Cortical circuits encoding sensory information consist of populations of neurons, yet how information aggregates via pooling individual cells remains poorly understood. Such pooling may be particularly important in no...
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Cortical circuits encoding sensory information consist of populations of neurons, yet how information aggregates via pooling individual cells remains poorly understood. Such pooling may be particularly important in noisy settings where single-neuron encoding is degraded. One example is the cocktail party problem, with competing sounds from multiple spatial locations. How populations of neurons in auditory cortex code competing sounds have not been previously investigated. Here, we apply a novel information-theoretic approach to estimate information in populations of neurons in mouse auditory cortex about competing sounds from multiple spatial locations, including both summed population (SP) and labeled line (LL) codes. We find that a small subset of neurons is sufficient to nearly maximize mutual information over different spatial configurations, with the labeled line code outperforming the summed population code and approaching information levels attained in the absence of competing stimuli. Finally, information in the labeled line code increases with spatial separation between target and masker, in correspondence with behavioral results on spatial release from masking in humans and animals. Taken together, our results reveal that a compact population of neurons in auditory cortex provides a robust code for competing sounds from different spatial locations.
Precisely timed action potentials related to stimuli and behavior have been observed in the cerebral cortex. However, information carried by the precise spike timing has to propagate through many cortical areas, and n...
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Precisely timed action potentials related to stimuli and behavior have been observed in the cerebral cortex. However, information carried by the precise spike timing has to propagate through many cortical areas, and noise could disrupt millisecond precision during the transmission. Previous studies have demonstrated that only strong stimuli that evoke a large number of spikes with small dispersion of spike times can propagate through multilayer networks without degrading the temporal precision. Here we show that feedback projections can increase the number of spikes in spike volleys without degrading their temporal precision. Feedback also increased the range of spike volleys that can propagate through multilayer networks. Our work suggests that feedback projections could be responsible for the reliable propagation of information encoded in spike times through cortex, and thus could serve as an attentional mechanism to regulate the flow of information in the cortex. Feedback projections may also participate in generating spike synchronization that is engaged in cognitive behaviors by the same mechanisms described here for spike propagation.
Olfaction is fundamentally distinct from other sensory modalities. Natural odor stimuli are complex mixtures of volatile chemicals that interact in the nose with a receptor array that, in rodents, is built from more t...
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Olfaction is fundamentally distinct from other sensory modalities. Natural odor stimuli are complex mixtures of volatile chemicals that interact in the nose with a receptor array that, in rodents, is built from more than 1,000 unique receptors. These interactions dictate a peripheral olfactory code, which in the brain is transformed and reformatted as it is broadcast across a set of highly interconnected olfactory regions. Here we discuss the problems of characterizing peripheral population codes for olfactory stimuli, of inferring the specific functions of different higher olfactory areas given their extensive recurrence, and of ultimately understanding how odor representations are linked to perception and action. We argue that, despite the differences between olfaction and other sensory modalities, addressing these specific questions will reveal general principles underlying brain function.
In this paper, we report a detailed study of the dynamic properties of horizontal, vertical and oblique saccades. These eye movements were measured with an improved version of the double-magnetic induction method in t...
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In this paper, we report a detailed study of the dynamic properties of horizontal, vertical and oblique saccades. These eye movements were measured with an improved version of the double-magnetic induction method in two rhesus monkeys. We found that onsets of orthogonal components of oblique saccades are so well synchronized in the monkey that a common initiation system seems likely. Saccade vectors obeyed a nonlinear peakvelocity/amplitude relationship in all directions. The peak-velocity/duration/amplitude relationship for components was not fixed, but depended on the relative size of the orthogonal component: for a component with a given size, its duration increased and its peak velocity decreased, as the saccade vector to which it contributed turned away from the component direction under consideration. This stretching effect, which reflects a nonlinearity in the system, was negligible for small saccade vectors but became very pronounced in large oblique saccades. These experimental data were confronted with quantitative predictions derived from two different models for the generation of saccades in two dimensions. It appears that a model which assumes the existence of synchronized, but otherwise independent, pulse generators for horizontal and vertical components must be rejected. An alternative model, featuring a nonlinear vectorial pulse generator followed by a decomposition stage which generates component velocity command signals from the vectorial eye velocity signal, provides good fit with the data. According to this common-source model, the two nonlinear phenomena observed, viz., the curvilinear peak-velocity/amplitude relationship of saccades in all directions and component stretching in large oblique saccades, are due to a single nonlinearity in the proposed vectorial pulse generator. A possible neural basis for the common-source model is discussed.
Recognizing objects in cluttered scenes requires attentional mechanisms to filter out distracting information. Previous studies have found several physiological correlates of attention in visual cortex, including larg...
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Recognizing objects in cluttered scenes requires attentional mechanisms to filter out distracting information. Previous studies have found several physiological correlates of attention in visual cortex, including larger responses for attended objects. However, it has been unclear whether these attention-related changes have a large impact on information about objects at the neural population level. To address this question, we trained monkeys to covertly deploy their visual attention from a central fixation point to one of three objects displayed in the periphery, and we decoded information about the identity and position of the objects from populations of similar to 200 neurons from the inferior temporal cortex using a pattern classifier. The results show that before attention was deployed, information about the identity and position of each object was greatly reduced relative to when these objects were shown in isolation. However, when a monkey attended to an object, the pattern of neural activity, represented as a vector with dimensionality equal to the size of the neural population, was restored toward the vector representing the isolated object. Despite this nearly exclusive representation of the attended object, an increase in the salience of nonattended objects caused "bottom-up" mechanisms to override these "top-down" attentional enhancements. The method described here can be used to assess which attention-related physiological changes are directly related to object recognition, and should be helpful in assessing the role of additional physiological changes in the future.
A review is provided of the chemical components in tissue extracts that elicit feeding behavior in marine fish and crustaceans. For most species, the major stimulants of feeding behavior in excitatory extracts are an ...
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A review is provided of the chemical components in tissue extracts that elicit feeding behavior in marine fish and crustaceans. For most species, the major stimulants of feeding behavior in excitatory extracts are an assemblage of common metabolites of low molecular weight including amino acids, quaternary ammonium compounds, nucleosides and nucleotides, and organic acids. It is often mixtures of substances rather than individual components that account for the stimulatory capacity of a natural extract. Recent studies using a shrimp,Palaemonetes pugio, are described in which behavioral bioassays were conducted with complex synthetic mixtures formulated on the basis of the composition of four tissue extracts. These results indicate that synergistic interactions occur among the mixture components. The neural mechanisms whereby marine crustaceans receive and code information about chemical mixtures are also reviewed. Narrowly tuned receptor cells, excited only by particular components of food extracts such as specific amino acids, nucleotides, quaternary ammonium compounds, and ammonium ions, are common in lobsters and could transmit information about mixtures as a labeled-line code. However, since physiological recordings indicate that most higher-level neurons in the brain each transmit information about many components of mixtures, rather than about a single component, it is suggested that information about a complex food odor is transmitted as an across-fiber pattern, instead of a labeled-line code. Electrophysiological recordings of responses of peripheral and central neurons of lobsters to odor mixtures and their components reveal that suppressive interactions occur, rather than the synergistic interactions noted earlier in the behavioral studies. Possible reasons for these differences are discussed. Evidence from the behavioral study indicates that the “direction” of a mixture interaction can be concentration-dependent and the synergism may occur at low mixture
Are symbolic and nonsymbolic numbers coded differently in the brain? Neuronal data indicate that overlap in numerical tuning curves is a hallmark of the approximate, analogue nature of nonsymbolic number representatio...
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Are symbolic and nonsymbolic numbers coded differently in the brain? Neuronal data indicate that overlap in numerical tuning curves is a hallmark of the approximate, analogue nature of nonsymbolic number representation. Consequently, patterns of fMRI activity should be more correlated when the representational overlap between two numbers is relatively high. In bilateral intraparietal sulci (IPS), for nonsymbolic numbers, the pattern of voxelwise correlations between pairs of numbers mirrored the amount of overlap in their tuning curves under the assumption of approximate, analogue coding. In contrast, symbolic numbers showed a flat field of modest correlations more consistent with discrete, categorical representation (no systematic overlap between numbers). Directly correlating activity patterns for a given number across formats (e.g., the numeral 6 with six dots) showed no evidence of shared symbolic and nonsymbolic number-specific representations. Overall (univariate) activity in bilateral IPS was well fit by the log of the number being processed for both nonsymbolic and symbolic numbers. IPS activity is thus sensitive to numerosity regardless of format;however, the nature in which symbolic and nonsymbolic numbers are encoded is fundamentally different. Hum Brain Mapp 36:475-488, 2015. (c) 2014 Wiley Periodicals, Inc.
Marsat G, Maler L. Preparing for the unpredictable: adaptive feedback enhances the response to unexpected communication signals. J Neurophysiol 107: 1241-1246, 2012. First published December 7, 2011;doi:10.1152/jn.009...
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Marsat G, Maler L. Preparing for the unpredictable: adaptive feedback enhances the response to unexpected communication signals. J Neurophysiol 107: 1241-1246, 2012. First published December 7, 2011;doi:10.1152/jn.00982.2011.-To interact with the environment efficiently, the nervous system must generate expectations about redundant sensory signals and detect unexpected ones. neural circuits can, for example, compare a prediction of the sensory signal that was generated by the nervous system with the incoming sensory input, to generate a response selective to novel stimuli. In the first-order electrosensory neurons of a gymnotiform electric fish, a negative image of low-frequency redundant communication signals is subtracted from the neural response via feedback, allowing unpredictable signals to be extracted. Here we show that the cancelling feedback not only suppresses the predictable signal but also actively enhances the response to the unpredictable communication signal. A transient mismatch between the predictive feedback and incoming sensory input causes both to be positive: the soma is suddenly depolarized by the unpredictable input, whereas the neuron's apical dendrites remain depolarized by the lagging cancelling feedback. The apical dendrites allow the backpropagation of somatic spikes. We show that backpropagation is enhanced when the dendrites are depolarized, causing the unpredictable excitatory input to evoke spike bursts. As a consequence, the feedback driven by a predictable low-frequency signal not only suppresses the response to a redundant stimulus but also induces a bursting response triggered by unpredictable communication signals.
In natural listening conditions, many sounds must be detected and identified in the context of competing sound sources, which function as background noise. Traditionally, noise is thought to degrade the cortical repre...
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In natural listening conditions, many sounds must be detected and identified in the context of competing sound sources, which function as background noise. Traditionally, noise is thought to degrade the cortical representation of sounds by suppressing responses and increasing response variability. However, recent studies of neural network models and brain slices have shown that background synaptic noise can improve the detection of signals. Because acoustic noise affects the synaptic background activity of cortical networks, it may improve the cortical responses to signals. We used spike train decoding techniques to determine the functional effects of a continuous white noise background on the responses of clusters of neurons in auditory cortex to foreground signals, specifically frequency-modulated sweeps (FMs) of different velocities, directions, and amplitudes. Whereas the addition of noise progressively suppressed the FM responses of some cortical sites in the core fields with decreasing signal-to-noise ratios (SNRs), the stimulus representation remained robust or was even significantly enhanced at specific SNRs in many others. Even though the background noise level was typically not explicitly encoded in cortical responses, significant information about noise context could be decoded from cortical responses on the basis of how the neural representation of the foreground sweeps was affected. These findings demonstrate significant diversity in signal in noise processing even within the core auditory fields that could support noise-robust hearing across a wide range of listening conditions. NEW & NOTEWORTHY The ability to detect and discriminate sounds in background noise is critical for our ability to communicate. The neural basis of robust perceptual performance in noise is not well understood. We identified neuronal populations in core auditory cortex of squirrel monkeys that differ in how they process foreground signals in background noise and that may contrib
Key points We measured fractal (self-similar) fluctuations in ongoing spiking activity in subcortical (lateral geniculate nucleus, LGN) and cortical (area MT) visual areas in anaesthetised marmosets. Cells in the evol...
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Key points We measured fractal (self-similar) fluctuations in ongoing spiking activity in subcortical (lateral geniculate nucleus, LGN) and cortical (area MT) visual areas in anaesthetised marmosets. Cells in the evolutionary ancient koniocellular LGN pathway and in area MT show high-amplitude fractal fluctuations, whereas evolutionarily newer parvocellular and magnocellular LGN cells do not. Spiking activity in koniocellular cells and MT cells shows substantial correlation to the local population activity, whereas activity in parvocellular and magnocellular cells is less correlated with local activity. We develop a model consisting of a fractal process and a global rate modulation which can reproduce and explain the fundamental relationship between fractal fluctuations and population coupling in LGN and MT. The model provides a unified account of apparently disparate aspects of neural spiking activity and can improve our understanding of information processing in evolutionary ancient and modern visual pathways. The brain represents and processes information through patterns of spiking activity, which is influenced by local and widescale brain circuits as well as intrinsic neural dynamics. Whether these influences have independent or linked effects on spiking activity is, however, not known. Here we measured spiking activity in two visual centres, the lateral geniculate nucleus (LGN) and cortical area MT, in marmoset monkeys. By combining the Fano-factor time curve, power spectral analysis and rescaled range analysis, we reveal inherent fractal fluctuations of spiking activity in LGN and MT. We found that the evolutionary ancient koniocellular (K) pathway in LGN and area MT exhibits strong fractal fluctuations at short (<1 s) time scales. Parvocellular (P) and magnocellular (M) LGN cells show weaker fractal fluctuations at longer (multi-second) time scales. In both LGN and MT, the amplitude and time scale of fractal fluctuations can explain short and long time scale
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