The receptive field is dead. Long live the receptive field?

Fairhall, Adrienne L.

doi:10.1016/j.conb.2014.02.001

Cited by 23 publications

(20 citation statements)

References 47 publications

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“…However, these are operational definitions and depend on the stimuli. Arguments against such classical receptive field concepts are developing [28], and our results contribute to this. Unlike the traditional, optimal feature viewpoint, selectivity does not appear to be one-dimensional; cells can respond linearly to one part of the stimulus ensemble while being nonlinear to others.…”

Section: Receptive Fields Redux?supporting

confidence: 57%

Flow stimuli reveal ecologically-appropriate responses in mouse visual cortex

Dyballa

Hoseini

Dadarlat

et al. 2018

Preprint

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Assessments of the mouse visual system based on spatial frequency analysis imply that its visual capacity is low, with few neurons responding to spatial frequencies greater than 0.5 cycles/degree. However, visuallymediated behaviors, such as prey capture, suggest that the mouse visual system is more precise. We introduce a new stimulus class-visual flow patterns-that is more like what the mouse would encounter in the natural world than are sine-wave gratings but is more tractable for analysis than are natural images. We used 128-site silicon microelectrodes to measure the simultaneous responses of single neurons in the primary visual cortex (V1) of alert mice. While holding temporal-frequency content fixed, we explored a class of drifting patterns of black or white dots that have energy only at higher spatial frequencies. These flow stimuli evoke strong visually-mediated responses well beyond those predicted by spatial frequency analysis. Flow responses predominate in higher spatial-frequency ranges (0.15-1.6 cycles/degree); many are orientation-or direction-selective; and flow responses of many neurons depend strongly on sign of contrast. Many cells exhibit distributed responses across our stimulus ensemble. Together, these results challenge conventional linear approaches to visual processing and expand our understanding of the mouse's visual capacity to behaviorally-relevant ranges.Significance Statement The visual system of the mouse is now widely studied as a model for development and disease in humans. Studies of its primary visual cortex (V1) using conventional grating stimuli to construct linear-nonlinear receptive fields suggest that the mouse must have very poor vision. Using novel stimuli resembling the flow of images across the retina as the mouse moves through the grass, we find that most V1 neurons respond reliably to very much finer details of the visual scene than previously believed. Our findings suggest that the conventional notion of a unique receptive field does not capture the operation of the neural network in mouse V1.

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Section: Receptive Fields Redux?supporting

confidence: 57%

Flow stimuli reveal ecologically-appropriate responses in mouse visual cortex

Dyballa

Hoseini

Dadarlat

et al. 2018

Preprint

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“…These findings provide crucial evidence that precise spike timing codes casually modulate vertebrate behavior. Additionally, they shift the focus from coding by individual spikes (1,14,19) to coding by multispike patterns and from using spike timing to represent time during a behavioral sequence (20,21) Table S1). (C) Discriminability (d′; full width of the line indicates mean ± SD; bootstrapped) of force profiles in five birds (birds IV1-IV5) after stimulation with the same stimuli as in B.…”

Section: Significancementioning

confidence: 99%

Motor control by precisely timed spike patterns

Srivastava

Holmes

Vellema

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

115

129

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A fundamental problem in neuroscience is understanding how sequences of action potentials ("spikes") encode information about sensory signals and motor outputs. Although traditional theories assume that this information is conveyed by the total number of spikes fired within a specified time interval (spike rate), recent studies have shown that additional information is carried by the millisecond-scale timing patterns of action potentials (spike timing). However, it is unknown whether or how subtle differences in spike timing drive differences in perception or behavior, leaving it unclear whether the information in spike timing actually plays a role in brain function. By examining the activity of individual motor units (the muscle fibers innervated by a single motor neuron) and manipulating patterns of activation of these neurons, we provide both correlative and causal evidence that the nervous system uses millisecond-scale variations in the timing of spikes within multispike patterns to control a vertebrate behavior-namely, respiration in the Bengalese finch, a songbird. These findings suggest that a fundamental assumption of current theories of motor coding requires revision.motor systems | neurophysiology | computational neuroscience | information theory | songbird T he brain uses sequences of spikes to encode sensory and motor signals. In principle, neurons can encode this information via their firing rates, the precise timing of their spikes, or both (1, 2). Although many studies have shown that spike timing contains information beyond that in the rate in sensory codes (3-5), these studies could not verify whether precise timing affects perception or behavior. In motor systems, rate coding approaches dominate (6, 7), but we recently showed that precise spike timing in motor cortex can predict upcoming behavior better than spike rates (8), showing that spike timing carries information in motor as well as sensory cortex. However, as in sensory systems, it remains unknown whether spike timing in motor systems actually controls variations in behavior (9, 10). Resolving this question, therefore, requires examining the spike code used by the neurons that innervate the muscles, because discovering an apparent spike timing code in any brain area upstream of motor neurons is subject to the same ambiguity about whether spike timing patterns actually affect behavior.A spike timing-based theory of motor production predicts that millisecond-scale fluctuations in spike timing, holding other spike train features constant, will causally influence behavior. We tested this prediction by analyzing the activity of single motor units (that is, the muscle fibers innervated by a single motor neuron), focusing largely on the minimal patterns that have variable spike timing but fixed firing rate, burst onset, and burst duration: sequences of three spikes ("triplets"), where the third spike is a fixed latency after the first, but the timing of the middle spike varies. We examined timing codes in songbirds by focusing on respiration, ...

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“…where abundant examples can be found (Moser and Moser 2015;Lettvin et al 1959;Fairhall 2014). Specifically, neurons are expected to code for specific properties of the environment, where its activity is associated with the detection of specific stimuli.…”

Section: The Neuron Doctrine and The Energy Homeostasis Principlementioning

confidence: 99%

“…Also, only a low percentage of neurons present high discharge rates (Olshausen and Field 2005), which should be expected under the Energy Homeostasis Principle scope. Moreover, due to the fact that high discharge rates might trigger changes in functional connectivity (synaptic weights), it should not be surprising that when presenting more complex visual scenes, classic receptive fields are no longer detectable (Fairhall 2014). We may consider that classic stimulation visual protocols impose an energy input, reflected in the high discharge rate, which needs to be managed.…”

Section: The Neuron Doctrine and The Energy Homeostasis Principlementioning

confidence: 99%

“…Since then, the same logic has spread into coding paradigms (Fairhall 2014;Lettvin et al 1959;Yuste 2015), especially in information processing frameworks (Friston 2002;Lorenz, Jeng, and Deem 2011;Robbins 2010;Fodor 1983), and has been scaled from neurons up to neural networks (Yuste 2015). A common and key element of these conceptual approaches has been to find neuronal correlates of behaviors, effectively associating specific behaviors with distinct neural patterns.…”

Section: Introductionmentioning

confidence: 99%

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The Energy Homeostasis Principle: Neuronal Energy Regulation Drives Local Network Dynamics Generating Behavior

Vergara

Jaramillo

Luarte

et al. 2019

Preprint

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A major goal of neuroscience is understanding how neurons arrange themselves into neural networks that result in behavior. Most theoretical and experimental efforts have focused on a top-down approach which seeks to identify neuronal correlates of behaviors. This has been accomplished by effectively mapping specific behaviors to distinct neural patterns, or by creating computational models that produce a desired behavioral outcome. Nonetheless, these approaches have only implicitly considered the fact that neural tissue, like any other physical system, is subjected to several restrictions and boundaries of operations.Here, we propose a new, bottom-up conceptual paradigm: The Energy Homeostasis Principle, where the balance between energy income, expenditure, and availability are the key parameters in determining the dynamics of the found neuronal phenomena from molecular to behavioral levels. Neurons display high energy consumption relative to other cells, with metabolic consumption of the brain representing 20% of the whole-body oxygen uptake, contrasting with this organ representing only 2% of the body weight. Also, neurons have specialized surrounding tissue providing the necessary energy which, in the case of the brain, is provided by astrocytes. Moreover, and unlike other cell types with high energy demands such as muscle cells, neurons have strict aerobic metabolism. These facts indicate that neurons are highly sensitive to energy limitations, with Gibb’s free energy dictating the direction of all cellular metabolic processes. From this activity, the largest energy, by far, is expended by action potentials and post-synaptic potentials; therefore, plasticity can be reinterpreted in terms of their energy context. Consequently, neurons, through their synapses, impose energy demands over post-synaptic neurons in a close loop-manner, modulating the dynamics of local circuits. Subsequently, the energy dynamics end up impacting the homeostatic mechanisms of neuronal networks. Furthermore, local energy management also emerges as a neural population property, where most of the energy expenses are triggered by sensory or other modulatory inputs. Local energy management in neurons may be sufficient to explain the emergence of behavior, enabling the assessment of which properties arise in neural circuits and how. Essentially, the proposal of the Energy Homeostasis Principle is also readily testable for simple neuronal networks.

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The receptive field is dead. Long live the receptive field?

Cited by 23 publications

References 47 publications

Flow stimuli reveal ecologically-appropriate responses in mouse visual cortex

Flow stimuli reveal ecologically-appropriate responses in mouse visual cortex

Motor control by precisely timed spike patterns

The Energy Homeostasis Principle: Neuronal Energy Regulation Drives Local Network Dynamics Generating Behavior

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