In Vivo Patterned Photo-Stimulation and Imaging in Independent Axial Planes Albeanu, Florin Poster 1, Session 2B Understanding the function of neural circuits requires monitoring large populations of neurons, while simultaneously perturbing specific circuit elements. Patterned illumination techniques allow the generation of flexible spatial and temporal photo-stimulation profiles, which together with multiphoton imaging and optogenetic manipulations provide an ideal framework towards achieving this goal. We describe here a platform combining one photon patterned photo- stimulation via a digital micro-mirror device (DMD) (5) with two photon imaging. Since neural circuits are often arranged in three dimensions, we developed a simple method that allows decoupling of the imaging and the photo stimulation planes. Briefly, the pulsed infrared laser beam for scanning two-photon imaging and a blue laser (488 nm) for photo-stimulation are coupled through the same objective. The blue light intensity is modulated by the DMD chip to form arbitrary spatial-temporal patterns on the brain surface. By introducing a movable holographic diffuser in a plane conjugated with the desired photo- stimulation plane in the sample, we decouple the photo-stimulation and the imaging planes, up to an axial shift of 500 μm. Additionally, this allows axial confinement of the photo-stimulation pattern. To target large populations of neurons, we set the photo-stimulation field to ~1.5 x 1.2 mm2 with a lateral resolution of ~20 μm. Two fast shutters in front of the blue laser and the PMT are used in anti-phase to rapidly alternate between photo-stimulation and imaging (10 Hz). When this technique is applied to the olfactory bulb (OB), we are able to optogenetically photo- stimulate specific neural populations within individual glomeruli while simultaneously monitoring GCaMP3 & GCamP6f signals from populations of bulb interneurons, or output neurons (mitral and tufted cells). We use the two-photon imaging to obtain anatomical and functional information to target particular structures of interest. Within the OB, our strategy offers exciting possibilities for understanding broadcasting of signals by single glomeruli and their combinations, as well as information integration rules at the level of individual neurons during olfactory behaviors. The information included in this abstract is intended for discussion only, and should not be quoted or used without express permission from the project author(s). To request a 508 compliant version, please email [email protected]. Dynamic Network Computations for Foraging in an Uncertain Environment Angelaki, Dora; Dragoi, Valentin; Pitkow, Xaq; Schrater, Paul Poster 2; Session 1B In this project we will characterize distributed neural computation in freely moving Rhesus macaques while they navigate and perform a complex task in a custom-built foraging room (Figure 1). We aim to record electrophysiologically from many hundreds of neurons simultaneously from multiple brain areas (Figure 2), both deep (EC, HPC, PHG, RS) and surface (V4, 7A, PFC). Our recordings will use wireless transmission and data-logging to untether the animals and permit their free exploration of a specially designed environment. By untethering the animals we expect to see more realistic behaviors, and to expand the richness and dimensionality of the neural responses. This is important not only to improve ethological relevance, but to understand computation as well: by including multiple nuisance variables that occur during natural behavior, we will challenge the brain to untangle the representations of task-relevant variables in ways that simpler, highly controlled tasks do not. Simpler tasks allow the brain to decode with mere template-matching; as linear processing does not need multiple processing steps, linear tasks cannot expose the function of multi-level processing. We will use this richness to create and fit population response models that can identify the distributed nonlinear processing that dynamically couples neural activity patterns between brain areas and thereby generates behavior. In the task we have designed, the monkey will try to find food pellets produced by several boxes along the perimeter of the room. The boxes have small computer screens that indicate ‘ripeness’: whether the reward is available soon (Figure 3). These indicators will not be perfectly reliable, however, so the animal must combine memories about the recent past rewards with the visible cues to properly assess ripeness (the ‘what’ task). We will record from area V4, PFC, and RS to identify neural representations of the sensory information and, possibly, attentional signals suggested by recorded eye and body movements. If the animal is sufficiently uncertain, s/he may move to a location we call the ‘oracle’ that slowly reveals the true ripeness of all targets. We will control the cost of switching boxes by dimming the lights while the animal is traveling, and at the same time we will evaluate the quality and computational mechanism of path integration in the dark (the ‘where’ task) by recording from navigation-related brain areas 7A, MEC, HPC. We will use control theory to fit the animal’s behavior and thereby impute latent variables like beliefs, relative values, and navigational state (Figure 4). Along with observables like sensory evidence and reward, these latents will function as additional targets of regression for measuring neural representations and interactions. Figure: 1. Overhead view of foraging room. 2. Brain regions from which we will record neural signals, and hypothesized context-dependent interactions between them. 3. Illustration of an animal’s expectation and uncertainty about reward for each box. 4. Environment and mental model used to infer latent variables, whose neural representations and interactions we will infer. The information included in this abstract is intended for discussion only, and should not be quoted or used without express permission from the project author(s). To request a 508 compliant version, please email [email protected]. A Digital Brain Bank to Support Collaborative Studies of Brain Microstructure at the Meso-Scale Level: The Example of Patient H.M. Annese, Jacopo Poster 3; Session 2A By preserving the brain of his aphasic patient Leborgne, the French neurologist Paul Broca inaugurated the most effective instrument of human neuroscience before Magnetic Resonance Imaging (MRI); that is: the postmortem documentation of neurological damage in patients with discrete behavioral deficits. Following this illustrious tradition, but applying modern digital technology, The Brain Observatory is currently engaged in the development of a permanent digital archive for images and data produced from donated human brains. The archive is meant to represent the phenotypical spectrum of normal brain maturation, aging, and neurological disease. The resource is designed to support remote collaboration, future comparisons, as well as retrospective studies, as exemplified by the NSF-supported neuroanatomical database created for amnesic patient H.M. an atlas composed of stereotaxically indexed data from MRI, blockface images and large-scale digital-pathology. The atlases are linked to MRI-based morphometrics, quantitative neuropathology and scores derived from neuropsychological tests. Some donors also consent to the recording of biographical audio- and video-interviews, adding a humanistic and personalize dimension to the resource. Web technologies (such as Google Maps APIs) maximize the navigation and interoperability of 2-D and 3-D data at multiple levels of resolution. Figure 1. A. 3-Dimensional (3-D) reconstruction of the brain of patient H.M. created from 2,401 digital anatomical images (40x40x70 µm voxels). The model allowed for the accurate delineation of the 1953 surgical lesion (red box for the left hemisphere) as well as the discovery of a substantial portion of hippocampal tissue in both medial temporal lobes (MTL; green box shown on left hemisphere; Annese et al., 2014). Anatomical images correspond to tissue slices that are stained and digitized at 0.5 µm/pixels allowing for examination at cellular resolution. B. Detail of deep white matter (WM) lesion (blue box in panel A) revealed with myelin staining (Gallyas, 1979). The information included in this abstract is intended for discussion only, and should not be quoted or used without express permission from the project author(s). To request a 508 compliant version, please email [email protected]. Sparse Genetically-Encoded Voltage Indicators Antic, Srdjan D; Knopfel, Thomas Poster 4; Session 2B Electrical (voltage) signal is the primary substrate of information processing in the brain. Recording and decoding voltage changes from large number of neurons in living animals remains a yet to be perfected key experimental approach in neurosciences. Standard glass and metal electrodes are hugely invasive and their use suffers from poor spatial resolution, limited coverage, and blindness to cellular identity. The popular calcium-sensitive indicators generate signals contaminated with changes in intracellular calcium that are unrelated to neuronal electrical signals or indirectly report the electrical signals with distorted timing and highly distorted waveform. A conceptually ideal principle to achieve monitoring of neuronal electrical activity is provided by optical voltage imaging using genetically-encoded voltage indicators