Uncovering spatial representations from spatiotemporal patterns of rodent hippocampal field potentials

Differences and similarities between human hippocampal low-frequency oscillations during navigation and mental simulation

Low frequency oscillations in the hippocampus emerge during by both spatial navigation and episodic memory function in humans. We have recently shown that in humans, memory-related processing is a stronger driver of low frequency oscillations than navigation. These findings and others support the idea that low-frequency oscillations are more strongly associated with a general memory function than with a specific role in spatial navigation. However, whether the low-frequency oscillations that support episodic memory and those during navigation could still share some similar functional roles remains unclear. In this study, patients undergoing intracranial electroencephalography (iEEG) monitoring performed a navigation task in which they navigated and performed internally directed route replay, similar to episodic memory. We trained a random forest classification model to use patterns in low-frequency power (2-12 Hz) to learn the position during navigation and subsequently used the same model to successfully decode position during mental simulation. We show that removal of background differences in power between navigation and mental simulation is critical to detecting the overlapping patterns. These results suggest that the low-frequency oscillations that emerge during navigation are more associated with a role in memory than specifically with a navigation related function.

Interictal spikes during spatial working memory carry helpful or distracting representations of space and have opposing impacts on performance

In temporal lobe epilepsy, interictal spikes (IS) - hypersynchronous bursts of network activity - occur at high rates in between seizures. We sought to understand the influence of IS on working memory by recording hippocampal local field potentials from epileptic mice while they performed a delayed alternation task. We found that IS disrupted performance when they were spatially non-restricted and occurred during running. In contrast, when IS were clustered at reward locations, animals performed well. A machine learning decoding approach revealed that IS at reward sites were larger than IS elsewhere on the maze, and could be classified as occurring at specific reward locations - suggesting they carry informative content for the memory task. Finally, a spiking model revealed that spatially clustered IS preserved hippocampal replay, while spatially dispersed IS disrupted replay by causing over-generalization. Together, these results show that IS can have opposing outcomes on memory.

Identifying behavioral links to neural dynamics of multifiber photometry recordings in a mouse social behavior network

Objective.Distributed hypothalamic-midbrain neural circuits help orchestrate complex behavioral responses during social interactions. Given rapid advances in optical imaging, it is a fundamental question how population-averaged neural activity measured by multi-fiber photometry (MFP) for calcium fluorescence signals correlates with social behaviors is a fundamental question. This paper aims to investigate the correspondence between MFP data and social behaviors.Approach:We propose a state-space analysis framework to characterize mouse MFP data based on dynamic latent variable models, which include a continuous-state linear dynamical system and a discrete-state hidden semi-Markov model. We validate these models on extensive MFP recordings during aggressive and mating behaviors in male-male and male-female interactions, respectively.Main results:Our results show that these models are capable of capturing both temporal behavioral structure and associated neural states, and produce interpretable latent states. Our approach is also validated in computer simulations in the presence of known ground truth.Significance:Overall, these analysis approaches provide a state-space framework to examine neural dynamics underlying social behaviors and reveals mechanistic insights into the relevant networks.

Identifying behavioral links to neural dynamics of multifiber photometry recordings in a mouse social behavior network

Distributed hypothalamic-midbrain neural circuits orchestrate complex behavioral responses during social interactions. How population-averaged neural activity measured by multi-fiber photometry (MFP) for calcium fluorescence signals correlates with social behaviors is a fundamental question. We propose a state-space analysis framework to characterize mouse MFP data based on dynamic latent variable models, which include continuous-state linear dynamical system (LDS) and discrete-state hidden semi-Markov model (HSMM). We validate these models on extensive MFP recordings during aggressive and mating behaviors in male-male and male-female interactions, respectively. Our results show that these models are capable of capturing both temporal behavioral structure and associated neural states. Overall, these analysis approaches provide an unbiased strategy to examine neural dynamics underlying social behaviors and reveals mechanistic insights into the relevant networks.

How our understanding of memory replay evolves

Memory reactivations and replay, widely reported in the hippocampus and cortex across species, have been implicated in memory consolidation, planning, and spatial and skill learning. Technological advances in electrophysiology, calcium imaging, and human neuroimaging techniques have enabled neuroscientists to measure large-scale neural activity with increasing spatiotemporal resolution and have provided opportunities for developing robust analytic methods to identify memory replay. In this article, we first review a large body of historically important and representative memory replay studies from the animal and human literature. We then discuss our current understanding of memory replay functions in learning, planning, and memory consolidation and further discuss the progress in computational modeling that has contributed to these improvements. Next, we review past and present analytic methods for replay analyses and discuss their limitations and challenges. Finally, looking ahead, we discuss some promising analytic methods for detecting nonstereotypical, behaviorally nondecodable structures from large-scale neural recordings. We argue that seamless integration of multisite recordings, real-time replay decoding, and closed-loop manipulation experiments will be essential for delineating the role of memory replay in a wide range of cognitive and motor functions.

Excitatory-inhibitory recurrent dynamics produce robust visual grids and stable attractors

Spatially modulated grid cells have been recently found in the rat secondary visual cortex (V2) during active navigation. However, the computational mechanism and functional significance of V2 grid cells remain unknown. To address the knowledge gap, we train a biologically inspired excitatory-inhibitory recurrent neural network to perform a two-dimensional spatial navigation task with multisensory input. We find grid-like responses in both excitatory and inhibitory RNN units, which are robust with respect to spatial cues, dimensionality of visual input, and activation function. Population responses reveal a low-dimensional, torus-like manifold and attractor. We find a link between functional grid clusters with similar receptive fields and structured excitatory-to-excitatory connections. Additionally, multistable torus-like attractors emerged with increasing sparsity in inter- and intra-subnetwork connectivity. Finally, irregular grid patterns are found in recurrent neural network (RNN) units during a visual sequence recognition task. Together, our results suggest common computational mechanisms of V2 grid cells for spatial and non-spatial tasks.

Large-scale Multimodal Recordings on a High-density Neurochip: Olfactory Bulb and Hippocampal Networks

A striking example of the brain's complexity and continued plasticity is the addition of new neuronal components to a circuit in a process called neurogenesis. Two brain regions exhibit profound circuit remodeling through this process - the olfactory bulb and hippocampus. However, how local network changes in both regions influence global circuit rewiring and dynamic network features remain largely unexplored due to the lack of spatiotemporal resolution technology and large-scale electrophysiological activity recordings. Here, we demonstrate large-scale recordings using a high-density neurochip to reveal multimodal circuit-wide electrophysiological properties and layer-specific functional connectivity in the olfactory bulb and hippocampal networks. Our findings illustrate simultaneous recordings from the entire network, which allows us to quantify synchronous electrophysiological parameter differences and layer-specific waveform markers. Examining pairwise cross-covariance between active electrode pairs reveals individual neuronal ensemble contributions to synchronous activation between layers and hub microcircuits, demonstrating network-wide rewiring. Our study suggests a novel tool to address the computational implications of large-scale activity patterns in functional multimodal neurogenic circuits.

Closed-loop stimulation using a multiregion brain-machine interface has analgesic effects in rodents

Effective treatments for chronic pain remain limited. Conceptually, a closed-loop neural interface combining sensory signal detection with therapeutic delivery could produce timely and effective pain relief. Such systems are challenging to develop because of difficulties in accurate pain detection and ultrafast analgesic delivery. Pain has sensory and affective components, encoded in large part by neural activities in the primary somatosensory cortex (S1) and anterior cingulate cortex (ACC), respectively. Meanwhile, studies show that stimulation of the prefrontal cortex (PFC) produces descending pain control. Here, we designed and tested a brain-machine interface (BMI) combining an automated pain detection arm, based on simultaneously recorded local field potential (LFP) signals from the S1 and ACC, with a treatment arm, based on optogenetic activation or electrical deep brain stimulation (DBS) of the PFC in freely behaving rats. Our multiregion neural interface accurately detected and treated acute evoked pain and chronic pain. This neural interface is activated rapidly, and its efficacy remained stable over time. Given the clinical feasibility of LFP recordings and DBS, our findings suggest that BMI is a promising approach for pain treatment.