representations of individual concepts (word embeddings created by Word2vec) and of relations between concepts (created by our BART model). We have used PAM to simulate a broad range of phenomena involving analogical mapping by both adults and children. Our approach demonstrates that human-like analogical mapping can emerge from comparison mechanisms applied to rich semantic representations of individual concepts and relations. More details can be found https://arxiv.org/ftp/arxiv/papers/2103/2103.16704.pdf

Mechanisms underlying detection and temporal sensitivity of single-photon responses in the mammalian retina

We have long known that rod and cone signals interact within the retina and can even contribute to color vision, but the extent of these influences has remained unclear. New results with more powerful methods of RNA expression profiling, specific cell labeling, and single-cell recording have provided greater clarity and are showing that rod and cone signals can mix at virtually every level of signal processing. These interactions influence the integration of retinal signals and make an important contribution to visual perception.

Towards a Translational Neuroscience of Consciousness

The cognitive neuroscience of conscious perception has seen considerable growth over the past few decades. Confirming an influential hypothesis driven by earlier studies of neuropsychological patients, we have found that the lateral and polar prefrontal cortices play important causal roles in the generation of subjective experiences. However, this basic empirical finding has been hotly contested by researchers with different theoretical commitments, and the differences are at times difficult to resolve. To address the controversies, I suggest one alternative venue may be to look for clinical applications derived from current theories. I outline an example in which we used closed-loop fMRI combined with machine learning to nonconsciously manipulate the physiological responses to threatening stimuli, such as spiders or snakes. A clinical trial involving patients with phobia is currently taking place. I also outline how this theoretical framework may be extended to other diseases. Ultimately, a truly meaningful understanding of the fundamental nature of our mental existence should lead to useful insights for our colleagues on the clinical frontlines. If we use this as a yardstick, whoever loses the esoteric theoretical debates, both science and the patients will always win.

Excitation from inhibition: a new model for the initiation of orienting movements

Interactions between the microbiome and nervous system during early development

The gut microbiota is emerging as an important modulator of brain function and behavior, as several recent discoveries reveal substantial effects of the microbiome on neurophysiology, neuroimmunity and animal behavior. Despite these findings supporting a “microbiome-gut-brain axis”, the molecular and cellular mechanisms that underlie interactions between the gut microbiota and brain remain poorly understood. To uncover these, the Hsiao laboratory is mining the human microbiota for microbial modulators of host neuroactive molecules, investigating the impact of microbiota-immune system interactions on neurodevelopment and examining the microbiome as an interface between gene-environment interactions in neurological diseases. In particular, our research on effects of the maternal microbiome on offspring development in utero are revealing novel interactions between microbiome-dependent metabolites and fetal thalamocortical axonogenesis. Overall, we aim to dissect biological pathways for communication between the gut microbiota and nervous system, toward understanding fundamental interactions between physiological systems that impact brain and behavior.

Abstract Semantic Relations in Mind, Brain, and Machines

Abstract semantic relations (e.g., category membership, part-whole, antonymy, cause-effect) are central to human intelligence, underlying the distinctively human ability to reason by analogy. I will describe a computational project (Bayesian Analogy with Relational Transformations) that aims to extract explicit representations of abstract semantic relations from non-relational inputs automatically generated by machine learning. BART’s representations predict patterns of typicality and similarity for semantic relations, as well as similarity of neural signals triggered by semantic relations during analogical reasoning. In this approach, analogy emerges from the ability to learn and compare relations; mapping emerges later from the ability to compare patterns of relations.

Interneuron desynchronization and breakdown of long-term place cell stability in temporal lobe epilepsy

Temporal lobe epilepsy is associated with memory deficits but the circuit mechanisms underlying these cognitive disabilities are not understood. We used electrophysiological recordings, open-source wire-free miniaturized microscopy and computational modeling to probe these deficits in a model of temporal lobe epilepsy. We find desynchronization of dentate gyrus interneurons with CA1 interneurons during theta oscillations and a loss of precision and stability of place fields. We also find that emergence of place cell dysfunction is delayed, providing a potential temporal window for treatments. Computation modeling shows that desynchronization rather than interneuron cell loss can drive place cell dysfunction. Future studies will uncover cell types driving these changes and transcriptional changes that may be driving dysfunction.

Predicting Patterns of Similarity Among Abstract Semantic Relations

In this talk, I will present some data showing that people’s similarity judgments among word pairs reflect distinctions between abstract semantic relations like contrast, cause-effect, or part-whole. Further, the extent that individual participants’ similarity judgments discriminate between abstract semantic relations was linearly associated with both fluid and crystallized verbal intelligence, albeit more strongly with fluid intelligence. Finally, I will compare three models according to their ability to predict these similarity judgments. All models take as input vector representations of individual word meanings, but they differ in their representation of relations: one model does not represent relations at all, a second model represents relations implicitly, and a third model represents relations explicitly. Across the three models, the third model served as the best predictor of human similarity judgments suggesting the importance of explicit relation representation to fully account for human semantic cognition.

Circuit dysfunction and sensory processing in Fragile X Syndrome

To uncover the circuit-level alterations that underlie atypical sensory processing associated with autism, we have adopted a symptom-to-circuit approach in theFmr1-/- mouse model of Fragile X syndrome (FXS). Using a go/no-go task and in vivo 2-photon calcium imaging, we find that impaired visual discrimination in Fmr1-/- mice correlates with marked deficits in orientation tuning of principal neurons in primary visual cortex, and a decrease in the activity of parvalbumin (PV) interneurons. Restoring visually evoked activity in PV cells in Fmr1-/- mice with a chemogenetic (DREADD) strategy was sufficient to rescue their behavioural performance. Strikingly, human subjects with FXS exhibit similar impairments in visual discrimination as Fmr1-/- mice. These results suggest that manipulating inhibition may help sensory processing in FXS. More recently, we find that the ability of Fmr1-/- mice to perform the visual discrimination task is also drastically impaired in the presence of visual or auditory distractors, suggesting that sensory hypersensitivity may affect perceptual learning in autism.