2008 — 2012 |
Siapas, Athanassios G |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Hippocampal Network Dynamics During Sleep @ California Institute of Technology
DESCRIPTION (provided by applicant): How are long-term memories formed? One prominent theory proposes a two-stage process: memories are encoded in the hippocampus during active behavior and then consolidated across neocortical circuits during sleep. At the level of neuronal assemblies, the main evidence for this conjecture is that the hippocampus replays portions of its experience-specific awake activity during both REM and slow-wave sleep (SWS), even though the electrical and chemical profiles of these stages differ drastically. This raises several key questions: What differential roles, if any, do these two sleep stages play in memory consolidation? How are these roles supported by the neural activity patterns of SWS and REM? And how does plasticity contribute to shaping these patterns? We will employ a combination of large-scale electrophysiology in freely behaving rats, computational modeling, and pharmacological manipulations to test the hypothesis that REM and SWS differentially alter coordinated firing in the hippocampus in an experience-dependent manner. In particular, our preliminary data strongly suggest that REM increases coordinated firing in the hippocampus (Aim 1), while SWS has the opposite effect (Aim 2). Building on the observation that hippocampal cells exhibit place specific firing (place cells), we will use repeated linear track traversals to generate consistent activation of hippocampal patterns. By changing the environment and the number of traversals, we will create multiple experience-specific traces of parametrically varying strengths and measure their evolution over several SWS/REM sleep stages. Changes in coordinated firing play a critical role in controlling the ability of the hippocampus to drive its post-synaptic targets and engage plasticity mechanisms. In Aim 3, we will characterize the role of synaptic plasticity in controlling the level of coordinated firing within the hippocampus by: (1) developing a computational framework for testing whether plasticity rules can account for changes in correlated firing produced by REM and SWS;(2) by repeating the experimental measurements of Aims 1,2 after pharmacological blockade of NMDA receptors;and (3) by using electrical stimulation to probe the mean synaptic weight of CA3 recurrent connections and track its evolution during sleep. PUBLIC HEALTH RELEVANCE The proposed studies integrate experimental and computational approaches to quantify how synchrony alters hippocampal activity patterns during sleep, and how these changes depend on waking experience. Misregulation of sleep activity is observed in many psychiatric disorders, such as schizophrenia and depression. The proposed studies may provide a framework for understanding the origins and consequences of such misregulation. In addition, we investigate the interactions between burst firing and plasticity in recurrent networks. Abnormal interactions between these processes may underlie paroxysmal states in the hippocampus such as epileptic seizures.
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1 |
2010 |
Siapas, Athanassios G |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Electrophysiology and Imaging of Drosophila Olfaction @ California Institute of Technology
DESCRIPTION (provided by applicant): Studies of the molecular biology, structure and function of olfactory systems throughout the animal kingdom reveal many common design features (for example: large set of odorant receptors, precise axonal convergence, oscillatory synchronization of activity) across evolutionary distant animal species such as insects and primates (including humans). Interestingly, many of these design features are implemented differently (for example: different gene sequences, synaptic types and cell morphologies) across these many species: comparing these different implementations and, through this comparison, identifying their common high-level features helps us identify the rules of function of olfactory systems. Because olfactory circuits are tightly associated with memory structures-the memories of smells are particularly vivid and long lasting in humans-understanding olfactory coding also helps us better understand the nature of associative memories and their underlying biology. The present work is geared towards understanding the basic physiology of these olfactory/memory circuits. This work will constitute the electrophysiological foundation on which new cellular and systems studies of olfactory learning can be built in a particularly advantageous model system: experiments will be carried out with the brain of the fruit-fly Drosophifa, taking advantage of a century of genetics and molecular work by hundreds of laboratories on this system: by virtue of their accessibility and knowledge we now have of them, Drosophila genes can be controlled so as to either mark or manipulate specific neurons and circuits. By combining these powerful tools with electrode recordings and fast brain imaging tools, we can examine the role of specific molecules, neurons or synapses for olfactory function. The relatively small size of the Drosophila brain (~100,000 neurons) will also greatly simplify the task of deciphering the nature of neural codes in more complex systems, such as the human brain, thus contributing to a better understanding of the possible causes of perceptual disorders.
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1 |
2011 — 2015 |
Siapas, Athanassios |
DP1Activity Code Description: To support individuals who have the potential to make extraordinary contributions to medical research. The NIH Director’s Pioneer Award is not renewable. |
Nanoprobe Arrays For Massively Parallel 3-D Recordings of Brain Activity @ California Institute of Technology
DESCRIPTION Abstract: Although great strides have been made in characterizing the properties of single neurons, enormous challenges remain before we understand how billions of neurons work in concert to produce complex phenomena such as perception, learning, and memory. Far and away, the biggest obstacle towards progress in systems neuroscience has been the difficulty of observing the activity of large populations of neurons in freely behaving animals. The flow of electricity in the form of action potentials and synaptic currents is the currency of the brain, and neural activity and synaptic changes are sensitive to millisecond timescales. Hence electrophysiology has been the gold standard for monitoring the brain since it directly measures electrical activity with sub-millisecond resolution. However, state of the art multi-electrode arrays have about 100 recording sites and can thus sample neuronal activity only very sparsely. This constraint makes it difficult to infer anything about global brain patterns and their evolution in time. To overcome these limitations, we propose to develop nanoprobe arrays which preserve the exceptional temporal resolution of electrophysiology while drastically increasing its spatial resolution and scale. The proposed arrays will have tens of thousands of recording sites-two orders of magnitude higher than current devices-and will enable mapping brain activity across entire volumes of brain tissue with unprecedented spatiotemporal resolution, exposing fundamental regularities far beyond the reach of current technologies. This development will fuel innovations at many levels: the design and nanofabrication of probes, integration with active electronics, development of high-speed acquisition systems, implantable interfaces for extensive testing in behaving animals, and development of computational and analysis infrastructure. Our goal is to go beyond proof of concept prototypes towards widely available transformative research tools by employing foundr
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1 |
2012 — 2017 |
Siapas, Athanassios |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Prefrontal-Hippocampal Interactions in Associative Learning @ California Institute of Technology
Forming associations between different stimuli is a fundamental building block for learning and memory. The hippocampus and prefrontal cortex are two brain regions that are believed to play a critical role in forming memories of associations. However, the neural mechanisms underlying the ability of these brain structures to form associations are poorly understood. This project will employ multi-electrode recordings to monitor neural activity in the hippocampus and prefrontal cortex during the learning of simple associations. These experiments will characterize the firing of neurons throughout the course of learning in order to determine how these brain circuits contribute to the formation of memories. This research requires an interdisciplinary approach employing techniques from neuroscience, engineering, and mathematics, providing a rich training experience for the next generation of students and researchers. Educational efforts will also involve the development of strong ties to local K-12 schools, active participation in programs providing research opportunities to underrepresented minorities, and talks and events aimed at exposing the public to memory research. This project also involves the development of computational infrastructure that may help advance the scale and efficacy of large-scale brain recordings.
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0.915 |
2014 — 2016 |
Roukes, Michael L [⬀] Shepard, Kenneth L Siapas, Athanassios Tolias, Andreas |
U01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Modular Nanophotonic Probes For Dense Neural Recording At Single-Cell Resolution @ California Institute of Technology
DESCRIPTION (provided by applicant): Our understanding of the properties of individual neurons and their role in brain computations has advanced significantly during the last few decades. However, we are still very far from understanding how large assemblies of cells interact to process information. Electrophysiology is the gold standard with unmatched temporal resolution, but is currently limited in terms of its ability record from every single neuron withina volume with cell-type specificity. Optical imaging provides a powerful alternative method, which enables localization of neurons in anatomical space and cell-type specificity via genetically encoded fluorescent markers. The current state-of-the-art in functional brain imaging is two-photon fluorescence laser-scanning microscopy. But this approach works best only on the surface of the brain, or transparent tissues and is not easily scalable. More generally, light scattering and absorption in tissue impose significant fundamental limits: in mammalian brains, accessible depths in vivo are restricted to superficial cortical regions, d1mm. Endoscopic methods developed to circumvent such restrictions impart significant damage to tissue above the imaging site given the large probe diameter (0.3 to >1 mm) and thus are quite limited (e.g. cannot be used to study cortical columns). Here we propose a novel paradigm for functional optical imaging that surmounts these limitations. It permits function- al imaging with cellular resolution in highly scattering brain tissue, enables complete coverage of all neurons within a target volume, and has long-term prospects for human applications. Our approach, which we term integrated neurophotonics, is based on distributing a dense 3-D lattice of emitter and detector pixels within the brain itself. These pixel arrays are embedded onto neurophotonic probes, realized as implantable, ultra narrow shanks that leverage recent advances in both integrated nanophotonics. Used with functional optical reporters (e.g. GCaMP6), one 25-shank probe module will be capable of recording the activity of all neurons within a 1- mm3 volume (~100,000 neurons) with single cell resolution. The methodology is scalable; multiple modules can be tiled to densely cover extended regions deep within the brain. It will ultimately permit simultaneous recording from millions of neurons at arbitrary positions and depths in the brain, to unveil the dynamics of complete neural networks - with single-cell resolution and cell-type specificity. Ultra-narrow neurophotonic probes will perturb brain tissue minimally, imposing negligible tissue displacement and minute local power dissipation. Importantly, they are readily producible though existing wafer-scale foundry (factory) based methods and thus will be widely available for use by the community. They will transform studies of circuit- level mechanisms of brain computation and neuropsychiatric disorders, and will accelerate drug discovery via high throughput in vivo screening. Our multi-disciplinary team spans all requisite expertise: nanotechnology and large-scale-integration for development of neurophotonic probe arrays (Roukes, Shepard), and in vivo testing and computational analysis (Tolias, Siapas).
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0.915 |
2015 — 2019 |
Roukes, Michael (co-PI) [⬀] Siapas, Athanassios |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Bigdata: Collaborative Research: Ia: Hardware and Software For Spike Detection and Sorting in Massively Parallel Electrophysiological Recording Systems For the Brain @ California Institute of Technology
Understanding how the brain works is arguably one of the most significant scientific challenges of our time and the focus of the BRAIN initiative. It is widely believed that neural circuit function is emergent, the result of complex interactions between constituents with individual neurons forming synaptic connections with thousands of other neurons. Mapping of these complex circuits has been virtually impossible because of the reliance on electrophysiological recordings which sample these networks extremely sparsely. These tools for extracellular spike recordings are only able to simultaneously record from several tens to a few hundred neurons. Raw signals from these recording electrodes are first filtered to remove out-of-band signals. Putative spike events are then detected and extracted. Finally, these snippets of time-series event are sorted, typically on the basis of waveform shapes, into clusters. Even at the very modest bandwidths for these systems, computing systems struggle to save the data and process the resulting data sets. Scalability of these measurement techniques by many orders of magnitude in recording density and channels will be essential to future progress in understanding neuron circuits.
This project is exploiting emerging electrophysiological recording systems in which the electrode (and channel) count is increased by almost three orders of magnitude over conventional systems with data bandwidths exceeding 1GB/sec. To handle these data bandwidths and resulting data volumes and deliver scalability, this project will develop dedicated hardware and associated algorithms for spike detection and sorting that allow these tasks to be performed in real-time in close proximity to the recording system. Compression by more than three orders of magnitude is possible by these means by taking advantage of the special spatiotemporal local structure in these data sets; by exploiting strong prior information about the spiking signal and reducing the dimensionality of the problem accordingly; and by adapting and extending modern scalable nonparametric Bayesian inference methods. In addition to providing important new tools for neuroscience, the tools developed here for scalable real-time event detection and annotation have broad applicability to other spatiotemporal data sets (or more generally, any data set comprising multiple streams of data, in which the streams could involve different data modalities) in which objects of interest are spatially and temporally localized with fixed spatial footprints. Examples abound in cell and molecular biology, particle and solid-state physics, financial monitoring, monitoring of power networks, and sensor networks.
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0.915 |
2017 — 2021 |
Siapas, Athanassios |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Hippocampal Influences On Auditory Cortical Circuits as a Function of Brain State and Learning @ California Institute of Technology
Project Summary The hippocampus plays a critical role in the formation of episodic memories. The current predominant hypothesis is that memories are gradually established across distributed cortical networks under the in?uence of hippocampal activity. However, our understanding of the nature and extent of hippocampal in?uences on cortical circuits remains incomplete. Part of the challenge has been that any subthreshold modulation of cortical neurons is inaccessible to extracellular recordings, which have been the main tool for studying of cortico-hippocampal interactions. We propose to combine whole-cell recordings in auditory cortex (AC) with optogenetic control of the projections to this area from the hippocampal complex (HC), in order to quantify the in?uence of the HC-AC projections on the membrane potential dynamics of individual cortical neurons as a function of brain state and learning. We aim to use this approach to identify cortical neurons that receive direct hippocampal input, and characterize their properties and the plasticity of the corresponding synapses in vivo. Finally, we aim to use optogenetic perturbations of the HC-AC projections during learning events and hippocampal ripples to establish the functional role of these projections in memory formation, and to study the timing of their participation in the consolidation process. Quantifying the subthreshold modulation of cortical neurons by hippocampal inputs can provide sensitive measures of the functional coupling between these areas and bring us closer to a mechanistic understanding of their interactions and their role in memory formation.
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0.915 |
2021 |
Doyle, John C (co-PI) [⬀] Lois, Carlos (co-PI) [⬀] Lubenov, Evgueniy Vassilev Siapas, Athanassios |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Stability and Robustness of Hippocampal Representations of Space @ California Institute of Technology
PROJECT SUMMARY How does the brain balance the need to preserve prior knowledge with the necessity to continuously learn new information? The tradeo? between stability and plasticity is inherent in both biological and arti?cial learning systems constrained by ?nite resources and capacity. The hippocampus is a brain region critical for memory formation and spatial learning, which can provide a powerful experimental system for characterizing this tradeo?. The role of the hippocampus in spatial cognition is supported by the ?nding that pyramidal neurons in this area (place cells) ?re in speci?c locations in an environment (place ?elds). The population of place cells active in an environment is believed to form a neural representation or cognitive map of that environment. Spatial learning is critical for survival and involves two competing constraints: representations of space must be plastic to enable fast learning of new environments and changes in behavioral contingencies, and stable over time to enable recognition of familiar environments, reliable navigation, and leveraging of previous learning. How do these competing constraints a?ect the stability of place ?elds across time? The experimental characterization of the long-term stability of spatial representations in the hippocampus has been challenging as it requires tracking the activity of multiple place cells across extended periods of time (days to weeks). We propose to use novel approaches in large-scale electrophysiology and imaging in behaving rodents to characterize which neurons change their spatial tuning and how these changes depend on behavior. Furthermore, we will use recordings and circuit perturbations to characterize the activity patterns that predict changes in tuning stability. Our analysis will be carried out in the context of a theoretical framework for understanding the interplay between plasticity and stability of hippocampal representations. Characterizing the evolution of neural representations is of fundamental importance in understanding how information is maintained across brain circuits and how such maintenance is perturbed in brain disorders.
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0.915 |