A survey of visual preprocessing and shape representation techniques

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)

A Vector-Integration-to-Endpoint Model for Performance of Viapoint Movements

Viapoint (VP) movements are movements to a desired point that are constrained to pass through an intermediate point. Studies have shown that VP movements possess properties, such as smooth curvature around the VP, that are not explicable by treating VP movements as strict concatenations of simpler point-to-point (PTP) movements. Such properties have led some theorists to propose whole-trajectory optimization models, which imply that the entire trajectory is pre-computed before movement initiation. This paper reports new experiments conducted to systematically compare VP with PTP trajectories. Analyses revealed a statistically significant early directional deviation in VP movements but no associated curvature change. An explanation of this effect is offered by extending the Vector-Integration-To-Endpoint (VITE) model (Bullock and Grossberg, 1988), which postulates that voluntary movement trajectories emerge as internal gating signals control the integration of continuously computed vector commands based on the evolving, perceptible difference between desired and actual position variables. The model explains the observed trajectories of VP and PTP movements as emergent properties of a dynamical system that does not precompute entire trajectories before movement initiation. The new model includes a working memory and a stage sensitive to time-to-contact information. These cooperate to control serial performance. The structural and functional relationships proposed in the model are consistent with available data on forebrain physiology and anatomy.Office of Naval Research (N00014-92-J-1309, N00014-93-1-1364, N0014-95-1-0409

A Neural Signal Processor for Low-Latency Spike Inference

This thesis describes the development of a system that can assign identities to a population of single-units, in multi-electrode recordings, at single-spike resolution with low-latency. The system has two parts. The first is a Field-Programmable Gate Array (FPGA)-based Neural Signal Processor (NSP) that receives raw input and generates labelled spikes as output, a process referred to as real-time spike inference. The second is a piece of software (Spiketag) that runs on a PC, communicates with the NSP, and generates a spike-sorted model to guide the real-time spike inference. The NSP provides clocks and control signals to five 32-channel INTAN RHD2132 chips to manage the acquisition of 160 channels of raw neural data. In parallel, the NSP further filters, detects and extracts extracellular spike waveforms from the raw neural data recorded by tetrodes or silicon probes and assigns single-unit identity to each detected spike. A set of Python application programming interfaces (APIs) was developed in Spiketag to enable the communication between the NSP and the PC. These APIs allow the NSP to obtain a model from the PC, which holds parameters such as reference channels, spike detection thresholds, spike feature transformation matrix and vector quantized clusters generated by spike sorting a short recording session. Using the spike-sorted model, the NSP performs data acquisition and real-time spike inference simultaneously. Algorithmic modules were implemented in the FPGA and pipelined to compute during 40 ms acquisition intervals. At the output end of the FPGA NSP, the real-time assigned single-unit identity (spike-id) is packaged with the timestamp, the electrode group, and the spike features as a spike-id packet. Spike-id packets are asynchronously transmitted through a low-latency Peripheral Component Interconnect Express (PCIe) interface to the PC, producing the real-time spike trains. The real-time spike trains can be used for further processing, such as real-time decoding. Several types of ground-truth data, including intracellular/extracellular paired recordings, synthesized tetrode extracellular waveforms with ground-truth spike timing and high-channel-count silicon probe recordings with ground-truth animal positions during navigation were used to validate the low-latency (1 ms) and high-accuracy (as high as state-of-the-art offline sorting and decoding algorithms) of the NSP’s real-time spike inference and the NSP-based real-time population decoding performance

The potential of microelectrode arrays and microelectronics for biomedical research and diagnostics

Planar microelectrode arrays (MEAs) are devices that can be used in biomedical and basic in vitro research to provide extracellular electrophysiological information about biological systems at high spatial and temporal resolution. Complementary metal oxide semiconductor (CMOS) is a technology with which MEAs can be produced on a microscale featuring high spatial resolution and excellent signal-to-noise characteristics. CMOS MEAs are specialized for the analysis of complete electrogenic cellular networks at the cellular or subcellular level in dissociated cultures, organotypic cultures, and acute tissue slices; they can also function as biosensors to detect biochemical events. Models of disease or the response of cellular networks to pharmacological compounds can be studied in vitro, allowing one to investigate pathologies, such as cardiac arrhythmias, memory impairment due to Alzheimer's disease, or vision impairment caused by ganglion cell degeneration in the retin

A review of stochastic resonance: Circuits and measurement

Copyright © 2002 IEEENoise in dynamical systems is usually considered a nuisance. However, in certain nonlinear systems, including electronic circuits and biological sensory systems, the presence of noise can enhance the detection of weak signals. The phenomenon is termed stochastic resonance and is of great interest for electronic instrumentation. We review and investigate the stochastic resonance of several bistable circuits. A new type of S characteristic circuit is demonstrated using simple nonlinear elements with an operational amplifier. Using this circuit, the effects on stochastic resonance were determined as the slope of the S shaped characteristic curve was varied.Gregory P. Harmer, Bruce R. Davis and Derek Abbot

The Mechanisms and Roles of Neural Feedback Loops for Visual Processing

Feedback pathways are widely present in various sensory systems transmitting time-delayed and partly-processed information from higher to lower visual centers. Although feedback loops are abundant in visual systems, investigations focusing on the mechanisms and roles of feedback in terms of micro-circuitry and system dynamics have been largely ignored. Here, we investigate the cellular, synaptic and circuit level properties of a cholinergic isthmic neuron: Ipc) to understand the role of isthmotectal feedback loop in visual processing of red-ear turtles, Trachemys scripta elegans. Turtle isthmotectal complex contains two distinct nuclei, Ipc and Imc, which interact exclusively with the optic tectum, but are otherwise isolated from other brain areas. The cholinergic Ipc neurons receive topographic glutamatergic inputs from tectal SGP neurons and project back to upper tectal layers in a topographic manner while GABAergic Imc neurons, which also get inputs from the SGP neurons project back non-topographically to both the tectum and Ipc nucleus. We have used an isolated eye-attached whole-brain preparation for our investigations of turtle isthmotectal feedback loop. We have investigated the cellular properties of the Ipc neurons by whole-cell blind-patch recordings and found that all Ipc neurons exhibit tonic firing responses to somatic current injections that are well-modeled by a leaky integrate-and-fire neuron with spike rate adaptation. Further investigations reveal that the optic nerve stimulations generate balanced excitatory and inhibitory synaptic currents in the Ipc neurons. We have also found that synaptic connection between the Imc to Ipc neuron is inhibitory. The visual response properties of the Ipc neurons to a range of computer-generated stimuli are investigated using extracellular recordings. We have found that the Ipc neurons have a localized excitatory receptive field and show stimulus selectivity and stimulus-size tuning. We also investigate lateral interactions in the Ipc neurons in response to multiple stimuli within the visual field. Finally, we quantify the oscillatory bursts observed in Ipc responses under visual stimulations

Sensory, decision and control systems, 1 Oct. 1965 - 15 Jul. 1966

Sensory, decision, and control systems for space exploratio

Engineering derivatives from biological systems for advanced aerospace applications

The present study consisted of a literature survey, a survey of researchers, and a workshop on bionics. These tasks produced an extensive annotated bibliography of bionics research (282 citations), a directory of bionics researchers, and a workshop report on specific bionics research topics applicable to space technology. These deliverables are included as Appendix A, Appendix B, and Section 5.0, respectively. To provide organization to this highly interdisciplinary field and to serve as a guide for interested researchers, we have also prepared a taxonomy or classification of the various subelements of natural engineering systems. Finally, we have synthesized the results of the various components of this study into a discussion of the most promising opportunities for accelerated research, seeking solutions which apply engineering principles from natural systems to advanced aerospace problems. A discussion of opportunities within the areas of materials, structures, sensors, information processing, robotics, autonomous systems, life support systems, and aeronautics is given. Following the conclusions are six discipline summaries that highlight the potential benefits of research in these areas for NASA's space technology programs