The Perirhinal Cortex Engages in Area and Layer-Specific Encoding of Item Dimensions

The perirhinal cortex (PRC), subdivided into areas 35 and 36, belongs to the parahippocampal regions that provide polysensory input to the hippocampus. Efferent and afferent connections along its rostro-caudal axis, and of areas 35 and 36, are extremely diverse. Correspondingly functional tasks in which the PRC participates are manifold. The PRC engages, for example, in sensory information processing, object recognition, and attentional processes. It was previously reported that layer II of the caudal area 35 may be critically involved in the encoding of large-scale objects. In the present study we aimed to disambiguate the roles of the different PRC layers, along with areas 35 and 36, and the rostro-caudal compartments of the PRC, in processing information about objects of different dimensions. Here, we compared effects on information encoding triggered by learning about subtle and discretely visible (microscale) object information and overt, highly visible landmark (macroscale) information. To this end, nuclear expression of the immediate early gene Arc was evaluated using fluorescence in situ hybridization. Increased nuclear Arc expression occurred in layers III and V-VI of the middle and caudal parts of area 35 in response to both novel microscale and macroscale object exposure. By contrast, a significant increase in Arc expression occurred in area 36 only in response to microscale objects. These results indicate that area 36 is specifically involved in the encoding of small and less prominently visible items. In contrast, area 35 engages globally (layer III to VI) in the encoding of object information independent of item dimensions.

Plasmonic Nanobar-on-Mirror Antenna with Giant Local Chirality: a New Platform for Ultrafast Chiral Single-Photon Emission

Providing an additional degree of freedom for binary information encoding and nonreciprocal information transmission, chiral single photons have become a new research frontier in quantum optics. Without using complex external...

Quantum Entanglement in Time

<p>This thesis is in the field of quantum information science, which is an area that reconceptualizes quantum physics in terms of information. Central to this area is the quantum effect of entanglement in space. It is an interdependence among two or more spatially separated quantum systems that would be impossible to replicate by classical systems. Alternatively, an entanglement in space can also be viewed as a resource in quantum information in that it allows the ability to perform information tasks that would be impossible or very difficult to do with only classical information. Two such astonishing applications are quantum communications which can be harnessed for teleportation, and quantum computers which can drastically outperform the best classical supercomputers. In this thesis our focus is on the theoretical aspect of the field, and we provide one of the first expositions on an analogous quantum effect known as entanglement in time. It can be viewed as an interdependence of quantum systems across time, which is stronger than could ever exist between classical systems. We explore this temporal effect within the study of quantum information and its foundations as well as through relativistic quantum information. An original contribution of this thesis is the design of one of the first quantum information applications of entanglement in time, namely a quantum blockchain. We describe how the entanglement in time provides the quantum advantage over a classical blockchain. Furthermore, the information encoding procedure of this quantum blockchain can be interpreted as non-classically influencing the past, and hence the system can be viewed as a `quantum time machine.'</p>

Quantum Entanglement in Time

<p>This thesis is in the field of quantum information science, which is an area that reconceptualizes quantum physics in terms of information. Central to this area is the quantum effect of entanglement in space. It is an interdependence among two or more spatially separated quantum systems that would be impossible to replicate by classical systems. Alternatively, an entanglement in space can also be viewed as a resource in quantum information in that it allows the ability to perform information tasks that would be impossible or very difficult to do with only classical information. Two such astonishing applications are quantum communications which can be harnessed for teleportation, and quantum computers which can drastically outperform the best classical supercomputers. In this thesis our focus is on the theoretical aspect of the field, and we provide one of the first expositions on an analogous quantum effect known as entanglement in time. It can be viewed as an interdependence of quantum systems across time, which is stronger than could ever exist between classical systems. We explore this temporal effect within the study of quantum information and its foundations as well as through relativistic quantum information. An original contribution of this thesis is the design of one of the first quantum information applications of entanglement in time, namely a quantum blockchain. We describe how the entanglement in time provides the quantum advantage over a classical blockchain. Furthermore, the information encoding procedure of this quantum blockchain can be interpreted as non-classically influencing the past, and hence the system can be viewed as a `quantum time machine.'</p>

Development of algorithm simulating spatial fold change of cell signaling for pattern formation in zebrafish

Embryos develop robust spatiotemporal patterns by encoding and interpreting biological signals in real time. Developmental patterns often scale with body or tissue size even when total cell number, cell size or growth rate are changed. A striking example of patterning is the segmentation of somites — the precursors of vertebral column. Despite decade-long efforts, how positional information for segmentation is encoded by cell signaling remained elusive. To address this fundamental question, we studied a novel zebrafish tail explant model that recapitulated the scaling of somite sizes with the length of unsegmented tissue in growing intact embryos. This paper provides an algorithm written in MATLAB as well as Python and finally finding a way to write an efficient algorithm to be able to answer the question described above. Information encoding by spatial fold-change of cell signaling is a remarkable strategy that could be utilized for engineering precisely patterned tissues or organs. We also discuss the limitations of simulations performed using MATLAB with performance decreasing with the large data sets. So, we tried to analyze the factors that impacted the performance of the algorithm. Finally, we tried to answer questions regarding the language selection in which a simulation method can be written efficiently.

Divisive normalization unifies disparate response signatures throughout the human visual hierarchy

Neural processing is hypothesized to apply the same mathematical operations in a variety of contexts, implementing so-called canonical neural computations. Divisive normalization (DN) is considered a prime candidate for a canonical computation. Here, we propose a population receptive field (pRF) model based on DN and evaluate it using ultra-high-field functional MRI (fMRI). The DN model parsimoniously captures seemingly disparate response signatures with a single computation, superseding existing pRF models in both performance and biological plausibility. We observe systematic variations in specific DN model parameters across the visual hierarchy and show how they relate to differences in response modulation and visuospatial information integration. The DN model delivers a unifying framework for visuospatial responses throughout the human visual hierarchy and provides insights into its underlying information-encoding computations. These findings extend the role of DN as a canonical computation to neuronal populations throughout the human visual hierarchy.

Mid-lateral cerebellar complex spikes encode multiple independent reward-related signals during reinforcement learning

Although the cerebellum has been implicated in simple reward-based learning recently, the role of complex spikes (CS) and simple spikes (SS), their interaction and their relationship to complex reinforcement learning and decision making is still unclear. Here we show that in a context where a non-human primate learned to make novel visuomotor associations, classifying CS responses based on their SS properties revealed distinct cell-type specific encoding of the probability of failure after the stimulus onset and the non-human primate’s decision. In a different context, CS from the same cerebellar area also responded in a cell-type and learning independent manner to the stimulus that signaled the beginning of the trial. Both types of CS signals were independent of changes in any motor kinematics and were unlikely to instruct the concurrent SS activity through an error based mechanism, suggesting the presence of context dependent, flexible, multiple independent channels of neural encoding by CS and SS. This diversity in neural information encoding in the mid-lateral cerebellum, depending on the context and learning state, is well suited to promote exploration and acquisition of wide range of cognitive behaviors that entail flexible stimulus-action-reward relationships but not necessarily motor learning.

TAMI-66. FUNCTIONAL ALTERATIONS IN CORTICAL PROCESSING OF SPEECH IN GLIOMA-INFILTRATED CORTEX

INTRODUCTION Recent developments in the biology of malignant gliomas have demonstrated that glioma cells interact with neurons through both paracrine signaling and electrochemical synapses. Glioma-neuron interactions consequently modulate the excitability of local neuronal circuits, and it is unclear the extent to which glioma-infiltrated cortex can meaningfully participate in neural computations. For example, gliomas may result in a local disorganization of activity that impedes the transient synchronization of neural oscillations. Alternatively, glioma-infiltrated cortex may retain the ability to engage in synchronized activity, in a manner similar to normal-appearing cortex, but exhibit other altered spatiotemporal patterns of activity with subsequent impact on cognitive processing. METHODS Here, we acquired invasive electrophysiologic recordings to sample both normal-appearing and glioma-infiltrated cortex during speech initiation in order to measure language task-related circuit dynamics of IDH-wild-type glioblastoma patients. We then applied an information theoretical framework to directly compare the encoding capacity and decodability of signals arising from these regions. RESULTS We find that glioma-infiltrated cortex engages in synchronous activity during task performance in a manner similar to normal-appearing cortex, but recruits a diffuse spatial network. On a temporal scale, we show that glioma-infiltrated cortex has lower capacity for information encoding when performing nuanced tasks such as speech production of monosyllabic versus polysyllabic words. As a result, temporal decoding strategies for distinguishing monosyllabic from polysyllabic words were feasible for signals arising from normal-appearing cortex, but not from glioma-infiltrated cortex. CONCLUSION These findings inform our understanding of cognitive processing in patients with malignant gliomas and have implications for patient survival, neuromodulation, and prosthetics in patients with malignant gliomas.