Area V4 is the first object-specific processing stage in the ventral visual pathway, just as area MT is the first motion-specific processing stage in the dorsal pathway. For almost 50 years, coding of object shape in ...
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Area V4 is the first object-specific processing stage in the ventral visual pathway, just as area MT is the first motion-specific processing stage in the dorsal pathway. For almost 50 years, coding of object shape in V4 has been studied and conceived in terms of flat pattern processing, given its early position in the transformation of 2D visual images. Here, however, in awake monkey recording experiments, we found that roughly half of V4 neurons are more tuned and responsive to solid, 3D shape-in-depth, as conveyed by shading, specularity, reflection, refraction, or disparity cues in images. Using 2-photon functional microscopy, we found that flat- and solid-preferring neurons were segregated into separate modules across the surface of area V4. These findings should impact early shape-processing theories and models, which have focused on 2D pattern processing. In fact, our analyses of early object processing in AlexNet, a standard visual deep network, revealed a similar distribution of sensitivities to flat and solid shape in layer 3. Early processing of solid shape, in parallel with flat shape, could represent a computational advantage discovered by both primate brain evolution and deep-network training.
The olfactory system is a well-known model for studying the temporal encoding of sensory stimuli due to its rhythmic stimulus delivery through respiration. Sniff-locked activity is pervasive in the primary olfactory a...
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The olfactory system is a well-known model for studying the temporal encoding of sensory stimuli due to its rhythmic stimulus delivery through respiration. Sniff-locked activity is pervasive in the primary olfactory area, the olfactory bulb, and is considered critical to structuring the output of its computation. We tested the behavioural importance of these temporal features using simple closed-loop optogenetics embedded in custom behavioural paradigms. We found that mice perceive differences in evoked spike counts and discriminate between synchronous vs. asynchronous activations of the output neurons. Surprisingly, they failed to distinguish the timing of evoked activity relative to the sniff cycle. These results suggest that, beyond the first steps of olfactory processing, sniff rhythms play a more nuanced role, with greater reliance on the spike rate and synchrony for the neural encoding of the environment, consistent with a gradual transformation of encoding format at successive stages of sensory processing.
To understand how anatomy and physiology allow an organism to perform its function, it is important to know how information that is transmitted by spikes in the brain is received and encoded. A natural question is whe...
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To understand how anatomy and physiology allow an organism to perform its function, it is important to know how information that is transmitted by spikes in the brain is received and encoded. A natural question is whether the spike rate alone encodes the information about a stimulus (rate code), or additional information is contained in the temporal pattern of the spikes (temporal code). Here we address this question using data from the cat Lateral Geniculate Nucleus (LGN), which is the visual portion of the thalamus, through which visual information from the retina is communicated to the visual cortex. We analyzed the responses of LGN neurons to spatially homogeneous spots of various sizes with temporally random luminance modulation. We compared the Firing Rate with the Shannon Information Transmission Rate , which quantifies the information contained in the temporal relationships between spikes. We found that the behavior of these two rates can differ quantitatively. This suggests that the energy used for spiking does not translate directly into the information to be transmitted. We also compared Firing Rates with Information Rates for X-ON and X-OFF cells. We found that, for X-ON cells the Firing Rate and Information Rate often behave in a completely different way, while for X-OFF cells these rates are much more highly correlated. Our results suggest that for X-ON cells a more efficient temporal code is employed, while for X-OFF cells a straightforward rate code is used, which is more reliable and is correlated with energy consumption.
Optogenetics enables experimental control over neural activity using light. Channelrhodopsin and its variants are typically activated using visible light excitation but can also be activated using infrared two-photon ...
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Optogenetics enables experimental control over neural activity using light. Channelrhodopsin and its variants are typically activated using visible light excitation but can also be activated using infrared two-photon excitation. Two-photon excitation can improve the spatial precision of stimulation in scattering tissue but has several practical limitations that need to be considered before use. Here we describe the methodology and best practices for using two-photon optogenetic stimulation of neurons within the brain of the fruit fly, Drosophila melanogaster, with an emphasis on projection neurons of the antennal lobe. less
neural modulation in primate motor cortex exhibits complex patterns. We found that modulation during reaching could be separated into discrete temporal epochs. To determine if these epochs are driven by behavioral eve...
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neural modulation in primate motor cortex exhibits complex patterns. We found that modulation during reaching could be separated into discrete temporal epochs. To determine if these epochs are driven by behavioral events, monkeys performed variations of a center-out reaching task. Monkeys viewed a computer cursor matched to hand position and a radial target at 1 of 16 locations. In some trials, they performed a visuomotor rotation (the cursor moved at an angle to the hand). After adaptation, encoding changes for single units are temporally structured: adaptation could affect one temporal component of a unit's response but not another. In half the normal and perturbed trials, we removed visual feedback before movement. Adaptation-sensitive firing components toward the end of movement are often weak or absent during reaches without feedback. These results show that temporal structure in motor cortical activity is driven by behavior, with a discrete component related to visual feedback.
Theories of neural coding in the taste system typically rely exclusively on data gleaned from tasteresponsive cells. However, even in the nucleus tractus solitarius (NTS), the first stage of central processing, neuron...
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Theories of neural coding in the taste system typically rely exclusively on data gleaned from tasteresponsive cells. However, even in the nucleus tractus solitarius (NTS), the first stage of central processing, neurons with taste selectivity coexist with neurons whose activity is linked to motor behavior related to ingestion. We recorded from a large (n = 324) sample of NTS neurons recorded in awake rats, examining both their taste selectivity and the association of their activity with licking. All subjects were implanted with a bundle of microelectrodes aimed at the NTS and allowed to recover. Following moderate water deprivation, rats were placed in an experimental chamber where tastants or artificial saliva (AS) were delivered from a lick spout. Electrophysiological responses were recorded, and waveforms from single cells were isolated offline. Results showed that only a minority of NTS cells responded to taste stimuli as determined by conventional firing-rate measures. In contrast, most cells, including taste-responsive cells, tracked the lick pattern, as evidenced by significant lick coherence in the 5- to 7-Hz range. Several quantitative measures of taste selectivity and lick relatedness showed that the population formed a continuum, ranging from cells dominated by taste responses to those dominated by lick relatedness. Moreover, even neurons whose responses were highly correlated with lick activity could convey substantial information about taste quality. In all, data point to a blurred boundary between taste-dominated and lick-related cells in NTS, suggesting that information from the taste of food and from the movements it evokes are seamlessly integrated. NEW & NOTEWORTHY Neurons in the rostral nucleus of the solitary tract (NTS) are known to encode information about taste. However. recordings from awake rats reveal that only a minority of NTS cells respond exclusively to taste stimuli. The majority of neurons track behaviors associated with food consumpti
A convex code is a binary code generated by the pattern of intersections of a collection of open convex sets in some Euclidean space. Convex codes are relevant to neuroscience as they arise from the activity of neuron...
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A convex code is a binary code generated by the pattern of intersections of a collection of open convex sets in some Euclidean space. Convex codes are relevant to neuroscience as they arise from the activity of neurons that have convex receptive fields. In this paper, we develop algebraic methods to determine if a code is convex. Specifically, we use the neural ideal of a code, which is a generalization of the Stanley Reisner ideal. Using the neural ideal together with its standard generating set, the canonical form, we provide algebraic signatures of certain families of codes that are non-convex. We connect these signatures to the precise conditions on the arrangement of sets that prevent the codes from being convex. Finally, we also provide algebraic signatures for some families of codes that are convex, including the class of intersection-complete codes. These results allow us to detect convexity and non-convexity in a variety of situations, and point to some interesting open questions. (C) 2018 Elsevier B.V. All rights reserved.
All sensory information is encoded in neural spike trains. It is unknown how the brain utilizes this neural code to drive behavior. Here, we unravel the decoding rules of the brain at the most elementary level by link...
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All sensory information is encoded in neural spike trains. It is unknown how the brain utilizes this neural code to drive behavior. Here, we unravel the decoding rules of the brain at the most elementary level by linking behavioral decisions to retinal output signals in a single-photon detection task. A transgenic mouse line allowed us to separate the two primary retinal outputs, ON and OFF pathways, carrying information about photon absorptions as increases and decreases in spiking, respectively. We measured the sensitivity limit of rods and the most sensitive ON and OFF ganglion cells and correlated these results with visually guided behavior using markerless head and eye tracking. We show that behavior relies only on the ON pathway even when the OFF pathway would allow higher sensitivity. Paradoxically, behavior does not rely on the spike code with maximal information but instead relies on a decoding strategy based on increases in spiking.
Goal-driven and feedforward-only convolutional neural networks (CNN) have been shown to be able to predict and decode cortical responses to natural images or videos. Here, we explored an alternative deep neural networ...
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Goal-driven and feedforward-only convolutional neural networks (CNN) have been shown to be able to predict and decode cortical responses to natural images or videos. Here, we explored an alternative deep neural network, variational auto-encoder (VAE), as a computational model of the visual cortex. We trained a VAE with a five-layer encoder and a five-layer decoder to learn visual representations from a diverse set of unlabeled images. Using the trained VAE, we predicted and decoded cortical activity observed with functional magnetic resonance imaging (fMRI) from three human subjects passively watching natural videos. Compared to CNN, VAE could predict the video-evoked cortical responses with comparable accuracy in early visual areas, but relatively lower accuracy in higher-order visual areas. The distinction between CNN and VAE in terms of encoding performance was primarily attributed to their different learning objectives, rather than their different model architecture or number of parameters. Despite lower encoding accuracies, VAE offered a more convenient strategy for decoding the fMRI activity to reconstruct the video input, by first converting the fMRI activity to the VAE's latent variables, and then converting the latent variables to the reconstructed video frames through the VAE's decoder. This strategy was more advantageous than alternative decoding methods, e.g. partial least squares regression, for being able to reconstruct both the spatial structure and color of the visual input. Such findings highlight VAE as an unsupervised model for learning visual representation, as well as its potential and limitations for explaining cortical responses and reconstructing naturalistic and diverse visual experiences.
Neurons in sensory areas of the neocortex are known to represent information both about sensory stimuli and behavioral state, but how these 2 disparate signals are integrated across cortical layers is poorly understoo...
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Neurons in sensory areas of the neocortex are known to represent information both about sensory stimuli and behavioral state, but how these 2 disparate signals are integrated across cortical layers is poorly understood. To study this issue, we measured the coding of visual stimulus orientation and of behavioral state by neurons within superficial and deep layers of area V4 in monkeys while they covertly attended or prepared eye movements to visual stimuli. We show that whereas single neurons and neuronal populations in the superficial layers conveyed more information about the orientation of visual stimuli than neurons in deep layers, the opposite was true of information about the behavioral relevance of those stimuli. In particular, deep layer neurons encoded greater information about the direction of planned eye movements than superficial neurons. These results suggest a division of labor between cortical layers in the coding of visual input and visually guided behavior.
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