Biology subjects

Frank, L. M.

Publications and source records attributed to Frank, L. M..

33 records · Page 2Linked to original sources

bioRxiv2020

Total recall: episodic memory retrieval,choice, and memory confidence in the rat

Episodic memory enables recollection of past experiences to guide future behavior. Humans know which memories to trust (high confidence) and which to doubt (low confidence). How memory retrieval, memory confidence, and memory-guided decisions are related, however, is not understood. Additionally, whether animals can assess confidence in episodic memories to guide behavior is unknown. We developed a spatial episodic memory task in which rats were incentivized to gamble their time: betting more following a correct choice yielded greater reward. Rat behavior reflected memory confidence, with higher temporal bets following correct choices. We applied modern machine learning to identify a memory decision variable, and built a generative model of memories evolving over time that accurately predicted both choices and confidence reports. Our results reveal in rats an ability thought to exist exclusively in primates, and introduce a unified model of memory dynamics, retrieval, choice, and confidence.

neuroscience↗

bioRxiv2020

Hippocampal replay of experience at real-world speeds

Representations related to past experiences play a critical role in memory and decision-making processes. The rat hippocampus expresses these types of representations during sharp-wave ripple (SWR) events, and previous work identified a minority of SWRs that contain "replay" of spatial trajectories at ~20x the movement speed of the animal. Efforts to understand replay typically make multiple assumptions about which events to examine and what sorts of representations constitute replay. We therefore lack a clear understanding of both the prevalence and the range of representational dynamics associated with replay. Here we develop a state space model that uses a combination of movement dynamics of different speeds to capture the spatial content and time evolution of replay during SWRs. Using this model, we find that the large majority of replay events contain spatially coherent, interpretable content. Furthermore, many events progress at real-world, rather than accelerated, movement speeds, consistent with actual experiences.

neuroscience↗

bioRxiv2020

A Variable Clock Underlies Internally Generated Hippocampal Sequences

Humans have the ability to store and retrieve memories with various degrees of specificity, and recent advances in reinforcement learning have identified benefits to learning when past experience is represented at different levels of temporal abstraction. How this flexibility might be implemented in the brain remains unclear. We analyzed the temporal organization of rat hippocampal population spiking to identify potential substrates for temporally flexible representations. We examined activity both during locomotion and during memory-associated population events known as sharp wave-ripples (SWRs). We found that spiking during SWRs is rhythmically organized with higher event-to-event variability than spiking during locomotion-associated population events. Decoding analyses using clusterless methods further indidate that similar spatial experience can be replayed in multiple SWRs, each time with a different rhythmic structure whose periodicity is sampled from a lognormal distribution. This variability increases with experience despite the decline in SWR rates that occurs as environments become more familiar. We hypothesize that the variability in temporal organization of hippocampal spiking provides a mechanism for storing experiences with various degrees of specificity.

neuroscience↗

bioRxiv2020

Scalable method for micro-CT analysis enables large scale quantitative characterization of brain lesions and implants

Anatomic evaluation is an important aspect of many studies in neuroscience; however, it often lacks information about the three-dimensional structure of the brain. Micro-CT imaging provides an excellent, nondestructive, method for the evaluation of brain structure, but current applications to neurophysiological or lesions studies require removal of the skull and hazardous chemicals, dehydration or embedding, limiting their scalability and utility. Here we present a protocol using eosin in combination with bone decalcification to enhance contrast in the tissue and then employ monochromatic and propagation phase-contrast micro-CT imaging to enable the imaging of brain structure with the preservation of the surrounding skull. Instead of relying on descriptive, time-consuming, or subjective methods, we develop simple quantitative analyses to map the locations of recording electrodes and to characterize the presence and extent of brain lesions.

neuroscience↗

bioRxiv2019

Dynamic preferences account for inter-animal variability during the continual learning of a cognitive task

Individual animals perform tasks in different ways, yet the nature and origin of that variability is poorly understood. In the context of spatial memory tasks, variability is often interpreted as resulting from differences in memory ability, but the validity of this interpretation is seldom tested since we lack a systematic approach for identifying and understanding factors that make one animals behavior different than another. Here we identify such factors in the context of spatial alternation in rats, a task often described as relying solely on memory of past choices. We combine hypothesis-driven behavioral design and reinforcement learning modeling to identify spatial preferences that, when combined with memory, support learning of a spatial alternation task. Identifying these preferences allows us to capture differences among animals, including differences in overall learning ability. Our results show that to understand the complexity of behavior requires quantitative accounts of the preferences of each animal.

neuroscience↗

bioRxiv2019

A microfabricated, 3D-sharpened silicon shuttle for insertion of flexible electrode arrays through dura mater into brain

Electrode arrays for chronic implantation in the brain are a critical technology in both neuroscience and medicine. Recently, flexible, thin-film polymer electrode arrays have shown promise in facilitating stable, single-unit recordings spanning months in rats. While array flexibility enhances integration with neural tissue, it also requires removal of the dura mater, the tough membrane surrounding the brain, and temporary bracing to penetrate the brain parenchyma. Durotomy increases brain swelling, vascular damage, and surgical time. Insertion using a bracing shuttle results in additional vascular damage and brain compression, which increase with device diameter; while a higher-diameter shuttle will have a higher critical load and more likely penetrate dura, it will damage more brain parenchyma and vasculature. One way to penetrate the intact dura and limit tissue compression without increasing shuttle diameter is to reduce the force required for insertion by sharpening the shuttle tip. We describe a novel design and fabrication process to create silicon insertion shuttles that are sharp in three dimensions and can penetrate rat dura, for faster, easier, and less damaging implantation of polymer arrays. Sharpened profiles are obtained by reflowing patterned photoresist, then transferring its sloped profile to silicon with dry etches. We demonstrate that sharpened shuttles can reliably implant polymer probes through dura to yield high quality single unit and local field potential recordings for at least 95 days. On insertion directly through dura, tissue compression is minimal. This is the first demonstration of a rat dural-penetrating array for chronic recording. This device obviates the need for a durotomy, reducing surgical time and risk of damage to the blood-brain barrier. This is an improvement to state-of-the-art flexible polymer electrode arrays that facilitates their implantation, particularly in multi-site recording experiments. This sharpening process can also be integrated into silicon electrode array fabrication.

neuroscience↗

bioRxiv2019

Dorsal and ventral hippocampus engage opposing networks in the nucleus accumbens

Memories of positive experiences require the brain to link places, events, and reward outcomes. Neural processing underlying the association of spatial experiences with reward is thought to depend on interactions between the hippocampus and the nucleus accumbens (NAc)1-9. Hippocampal projections to the NAc arise from both the ventral hippocampus (vH) and the dorsal hippocampus (dH)6-12, and studies using optogenetic interventions have demonstrated that either vH5, 6 or dH7 input to the NAc can support behaviors dependent on spatial-reward associations. It remains unclear, however, whether dH, vH, or both coordinate memory processing of spatial-reward information in the hippocampal-NAc circuit under normal conditions. Times of memory reactivation within and outside the hippocampus are marked by hippocampal sharp-wave ripples (SWRs)13-19, discrete events which facilitate investigation of inter-regional information processing. It is unknown whether dH and vH SWRs act in concert or separately to engage NAc neuronal networks, and whether either dH or vH SWRs are preferentially linked to spatial-reward representations. Here we show that dH and vH SWRs occur asynchronously in the awake state and that NAc spatial-reward representations are selectively activated during dH SWRs. We performed simultaneous extracellular recordings in the dH, vH, and NAc of rats learning and performing an appetitive spatial task and during sleep. We found that individual NAc neurons activated during SWRs from one subdivision of the hippocampus were typically suppressed or unmodulated during SWRs from the other. NAc neurons activated during dH versus vH SWRs showed markedly different task-related firing patterns. Only dH SWR-activated neurons were tuned to similarities across spatial paths and past reward, indicating a specialization for the dH-NAc, but not vH-NAc, network in linking reward to discrete spatial paths. These temporally and anatomically separable hippocampal-NAc interactions suggest that dH and vH coordinate opposing channels of mnemonic processing in the NAc.

neuroscience↗

bioRxiv2019

Robust and replicable measurement for prepulse inhibition of the acoustic startle response

Measuring animal behavior in the context of experimental manipulation is critical for modeling and understanding neuro-psychiatric disease. Prepulse inhibition of the acoustic startle response (PPI) is a behavioral paradigm used extensively for this purpose, but the results of PPI studies are often inconsistent. As a result, the utility of this metric remains uncertain. Here we deconstruct the phenomenon of PPI. We first confirm several limitations of the traditional PPI metric, including that the underlying startle response has a non-Gaussian distribution and that the traditional PPI metric changes with different stimulus condition. We then develop a novel model that reveals PPI to be a combination of the previously appreciated scaling of the startle response, as well as a scaling of sound perception. Using our model, we find no evidence for differences in PPI in a rat model of Fragile-X Syndrome (FXS) compared to wild-type controls. These results in the rat provide a reliable methodology that could be used to clarify inconsistent PPI results in mice and humans. In addition, we find robust differences between wild-type male and female rats. Our model allows us to understand the nature of these differences, and we find that both the startle-scaling and sound-scaling components of PPI are a function of the baseline startle response. Males and females differ specifically in the startle-scaling, but not the sound-scaling, component of PPI. These findings establish a robust experimental and analytical approach that has the potential to provide a consistent biomarker of brain function.

animal behavior and cognition↗

bioRxiv2019

Early Hippocampal Sharp-Wave Ripple Deficits Predict Later Learning and Memory Impairments in an Alzheimer’s Disease Mouse Model

Alzheimers disease (AD) is characterized by progressive memory loss, and there is a pressing need to identify early pathophysiological alterations that predict subsequent memory impairment. Hippocampal sharp-wave ripples (SWRs) - electrophysiological signatures of memory reactivation in the hippocampus - are a compelling candidate for doing so. Mouse models of AD show reductions in both SWR abundance and associated slow gamma (SG) power during aging, but these alterations have yet to be directly linked to memory impairments. In aged apolipoprotein E4 knock in (apoE4-KI) mice - a model of the major genetic risk factor for AD - we found that reduced SWR abundance and associated CA3 SG power predicted spatial memory impairments measured 1-2 months later. Importantly, SWR-associated CA3 SG power reduction in young apoE4-KI mice also predicted spatial memory deficits measured 10 months later. These results establish features of SWRs as potential functional biomarkers of memory impairment in AD.

neuroscience↗

bioRxiv2019

Regular cycling between representations of alternatives in the hippocampus

Cognitive faculties such as imagination, planning, and decision-making require the ability to represent alternative scenarios. In animals, split-second decision-making implies that the brain can represent alternatives at a commensurate speed. Yet despite this insight, it has remained unknown whether there exists neural activity that can consistently represent alternatives in <1 s. Here we report that neural activity in the hippocampus, a brain structure vital to cognition, can regularly cycle between representations of alternative locations (bifurcating paths in a maze) at 8 Hz. This cycling dynamic was paced by the internally generated 8 Hz theta rhythm, often occurred in the absence of overt deliberative behavior, and unexpectedly also governed an additional hippocampal representation defined by alternatives (heading direction). These findings implicate a fast, regular, and generalized neural mechanism underlying the representation of competing possibilities.

neuroscience↗

bioRxiv2019

NWB:N 2.0: An Accessible Data Standard for Neurophysiology

Neurodata Without Borders: Neurophysiology (NWB:N) is a data standard for neurophysiology, providing neuroscientists with a common standard to share, archive, use, and build common analysis tools for neurophysiology data. With NWB:N version 2.0 (NWB:N 2.0) we made significant advances towards creating a usable standard, software ecosystem, and vibrant community for standardizing neurophysiology data. In this manuscript we focus in particular on the NWB:N data standard schema and present advances towards creating an accessible data standard for neurophysiology.

neuroscience↗

bioRxiv2018

Real-Time Point Process Filter for Multidimensional Decoding Problems Using Mixture Models

There is an increasing demand for a computationally efficient and accurate point process filter solution for real-time decoding of population spiking activity in multidimensional spaces. Real-time tools for neural data analysis, specifically real-time neural decoding solutions open doors for developing experiments in a closed-loop setting and more versatile brain-machine interfaces. Over the past decade, the point process filter has been successfully applied in the decoding of behavioral and biological signals using spiking activity of an ensemble of cells; however, the filter solution is computationally expensive in multi-dimensional filtering problems. Here, we propose an approximate filter solution for a general point-process filter problem when the conditional intensity of a cells spiking activity is characterized using a Mixture of Gaussians. We propose the filter solution for a broader class of point process observation called marked point-process, which encompasses both clustered - mainly, called sorted - and clusterless - generally called unsorted or raw- spiking activity. We assume that the posterior distribution on each filtering time-step can be approximated using a Gaussian Mixture Model and propose a computationally efficient algorithm to estimate the optimal number of mixture components and their corresponding weights, mean, and covariance estimates. This algorithm provides a real-time solution for multi-dimensional point-process filter problem and attains accuracy comparable to the exact solution. Our solution takes advantage of mixture dropping and merging algorithms, which collectively control the growth of mixture components on each filtering time-step. We apply this methodology in decoding a rats position in both 1-D and 2-D spaces using clusterless spiking data of an ensemble of rat hippocampus place cells. The approximate solution in 1-D and 2-D decoding is more than 20 and 4,000 times faster than the exact solution, while their accuracy in decoding a rat position only drops by less than 9% and 4% in RMSE and 95% HPD coverage performance metrics. Though the marked-point filter solution is better suited for real-time decoding problems, we discuss how the filter solution can be applied to sorted spike data to better reflect the proposed methodology versatility.

bioinformatics↗

bioRxiv2018

Marked point process filter for clusterless and adaptive encoding-decoding of multiunit activity

Real-time, closed-loop experiments can uncover causal relationships between specific neural activity and behavior. An important advance in realizing this is the marked point process filtering framework which utilizes the \"mark\" or the waveform features of unsorted spikes, to construct a relationship between these features and behavior, which we call the encoding model. This relationship is not fixed, because learning changes coding properties of individual neurons, and electrodes can physically move during the experiment, changing waveform characteristics. We introduce a sequential, Bayesian encoding model which allows incorporation of new information on the fly to adapt the model in real time. A possible application of this framework is to the decoding of the contents of hippocampal ripples in rats exploring a maze. During physical exploration, we observe the marks and positions at which they occur, to update the encoding model, which is employed to decode contents of ripples when rats stop moving, and switch back to updating the model once the rat starts moving again.

neuroscience↗

bioRxiv2018

A polymer probe-based system for high density, long-lasting electrophysiological recordings across distributed neuronal circuits

The brain is a massive neuronal network, organized into anatomically distributed sub-circuits, with functionally relevant activity occurring at timescales ranging from milliseconds to months. Current methods to monitor neural activity, however, lack the necessary conjunction of anatomical spatial coverage, temporal resolution, and long-term stability to measure this distributed activity. Here we introduce a large-scale, multi-site recording platform that integrates polymer electrodes with a modular stacking headstage design supporting up to 1024 recording channels in freely behaving rats. This system can support months-long recordings from hundreds of well-isolated units across multiple brain regions. Moreover, these recordings are stable enough to track 25% of single units for over a week. This platform enables large-scale electrophysiological interrogation of the fast dynamics and long-timescale evolution of anatomically distributed circuits, and thereby provides a new tool for understanding brain activity.

neuroscience↗

bioRxiv2017

Specific hippocampal representations are linked to generalized cortical representations in memory.

Memories link information about specific experiences to more general knowledge that is abstracted from and contextualizes those experiences, but how neuronal activity patterns support this link is not known. Here we show that during memory reactivation in a foraging task with multiple spatial paths, specific hippocampal place representations are concurrently and preferentially reactivated with a subset of prefrontal cortical task representations that generalize across different paths. This link between specific and general representations may serve as a neural substrate for abstraction and task guidance in mammals.

neuroscience↗