VSImG: A high frame rate bitmap based display system for neuroscience research

Almeida, Lírio Onofre Baptista de; Slaets, Jan Frans Willem; Köberle, R.

doi:10.1016/j.neucom.2011.02.016

Cited by 2 publications

(3 citation statements)

References 22 publications

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“…We used two experimental setups: Tektronix and Slide. In the Tektronix setup, the fly views vertical bar patterns on a Tektronix 608 monitor, whose update-rate was 500 Hz, so that the bar pattern moved by δx = v(t) every 2 ms [1,2]. In the Slide setup the fly views a large ( 60 cm x 40 cm ) translucent convex cylindrical screen, a slide being projected on the concave side via a simple optical system containing two mirrors.…”

Section: Methods Experimental Setup and Preparationmentioning

confidence: 99%

How to take turns: the fly's way to encode and decode rotational information

Esteves¹,

Fernandes²,

Köberle³

2011

Preprint

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Sensory systems take continuously varying stimuli as their input and encode features relevant for the organism's survival into a sequence of action potentials -spike trains. The full dynamic range of complex dynamical inputs has to be compressed into a set of discrete spike times and the question, facing any sensory system, arises: which features of the stimulus are thereby encoded and how does the animal decode them to recover its external sensory world?Here we study this issue for the two motion-sensitive H1 neurons of the fly's optical system, which are sensitive to horizontal velocity stimuli, each neuron responding to oppositely pointing preferred directions. They constitute an efficient detector for rotations of the fly's body about a vertical axis. Surprisingly the spike trains ρ B (t) generated by an empoverished stimulus S B (t), containing just the instants when the of velocity S(t) reverses its direction, convey the same amount of global (Shannon) information as spike trains ρ(t) generated by the complete stimulus S(t). This amount of information is just enough to encode the instants of velocity reversal. Yet this suffices to give the motor system just one, yet vital order: go left or right, turning the H1 neurons into efficient analog-to-digital converters. Furthermore also probability distributions computed from ρ(t) and ρ B (t) are identical. Still there are regions in the spike trains following velocity reversals, 80 msec long and containing about 3-6 msec long spike intervals, where detailed stimulus properties are encoded. We suggest a decoding scheme -how to

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Section: Methods Experimental Setup and Preparationmentioning

confidence: 99%

How to take turns: the fly's way to encode and decode rotational information

Esteves¹,

Fernandes²,

Köberle³

2011

Preprint

Self Cite

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“…The first input channel was programmed to synchronize DAC conversions, thus we have fed it with 500 Hz (frame rate frequency adopted by the VSImG [15] visual stimulation system). The second and third channels were supplied with close but incommensurable frequencies (6.2 kHz and 6.1 kHz).…”

Section: Experimental Validationmentioning

confidence: 99%

“…The acquisition input is provided to our digital logic by an analog front-end system which generates an asynchronous TTL-compatible signal pulse at the occurrence of each valid spike. Our entire circuit was designed to be compatible and easily inserted into a previous experimental setup [15] devised for studying neural codification in Chrysomya megacephala's visual system, but it is sufficiently generic to be suitable for a wide range of neuroscience experiments.…”

Section: Introductionmentioning

confidence: 99%

Modular Acquisition and Stimulation System for Timestamp-Driven Neuroscience Experiments

Matias

Guariento

Almeida

et al. 2015

Lecture Notes in Computer Science

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Dedicated systems are fundamental for neuroscience experimental protocols that require timing determinism and synchronous stimuli generation. We developed a data acquisition and stimuli generator system for neuroscience research, optimized for recording timestamps from up to 6 spiking neurons and entirely specified in a high-level Hardware Description Language (HDL). Despite the logic complexity penalty of synthesizing from such a language, it was possible to implement our design in a low-cost small reconfigurable device. Under a modular framework, we explored two different memory arbitration schemes for our system, evaluating both their logic element usage and resilience to input activity bursts. One of them was designed with a decoupled and latency insensitive approach, allowing for easier code reuse, while the other adopted a centralized scheme, constructed specifically for our application. The usage of a high-level HDL allowed straightforward and stepwise code modifications to transform one architecture into the other. The achieved modularity is very useful for rapidly prototyping novel electronic instrumentation systems tailored to scientific research.

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VSImG: A high frame rate bitmap based display system for neuroscience research

Cited by 2 publications

References 22 publications

How to take turns: the fly's way to encode and decode rotational information

How to take turns: the fly's way to encode and decode rotational information

Modular Acquisition and Stimulation System for Timestamp-Driven Neuroscience Experiments

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