Abstract:Abstract. We propose a novel model of visual contrast measurement based on segregated ON and OFF pathways. Two driving forces have shaped our investigation: (1) establishing a mechanism selective for sharp local transitions in the luminance distribution; (2) generating a robust scheme of oriented contrast detection. Our starting point was the architecture of early stages in the mammalian visual system. We show that the circuit behaves as a soft AND-gate and analyze the scale-space selectivity properties of the… Show more
“…Also, Nirenberg and Meister (1997) have shown that transient and motion-sensitive responses in ganglion cells may be produced by self-inhibitory feedback of the amacrine cells in mouse retina (for similar results, see Berry, Brivanlou, Jordan, & Meister, 1999;Victor, 1987;Crevier & Meister, 1998). Computational models also suggested that self-inhibition may exist in cells sensitive to light-dark contrast (Neumann, Pessoa, & Hanse, 1999). Disinhibition can effectively reduce the amount of inhibition where there is a large area of bright input, and self-inhibitioncan give rise to oscillations in the response over time.…”
“…Also, Nirenberg and Meister (1997) have shown that transient and motion-sensitive responses in ganglion cells may be produced by self-inhibitory feedback of the amacrine cells in mouse retina (for similar results, see Berry, Brivanlou, Jordan, & Meister, 1999;Victor, 1987;Crevier & Meister, 1998). Computational models also suggested that self-inhibition may exist in cells sensitive to light-dark contrast (Neumann, Pessoa, & Hanse, 1999). Disinhibition can effectively reduce the amount of inhibition where there is a large area of bright input, and self-inhibitioncan give rise to oscillations in the response over time.…”
“…The neural architecture shown in Fig. 3b is sharing some similarities with the one presented in 55 to measure contrast. However, they are both fundamentally different as one is focusing on measuring spatial contrast with no consideration to the temporal domain, while the other detects the maximum contrast in time and in space, by detecting the shortest duration between polarity changes.Figure 3Spiking neural network.…”
Depth from defocus is an important mechanism that enables vision systems to perceive depth. While machine vision has developed several algorithms to estimate depth from the amount of defocus present at the focal plane, existing techniques are slow, energy demanding and mainly relying on numerous acquisitions and massive amounts of filtering operations on the pixels’ absolute luminance value. Recent advances in neuromorphic engineering allow an alternative to this problem, with the use of event-based silicon retinas and neural processing devices inspired by the organizing principles of the brain. In this paper, we present a low power, compact and computationally inexpensive setup to estimate depth in a 3D scene in real time at high rates that can be directly implemented with massively parallel, compact, low-latency and low-power neuromorphic engineering devices. Exploiting the high temporal resolution of the event-based silicon retina, we are able to extract depth at 100 Hz for a power budget lower than a 200 mW (10 mW for the camera, 90 mW for the liquid lens and ~100 mW for the computation). We validate the model with experimental results, highlighting features that are consistent with both computational neuroscience and recent findings in the retina physiology. We demonstrate its efficiency with a prototype of a neuromorphic hardware system and provide testable predictions on the role of spike-based representations and temporal dynamics in biological depth from defocus experiments reported in the literature.
“…This is motivated from data and models for retinal ganglion cells (Koenderink & van Doorn, 1990;Rodieck, 1965). In these models, field components were integrated either linearly (Marr & Hildreth, 1980;Rodieck, 1965) or with other mechanisms (Furman, 1965;Grossberg, 1970), all of which can be combined into one formal framework (Neumann, Pessoa, & Hanse, 1999).…”
Section: Bottom-up Construction With Biologically Plausible Representationsmentioning
Although hippocampal grid cells are thought to be crucial for spatial navigation, their computational purpose remains disputed. Recently, they were proposed to represent spatial transitions and convey this knowledge downstream to place cells. However, a single scale of transitions is insufficient to plan long goal-directed sequences in behaviorally acceptable time. Here, a scale-space data structure is suggested to optimally accelerate retrievals from transition systems, called transition scale-space (TSS). Remaining exclusively on an algorithmic level, the scale increment is proved to be ideally [Formula: see text] for biologically plausible receptive fields. It is then argued that temporal buffering is necessary to learn the scale-space online. Next, two modes for retrieval of sequences from the TSS are presented: top down and bottom up. The two modes are evaluated in symbolic simulations (i.e., without biologically plausible spiking neurons). Additionally, a TSS is used for short-cut discovery in a simulated Morris water maze. Finally, the results are discussed in depth with respect to biological plausibility, and several testable predictions are derived. Moreover, relations to other grid cell models, multiresolution path planning, and scale-space theory are highlighted. Summarized, reward-free transition encoding is shown here, in a theoretical model, to be compatible with the observed discretization along the dorso-ventral axis of the medial entorhinal cortex. Because the theoretical model generalizes beyond navigation, the TSS is suggested to be a general-purpose cortical data structure for fast retrieval of sequences and relational knowledge. Source code for all simulations presented in this paper can be found at https://github.com/rochus/transitionscalespace .
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