Investigating the relationship between tuning and spike timing is necessary to understand how neuronal populations in anterior visual cortex process complex stimuli. Are tuning and spontaneous spike time synchrony lin...
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Investigating the relationship between tuning and spike timing is necessary to understand how neuronal populations in anterior visual cortex process complex stimuli. Are tuning and spontaneous spike time synchrony linked by a common spatial structure (do some cells covary more strongly, even in the absence of visual stimulation?), and what is the object coding capability of this structure? Here, we recorded from spiking populations in macaque inferior temporal (IT) cortex under neurolept anesthesia. We report that, although most nearby IT neurons are weakly correlated, neurons with more similar tuning are also more synchronized during spontaneous activity. This link between tuning and synchrony was not simply due to cell separation distance. Instead, it expands on previous reports that neurons along an IT penetration are tuned to similar but slightly different features. This constraint on possible population firing rate patterns was consistent across stimulus sets, including animate vs. inanimate object categories. A classifier trained on this structure was able to generalize category "read-out" to untrained objects using only a few dimensions (a few patterns of site weightings per electrode array). We suggest that tuning and spike synchrony are linked by a common spatial structure that is highly efficient for object representation.
The intrinsic parallelism of visual neural architectures based on distributed hierarchical layers is well suited to be implemented on the multi-core architectures of modern graphics cards. The design strategies that a...
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The intrinsic parallelism of visual neural architectures based on distributed hierarchical layers is well suited to be implemented on the multi-core architectures of modern graphics cards. The design strategies that allow us to optimally take advantage of such parallelism, in order to efficiently map on GPU the hierarchy of layers and the canonical neural computations, are proposed. Specifically, the advantages of a cortical map-like representation of the data are exploited. Moreover, a GPU implementation of a novel neural architecture for the computation of binocular disparity from stereo image pairs, based on populations of binocular energy neurons, is presented. The implemented neural model achieves good performances in terms of reliability of the disparity estimates and a near real-time execution speed, thus demonstrating the effectiveness of the devised design strategies. The proposed approach is valid in general, since the neural building blocks we implemented are a common basis for the modeling of visual neural functionalities.
Elasmobranchs can detect a little amount of electric fields and they have characteristic approach strategies to find an electric dipole source generated by prey or conspecifics. They appear to align the body at a cons...
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Elasmobranchs can detect a little amount of electric fields and they have characteristic approach strategies to find an electric dipole source generated by prey or conspecifics. They appear to align the body at a constant angle with the current flow line of the electric field while swimming towards prey. However, it has not been studied how they process the perception of electric fields for the approach behaviour or what kind of neural mechanism is used. We use a model of electrosensory perception with electrodynamics and explore a possible approach mechanism based on the sensory landscape distributed on electroreceptors. This paper presents that elasmobranchs can estimate the direction of the electric field by swaying their head, which will be a basis information for their particular approach behaviour. A velocity profile of voltage gradients and intensity difference among the ampullary clusters can be another cues to detect a prey source. (c) 2006 Elsevier Ireland Ltd. All rights reserved.
To maximize future rewards in this ever-changing world, animals must be able to discover the temporal structure of stimuli and then anticipate or act correctly at the right time. How do animals perceive, maintain, and...
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To maximize future rewards in this ever-changing world, animals must be able to discover the temporal structure of stimuli and then anticipate or act correctly at the right time. How do animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multiseconds in working memory? How is temporal information processed concurrently with spatial information and decision making? Why are there strong neuronal temporal signals in tasks in which temporal information is not required? A systematic understanding of the underlying neural mechanisms is still lacking. Here, we addressed these problems using supervised training of recurrent neural network models. We revealed that neural networks perceive elapsed time through state evolution along stereotypical trajectory, maintain time intervals in working memory in the monotonic increase or decrease of the firing rates of interval-tuned neurons, and compare or produce time intervals by scaling state evolution speed. Temporal and nontemporal information is coded in subspaces orthogonal with each other, and the state trajectories with time at different nontemporal information are quasiparallel and isomorphic. Such coding geometry facilitates the decoding generalizability of temporal and nontemporal information across each other. The network structure exhibits multiple feedforward sequences that mutually excite or inhibit depending on whether their preferences of nontemporal information are similar or not. We identified four factors that facilitate strong temporal signals in nontiming tasks, including the anticipation of coming events. Our work discloses fundamental computational principles of temporal processing, and it is supported by and gives predictions to a number of experimental phenomena.
The study of complex information processing systems requires appropriate theoretical tools to help unravel their underlying design principles. Information theory is one such tool, and has been utilized extensively in ...
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The study of complex information processing systems requires appropriate theoretical tools to help unravel their underlying design principles. Information theory is one such tool, and has been utilized extensively in the study of the neural code. Although much progress has been made in information theoretic methodology, there is still no satisfying answer to the question: "What is the information that a given property of the neural population activity (e.g., the responses of single cells within the population) carries about a set of stimuli?" Here, we answer such questions via the minimum mutual information (MinMI) principle. We quantify the information in any statistical property of the neural response by considering all hypothetical neuronal populations that have the given property and finding the one that contains the minimum information about the stimuli. All systems with higher information values necessarily contain additional information processing mechanisms and, thus, the minimum captures the information related to the given property alone. MinMI may be used to measure information in properties of the neural response, such as that conveyed by responses of small subsets of cells (e.g., singles or pairs) in a large population and cooperative effects between subunits in networks. We show how the framework can be used to study neural coding in large populations and to reveal properties that are not discovered by other information theoretic methods.
Physiological studies suggest that decision networks read from the neural representation in the middle temporal area to determine the perceived direction of visual motion, whereas psychophysical studies tend to charac...
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Physiological studies suggest that decision networks read from the neural representation in the middle temporal area to determine the perceived direction of visual motion, whereas psychophysical studies tend to characterize motion perception in terms of the statistical properties of stimuli. To reconcile these different approaches, we examined whether estimating the central tendency of the physical direction of global motion was a better indicator of perceived direction than algorithms (e.g., maximum likelihood) that read from directionally tuned mechanisms near the end of the motion pathway. The task of human observers was to discriminate the global direction of random dot kinematograms composed of asymmetrical distributions of local directions with distinct measures of central tendency. None of the statistical measures of image direction central tendency provided consistently accurate predictions of perceived global motion direction. However, regardless of the local composition of motion directions, a maximum-likelihood decoder produced global motion estimates commensurate with the psychophysical data. Our results suggest that mechanism-based, read-out algorithms offer a more accurate and robust guide to human motion perception than any stimulus-based, statistical estimate of central tendency.
In two previous studies, we had demonstrated the influence of eye position on neuronal discharges in the middle temporal area, medial superior temporal area, lateral intraparietal area and area 7A of the awake monkey ...
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In two previous studies, we had demonstrated the influence of eye position on neuronal discharges in the middle temporal area, medial superior temporal area, lateral intraparietal area and area 7A of the awake monkey (Bremmer et al., 1997a,b), Eye position effects also have been found in visual cortical areas V3A and V6 and even in the premotor cortex and the supplementary eye field. These effects are generally discussed in light of a coordinate transformation of visual signals into a non-retinocentric frame of reference. Neural network studies dealing with the eye position effect succeeded in constructing such non-retinocentric representations by using model neurones whose response characteristics resembled those of 'real' neurones. However, to our knowledge, response properties of real neurones never acted as input into these neural networks. In the present study, we thus investigated whether, theoretically, eye position could be estimated from the population discharge of the (previously) recorded neurones and, if so, we intended to develop an encoding algorithm for the position of the eyes in the orbit. The optimal linear estimator proved the capability of the ensemble activity for determining correctly eye position. We then developed the so-called subpopulation encoding of eye position. This algorithm is based on the partition of the ensemble of neurones into two pairs of subpopulations. Eye position is represented by the differences of activity levels within each pair of subpopulations. Considering this result, encoding of the location of an object relative to the head could easily be accomplished by combining eye position information with the intrinsic knowledge about the retinal location of a visual stimulus. Taken together, these results show that throughout the monkey's visual cortical system information is available which can be used in a fairly simple manner in order to generate a non-retinocentric representation of visual information.
Mustached bats emit an acoustically rich variety of calls for social communication. In the posterior primary auditory cortex, activity of neural ensembles measured as local field potentials (LFPs) can uniquely encode ...
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Mustached bats emit an acoustically rich variety of calls for social communication. In the posterior primary auditory cortex, activity of neural ensembles measured as local field potentials (LFPs) can uniquely encode each call type. Here we report that LFPs recorded in response to calls contain oscillatory activity in the gamma-band frequency range (> 20 Hz). The power spectrum of these high-frequency oscillations shows either two peaks of energy (at 40 Hz and 100 Hz), or just one peak at 40 Hz. The relative power of gamma-band activity in the power spectrum of a call-evoked UP correlates significantly with the 'harmonic complexity' of a call. Gamma-band activity is attenuated with reversal of frequency-modulated calls. Amplitude modulation, even when asymmetric across call reversals, has no significant effect on gamma-band activity. These results provide the first experimental evidence that complex features within different groups of species-specific calls modify the power spectrum of evoked gamma-band activity. (c) 2007 Elsevier B.V. All rights reserved.
Attention is critical to high-level cognition and attention deficits are a hallmark of neurologic and neuropsychiatric disorders. Although years of research indicates that distinct neuromodulators influence attentiona...
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Attention is critical to high-level cognition and attention deficits are a hallmark of neurologic and neuropsychiatric disorders. Although years of research indicates that distinct neuromodulators influence attentional control, amechanistic account that traverses levels of analysis (cells, circuits, behavior) is missing. However, such an account is critical to guide the development of next-generation pharmacotherapies aimed at forestalling or remediating the global burden associated with disorders of attention. Here, we summarize current neuroscientific understanding of how attention affects single neurons and networks of neurons. We then review key results that have informed our understanding of how neuromodulation shapes these neuron and network properties and thereby enables the appropriate allocation of attention to relevant external or internal events. Finally, we highlight areas where we believe hypotheses can be formulated and tackled experimentally in the near future, thereby critically increasing our mechanistic understanding of how attention is implemented at the cellular and network levels.
The electrical activity of diverse brain cells is modulated across states of vigilance, namely wakefulness, non-rapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Enhanced activity of neuronal circui...
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The electrical activity of diverse brain cells is modulated across states of vigilance, namely wakefulness, non-rapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Enhanced activity of neuronal circuits during NREM sleep impacts on subsequent awake behaviors, yet the significance of their activation, or lack thereof, during REM sleep remains unclear. This review focuses on feeding-promoting cells in the lateral hypothalamus (LH) that express the vesicular GABA and glycine transporter (vgat) as a model to further understand the impact of REM sleep on neural encoding of goal-directed behavior. It emphasizes both spatial and temporal aspects of hypothalamic cell dynamics across awake behaviors and REM sleep, and discusses a role for REM sleep in brain plasticity underlying energy homeostasis and behavioral optimization.
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