Integer Echo State Networks: Hyperdimensional Reservoir Computing

We propose an approximation of Echo State Networks (ESN) that can be efficiently implemented on digital hardware based on the mathematics of hyperdimensional computing. The reservoir of the proposed Integer Echo State Network (intESN) is a vector containing only n-bits integers (where n<8 is normally sufficient for a satisfactory performance). The recurrent matrix multiplication is replaced with an efficient cyclic shift operation. The intESN architecture is verified with typical tasks in reservoir computing: memorizing of a sequence of inputs; classifying time-series; learning dynamic processes. Such an architecture results in dramatic improvements in memory footprint and computational efficiency, with minimal performance loss.Comment: 10 pages, 10 figures, 1 tabl

Echoes of Vision: Mental Imagery in the Human Brain

When you picture the face of a friend or imagine your dream house, you are using the same parts of your brain that you use to see. How does the same system manage to both accurately analyze the world around it and synthesize visual experiences without any external input at all? We approach this question and others by extending the well-established theory that the human visual system embodies a probabilistic generative model of the visual world. That is, just as visual features co-occur with one another in the real world with a certain probability (the feature “tree” has a high probability of occurring with the feature “green”), so do the patterns of activity that encode those features in the brain. With such a joint probability distribution at its disposal, the brain can not only infer the cause of a given activity pattern on the retina (vision), but can also generate the probable visual consequence of an assumed or remembered cause (imagery). The formulation of this model predicts that the encoding of imagined stimuli in low-level visual areas resemble the encoding of seen stimuli in higher areas. To test this prediction we developed imagery encoding models-a novel tool that reveals how the features of imagined stimuli are encoded in brain activity. We estimated imagery encoding models from brain activity measured while subjects imagined complex visual stimuli, and then compared these to visual encoding models estimated from a matched viewing experiment. Consistent with our proposal, imagery encoding models revealed changes in spatial frequency tuning and receptive field properties that made early visual areas during imagery more functionally similar to higher visual areas during vision. Likewise, signal and noise properties of the voxel activation between vision and imagery favor the generative model interpretation. Our results provide new evidence for an internal generative model of the visual world, while demonstrating that vision is just one of many possible forms of inference that this putative internal model may support

An overview of parallel distributed processing

Parallel Distributed Processing (PDP), or Connectionism, is a frontier cognitive theory that is currently garnering considerable attention from a variety of fields. Briefly summarized herein are the theoretical foundations of the theory, the key elements observed in creating simulation computer programs, examples of its applications, and some comparisons with other models of cognition. A majority of the information is culled from Rumelhart and McClelland\u27s (1986) two volume introduction to the theory, while some concerns from the field and the theorists\u27 accompanying responses are taken from a 1990 article by Hanson and Burr

Superior longitudinal fasciculus microstructure and its functional triple-network mechanisms in depressive rumination

Depressive rumination, which involves a repetitive focus on one's distress, is associated with function connectivity disturbances of Default-Mode, Salience, and Executive-Control networks, comprising the so-called "triple-network" of attention. Missing, however, is a multimodal account of rumination that neuroanatomically explains the perseveration of these dysfunctional networks as a stable human trait. Using diffusion and functional Magnetic Resonance Imaging, we explored multimodal relationships between rumination severity, white-matter microstructure, and resting-state functional connectivity in N=39 depressed adults, and then directly replicated our findings in a demographically-matched, independent sample (N=39). Among the fully-replicated results, three core findings emerged. First, rumination severity is associated with both disintegrated and desegregated functional connectivity of the triple-network. Second, global microstructural inefficiency of the right Superior Longitudinal Fasciculus (SLF) provides a neuroanatomical connectivity basis for rumination and accounts for anywhere between 25-37% of the variance in rumination (Discovery: p corr<0.01; Replication: p corr<0.01; MSE=0.05). Finally, microstructure of the right SLF and auxiliary white-matter is strongly associated with functional connectivity biomarkers of rumination, both within and between components of the triple-network (Discovery: R²=0.36, p corr<0.05; Replication: R²=0.25, p corr<0.05; MSE=0.04-0.06). By cross-validating discovery with replication, our findings advance a reproducible microstructural-functional brain connectivity model of depressive rumination that unifies neurodevelopmental and neurocognitive perspectives.Psycholog

Predicting speech from a cortical hierarchy of event-based timescales

How do predictions in the brain incorporate the temporal unfolding of context in our natural environment? We here provide evidence for a neural coding scheme that sparsely updates contextual representations at the boundary of events. This yields a hierarchical, multilayered organization of predictive language comprehension. Training artificial neural networks to predict the next word in a story at five stacked time scales and then using model-based functional magnetic resonance imaging, we observe an event-based “surprisal hierarchy” evolving along a temporoparietal pathway. Along this hierarchy, surprisal at any given time scale gated bottom-up and top-down connectivity to neighboring time scales. In contrast, surprisal derived from continuously updated context influenced temporoparietal activity only at short time scales. Representing context in the form of increasingly coarse events constitutes a network architecture for making predictions that is both computationally efficient and contextually diverse

Storing, single photons in broadband vapor cell quantum memories

Single photons are an essential resource for realizing quantum technologies. Together with compatible quantum memories granting control over when a photon arrives, they form a foundational component both of quantum communication and quantum information processing. Quality solid-state single photon sources deliver on the high bandwidths and rates required for scalable quantum technology, but require memories that match these operational parameters. In this thesis, I report on quantum memories based on electromagnetically induced transparency and built in warm rubidium vapor, with such fast and high bandwidth interfaces in mind. I also present work on a heralded single photon source based on parametric downconversion in an optical cavity, operated in a bandwidth regime of a few 100s of megahertz. The systems are characterized on their own and together in a functional interface. As the photon generation process is spontaneous, the memory is implemented as a fully reactive device, capable of storing and retrieving photons in response to an asynchronous external trigger. The combined system is used to demonstrate the storage and retrieval of single photons in and from the quantum memory. Using polarization selection rules in the Zeeman substructure of the atoms, the read-out noise of the memory is considerably reduced from what is common in ground-state storage schemes in warm vapor. Critically, the quantum signature in the photon number statistics of the retrieved photons is successfully maintained, proving that the emission from the memory is dominated by single photons. We observe a retrieved single-photon state accuracy of $g_{c,\,\text{ret}}^{(2)}=0.177(23)$ for short storage times, which remains $g_{c,\,\text{ret}}^{(2)}<0.5$ throughout the memory lifetime of $680(50)\,$ns. The end-to-end efficiency of the memory interfaced with the photon source is $\eta_{e2e}=1.1(2)\,\%$, which will be further improved in the future by optimizing the operating regime. With its operation bandwidth of $370\,$MHz, our system opens up new possibilities for single-photon synchronization and local quantum networking experiments at high repetition rates