51
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Abstract
Background: The roles of neuromodulation in a neural network, such as in a cortical microcolumn, are still incompletely understood. Neuromodulation influences neural processing by presynaptic and postsynaptic regulation of synaptic efficacy. Neuromodulation also affects ion channels and intrinsic excitability. Methods: Synaptic efficacy modulation is an effective way to rapidly alter network density and topology. We alter network topology and density to measure the effect on spike synchronization. We also operate with differently parameterized neuron models which alter the neuron's intrinsic excitability, i.e., activation function. Results: We find that (a) fast synaptic efficacy modulation influences the amount of correlated spiking in a network. Also, (b) synchronization in a network influences the read-out of intrinsic properties. Highly synchronous input drives neurons, such that differences in intrinsic properties disappear, while asynchronous input lets intrinsic properties determine output behavior. Thus, altering network topology can alter the balance between intrinsically vs. synaptically driven network activity. Conclusion: We conclude that neuromodulation may allow a network to shift between a more synchronized transmission mode and a more asynchronous intrinsic read-out mode. This has significant implications for our understanding of the flexibility of cortical computations.
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Affiliation(s)
- Gabriele Scheler
- Carl Correns Foundation for Mathematical Biology, Mountain View, CA, 94040, USA
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52
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Nougaret S, Genovesio A. Learning the meaning of new stimuli increases the cross-correlated activity of prefrontal neurons. Sci Rep 2018; 8:11680. [PMID: 30076326 PMCID: PMC6076274 DOI: 10.1038/s41598-018-29862-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Accepted: 07/19/2018] [Indexed: 11/09/2022] Open
Abstract
The prefrontal cortex (PF) has a key role in learning rules and generating associations between stimuli and responses also called conditional motor learning. Previous studies in PF have examined conditional motor learning at the single cell level but not the correlation of discharges between neurons at the ensemble level. In the present study, we recorded from two rhesus monkeys in the dorsolateral and the mediolateral parts of the prefrontal cortex to address the role of correlated firing of simultaneously recorded pairs during conditional motor learning. We trained two rhesus monkeys to associate three stimuli with three response targets, such that each stimulus was mapped to only one response. We recorded the neuronal activity of the same neuron pairs during learning of new associations and with already learned associations. In these tasks after a period of fixation, a visual instruction stimulus appeared centrally and three potential response targets appeared in three positions: right, left, and up from center. We found a higher number of neuron pairs significantly correlated and higher cross-correlation coefficients during stimulus presentation in the new than in the familiar mapping task. These results demonstrate that learning affects the PF neural correlation structure.
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Affiliation(s)
- Simon Nougaret
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185, Rome, Italy
| | - Aldo Genovesio
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185, Rome, Italy.
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53
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Abstract
Understanding how cognitive processes affect the responses of sensory neurons may clarify the relationship between neuronal population activity and behavior. However, tools for analyzing neuronal activity have not kept up with technological advances in recording from large neuronal populations. Here, we describe prevalent hypotheses of how cognitive processes affect sensory neurons, driven largely by a model based on the activity of single neurons or pools of neurons as the units of computation. We then use simple simulations to expand this model to a new conceptual framework that focuses on subspaces of population activity as the relevant units of computation, uses comparisons between brain areas or to behavior to guide analyses of these subspaces, and suggests that population activity is optimized to decode the large variety of stimuli and tasks that animals encounter in natural behavior. This framework provides new ways of understanding the ever-growing quantity of recorded population activity data.
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Affiliation(s)
- Douglas A Ruff
- Department of Neuroscience and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA;
| | - Amy M Ni
- Department of Neuroscience and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA;
| | - Marlene R Cohen
- Department of Neuroscience and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA;
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54
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Yao JD, Sanes DH. Developmental deprivation-induced perceptual and cortical processing deficits in awake-behaving animals. eLife 2018; 7:33891. [PMID: 29873632 PMCID: PMC6005681 DOI: 10.7554/elife.33891] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Accepted: 06/04/2018] [Indexed: 01/02/2023] Open
Abstract
Sensory deprivation during development induces lifelong changes to central nervous system function that are associated with perceptual impairments. However, the relationship between neural and behavioral deficits is uncertain due to a lack of simultaneous measurements during task performance. Therefore, we telemetrically recorded from auditory cortex neurons in gerbils reared with developmental conductive hearing loss as they performed an auditory task in which rapid fluctuations in amplitude are detected. These data were compared to a measure of auditory brainstem temporal processing from each animal. We found that developmental HL diminished behavioral performance, but did not alter brainstem temporal processing. However, the simultaneous assessment of neural and behavioral processing revealed that perceptual deficits were associated with a degraded cortical population code that could be explained by greater trial-to-trial response variability. Our findings suggest that the perceptual limitations that attend early hearing loss are best explained by an encoding deficit in auditory cortex.
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Affiliation(s)
- Justin D Yao
- Center for Neural Science, New York University, New York, United States
| | - Dan H Sanes
- Center for Neural Science, New York University, New York, United States.,Department of Psychology, New York University, New York, United States.,Department of Biology, New York University, New York, United States.,Neuroscience Institute, NYU Langone Medical Center, New York, United States
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55
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Downey JE, Schwed N, Chase SM, Schwartz AB, Collinger JL. Intracortical recording stability in human brain–computer interface users. J Neural Eng 2018; 15:046016. [PMID: 29553484 DOI: 10.1088/1741-2552/aab7a0] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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56
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The Magnitude, But Not the Sign, of MT Single-Trial Spike-Time Correlations Predicts Motion Detection Performance. J Neurosci 2018; 38:4399-4417. [PMID: 29626168 DOI: 10.1523/jneurosci.1182-17.2018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 03/23/2018] [Accepted: 03/29/2018] [Indexed: 11/21/2022] Open
Abstract
Spike-time correlations capture the short timescale covariance between the activity of neurons on a single trial. These correlations can significantly vary in magnitude and sign from trial to trial, and have been proposed to contribute to information encoding in visual cortex. While monkeys performed a motion-pulse detection task, we examined the behavioral impact of both the magnitude and sign of single-trial spike-time correlations between two nonoverlapping pools of middle temporal (MT) neurons. We applied three single-trial measures of spike-time correlation between our multiunit MT spike trains (Pearson's, absolute value of Pearson's, and mutual information), and examined the degree to which they predicted a subject's performance on a trial-by-trial basis. We found that on each trial, positive and negative spike-time correlations were almost equally likely, and, once the correlational sign was accounted for, all three measures were similarly predictive of behavior. Importantly, just before the behaviorally relevant motion pulse occurred, single-trial spike-time correlations were as predictive of the performance of the animal as single-trial firing rates. While firing rates were positively associated with behavioral outcomes, the presence of either strong positive or negative correlations had a detrimental effect on behavior. These correlations occurred on short timescales, and the strongest positive and negative correlations modulated behavioral performance by ∼9%, compared with trials with no correlations. We suggest a model where spike-time correlations are associated with a common noise source for the two MT pools, which in turn decreases the signal-to-noise ratio of the integrated signals that drive motion detection.SIGNIFICANCE STATEMENT Previous work has shown that spike-time correlations occurring on short timescales can affect the encoding of visual inputs. Although spike-time correlations significantly vary in both magnitude and sign across trials, their impact on trial-by-trial behavior is not fully understood. Using neural recordings from area MT (middle temporal) in monkeys performing a motion-detection task using a brief stimulus, we found that both positive and negative spike-time correlations predicted behavioral responses as well as firing rate on a trial-by-trial basis. We propose that strong positive and negative spike-time correlations decreased behavioral performance by reducing the signal-to-noise ratio of integrated MT neural signals.
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57
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Dechery JB, MacLean JN. Functional triplet motifs underlie accurate predictions of single-trial responses in populations of tuned and untuned V1 neurons. PLoS Comput Biol 2018; 14:e1006153. [PMID: 29727448 PMCID: PMC5955581 DOI: 10.1371/journal.pcbi.1006153] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 05/16/2018] [Accepted: 04/25/2018] [Indexed: 11/30/2022] Open
Abstract
Visual stimuli evoke activity in visual cortical neuronal populations. Neuronal activity can be selectively modulated by particular visual stimulus parameters, such as the direction of a moving bar of light, resulting in well-defined trial averaged tuning properties. However, given any single stimulus parameter, a large number of neurons in visual cortex remain unmodulated, and the role of this untuned population is not well understood. Here, we use two-photon calcium imaging to record, in an unbiased manner, from large populations of layer 2/3 excitatory neurons in mouse primary visual cortex to describe co-varying activity on single trials in neuronal populations consisting of both tuned and untuned neurons. Specifically, we summarize pairwise covariability with an asymmetric partial correlation coefficient, allowing us to analyze the resultant population correlation structure, or functional network, with graph theory. Using the graph neighbors of a neuron, we find that the local population, including both tuned and untuned neurons, are able to predict individual neuron activity on a moment to moment basis, while also recapitulating tuning properties of tuned neurons. Variance explained in total population activity scales with the number of neurons imaged, demonstrating larger sample sizes are required to fully capture local network interactions. We also find that a specific functional triplet motif in the graph results in the best predictions, suggesting a signature of informative correlations in these populations. In summary, we show that unbiased sampling of the local population can explain single trial response variability as well as trial-averaged tuning properties in V1, and the ability to predict responses is tied to the occurrence of a functional triplet motif.
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Affiliation(s)
- Joseph B. Dechery
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois, United States of America
| | - Jason N. MacLean
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois, United States of America
- Department of Neurobiology, University of Chicago, Chicago, Illinois, United States of America
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58
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Calderini M, Zhang S, Berberian N, Thivierge JP. Optimal Readout of Correlated Neural Activity in a Decision-Making Circuit. Neural Comput 2018; 30:1573-1611. [PMID: 29652584 DOI: 10.1162/neco_a_01083] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
The neural correlates of decision making have been extensively studied with tasks involving a choice between two alternatives that is guided by visual cues. While a large body of work argues for a role of the lateral intraparietal (LIP) region of cortex in these tasks, this role may be confounded by the interaction between LIP and other regions, including medial temporal (MT) cortex. Here, we describe a simplified linear model of decision making that is adapted to two tasks: a motion discrimination and a categorization task. We show that the distinct contribution of MT and LIP may indeed be confounded in these tasks. In particular, we argue that the motion discrimination task relies on a straightforward visuomotor mapping, which leads to redundant information between MT and LIP. The categorization task requires a more complex mapping between visual information and decision behavior, and therefore does not lead to redundancy between MT and LIP. Going further, the model predicts that noise correlations within LIP should be greater in the categorization compared to the motion discrimination task due to the presence of shared inputs from MT. The impact of these correlations on task performance is examined by analytically deriving error estimates of an optimal linear readout for shared and unique inputs. Taken together, results clarify the contribution of MT and LIP to decision making and help characterize the role of noise correlations in these regions.
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Affiliation(s)
- Matias Calderini
- Center for Neural Dynamics and School of Psychology, University of Ottawa, Ontario K1N 6N5, Canada
| | - Sophie Zhang
- Center for Neural Dynamics and School of Psychology, University of Ottawa, Ontario K1N 6N5, Canada
| | - Nareg Berberian
- Center for Neural Dynamics and School of Psychology, University of Ottawa, Ontario K1N 6N5, Canada
| | - Jean-Philippe Thivierge
- Center for Neural Dynamics and School of Psychology, University of Ottawa, Ontario K1N 6N5, Canada
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59
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Bondy AG, Haefner RM, Cumming BG. Feedback determines the structure of correlated variability in primary visual cortex. Nat Neurosci 2018; 21:598-606. [PMID: 29483663 PMCID: PMC5876152 DOI: 10.1038/s41593-018-0089-1] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Accepted: 01/22/2018] [Indexed: 02/07/2023]
Abstract
The variable responses of sensory neurons tend to be weakly correlated (spike-count correlation, rsc). This is widely thought to reflect noise in shared afferents, in which case rsc can limit the reliability of sensory coding. However, it could also be due to feedback from higher-order brain regions. Currently, the relative contributions of these sources are unknown. We addressed this by recording from populations of V1 neurons in macaques performing different discrimination tasks involving the same visual input. We found that the structure of rsc (the way rsc varied with neuronal stimulus preference) changed systematically with task instruction. Therefore, even at the earliest stage in the cortical visual hierarchy, rsc structure during task performance primarily reflects feedback dynamics. Consequently, previous proposals for how rsc constrains sensory processing need not apply. Furthermore, we show that correlations between the activity of single neurons and choice depend on feedback engaged by the task.
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Affiliation(s)
- Adrian G Bondy
- Laboratory of Sensorimotor Research, National Eye Institute, NIH, Bethesda, MD, USA. .,Brown-NIH Neuroscience Graduate Partnership Program, Providence, RI, USA.
| | - Ralf M Haefner
- Brain & Cognitive Sciences, Center for Visual Science, University of Rochester, Rochester, NY, USA
| | - Bruce G Cumming
- Laboratory of Sensorimotor Research, National Eye Institute, NIH, Bethesda, MD, USA
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60
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Smith RJ, Soares AB, Rouse AG, Schieber MH, Thakor NV. Modeling task-specific neuronal ensembles improves decoding of grasp. J Neural Eng 2018; 15:036006. [PMID: 29393065 DOI: 10.1088/1741-2552/aaac93] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Dexterous movement involves the activation and coordination of networks of neuronal populations across multiple cortical regions. Attempts to model firing of individual neurons commonly treat the firing rate as directly modulating with motor behavior. However, motor behavior may additionally be associated with modulations in the activity and functional connectivity of neurons in a broader ensemble. Accounting for variations in neural ensemble connectivity may provide additional information about the behavior being performed. APPROACH In this study, we examined neural ensemble activity in primary motor cortex (M1) and premotor cortex (PM) of two male rhesus monkeys during performance of a center-out reach, grasp and manipulate task. We constructed point process encoding models of neuronal firing that incorporated task-specific variations in the baseline firing rate as well as variations in functional connectivity with the neural ensemble. Models were evaluated both in terms of their encoding capabilities and their ability to properly classify the grasp being performed. MAIN RESULTS Task-specific ensemble models correctly predicted the performed grasp with over 95% accuracy and were shown to outperform models of neuronal activity that assume only a variable baseline firing rate. Task-specific ensemble models exhibited superior decoding performance in 82% of units in both monkeys (p < 0.01). Inclusion of ensemble activity also broadly improved the ability of models to describe observed spiking. Encoding performance of task-specific ensemble models, measured by spike timing predictability, improved upon baseline models in 62% of units. SIGNIFICANCE These results suggest that additional discriminative information about motor behavior found in the variations in functional connectivity of neuronal ensembles located in motor-related cortical regions is relevant to decode complex tasks such as grasping objects, and may serve the basis for more reliable and accurate neural prosthesis.
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Affiliation(s)
- Ryan J Smith
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
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61
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Neuronal Correlations in MT and MST Impair Population Decoding of Opposite Directions of Random Dot Motion. eNeuro 2018; 5:eN-NWR-0336-18. [PMID: 30637327 PMCID: PMC6327941 DOI: 10.1523/eneuro.0336-18.2018] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 10/04/2018] [Accepted: 11/21/2018] [Indexed: 01/20/2023] Open
Abstract
The study of neuronal responses to random-dot motion patterns has provided some of the most valuable insights into how the activity of neurons is related to perception. In the opposite directions of motion paradigm, the motion signal strength is decreased by manipulating the coherence of random dot patterns to examine how well the activity of single neurons represents the direction of motion. To extend this paradigm to populations of neurons, studies have used modelling based on data from pairs of neurons, but several important questions require further investigation with larger neuronal datasets. We recorded neuronal populations in the middle temporal (MT) and medial superior temporal (MST) areas of anaesthetized marmosets with electrode arrays, while varying the coherence of random dot patterns in two opposite directions of motion (left and right). Using the spike rates of simultaneously recorded neurons, we decoded the direction of motion at each level of coherence with linear classifiers. We found that the presence of correlations had a detrimental effect to decoding performance, but that learning the correlation structure produced better decoding performance compared to decoders that ignored the correlation structure. We also found that reducing motion coherence increased neuronal correlations, but decoders did not need to be optimized for each coherence level. Finally, we showed that decoder weights depend of left-right selectivity at 100% coherence, rather than the preferred direction. These results have implications for understanding how the information encoded by populations of neurons is affected by correlations in spiking activity.
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62
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Gutnisky DA, Beaman C, Lew SE, Dragoi V. Cortical response states for enhanced sensory discrimination. eLife 2017; 6:29226. [PMID: 29274146 PMCID: PMC5760207 DOI: 10.7554/elife.29226] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2017] [Accepted: 12/21/2017] [Indexed: 11/20/2022] Open
Abstract
Brain activity during wakefulness is characterized by rapid fluctuations in neuronal responses. Whether these fluctuations play any role in modulating the accuracy of behavioral responses is poorly understood. Here, we investigated whether and how trial changes in the population response impact sensory coding in monkey V1 and perceptual performance. Although the responses of individual neurons varied widely across trials, many cells tended to covary with the local population. When population activity was in a ‘low’ state, neurons had lower evoked responses and correlated variability, yet higher probability to predict perceptual accuracy. The impact of firing rate fluctuations on network and perceptual accuracy was strongest 200 ms before stimulus presentation, and it greatly diminished when the number of cells used to measure the state of the population was decreased. These findings indicate that enhanced perceptual discrimination occurs when population activity is in a ‘silent’ response mode in which neurons increase information extraction.
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Affiliation(s)
- Diego A Gutnisky
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States.,Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Charles Beaman
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
| | - Sergio E Lew
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States.,Instituto de Ingeniería Biomédica, Universidad de Buenos Aires, Argentina, South America
| | - Valentin Dragoi
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
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63
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Anders UM, McLean CS, Ouyang B, Ditterich J. Perceptual Decisions in the Presence of Relevant and Irrelevant Sensory Evidence. Front Neurosci 2017; 11:618. [PMID: 29176941 PMCID: PMC5686122 DOI: 10.3389/fnins.2017.00618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Accepted: 10/23/2017] [Indexed: 11/23/2022] Open
Abstract
Perceptual decisions in the presence of decision-irrelevant sensory information require a selection of decision-relevant sensory evidence. To characterize the mechanism that is responsible for separating decision-relevant from irrelevant sensory information we asked human subjects to make judgments about one of two simultaneously present motion components in a random dot stimulus. Subjects were able to ignore the decision-irrelevant component to a large degree, but their decisions were still influenced by the irrelevant sensory information. Computational modeling revealed that this influence was not simply the consequence of subjects forgetting at times which stimulus component they had been instructed to base their decision on. Instead, residual irrelevant information always seems to be leaking through, and the decision process is captured by a net sensory evidence signal being accumulated to a decision threshold. This net sensory evidence is a linear combination of decision-relevant and irrelevant sensory information. The selection process is therefore well-described by a strong linear gain modulation, which, in our experiment, resulted in the relevant sensory evidence having at least 10 times more impact on the decision than the irrelevant evidence.
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Affiliation(s)
- Ursula M Anders
- Center for Neuroscience and Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA, United States
| | - Charlotte S McLean
- Center for Neuroscience and Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA, United States
| | - Bowen Ouyang
- Center for Neuroscience and Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA, United States
| | - Jochen Ditterich
- Center for Neuroscience and Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA, United States
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64
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Beaman CB, Eagleman SL, Dragoi V. Sensory coding accuracy and perceptual performance are improved during the desynchronized cortical state. Nat Commun 2017; 8:1308. [PMID: 29101393 PMCID: PMC5670198 DOI: 10.1038/s41467-017-01030-4] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Accepted: 08/13/2017] [Indexed: 01/26/2023] Open
Abstract
Cortical activity changes continuously during the course of the day. At a global scale, population activity varies between the ‘synchronized’ state during sleep and ‘desynchronized’ state during waking. However, whether local fluctuations in population synchrony during wakefulness modulate the accuracy of sensory encoding and behavioral performance is poorly understood. Here, we show that populations of cells in monkey visual cortex exhibit rapid fluctuations in synchrony ranging from desynchronized responses, indicative of high alertness, to highly synchronized responses. These fluctuations are local and control the trial variability in population coding accuracy and behavioral performance in a discrimination task. When local population activity is desynchronized, the correlated variability between neurons is reduced, and network and behavioral performance are enhanced. These findings demonstrate that the structure of variability in local cortical populations is not noise but rather controls how sensory information is optimally integrated with ongoing processes to guide network coding and behavior. Changes in synchrony of cortical populations are observed across the sleep-wake cycle, however the effect of fluctuations in synchrony during wakefulness is not understood. Here the authors show that visual cortical neurons have improved sensory encoding accuracy as well as improved perceptual performance during periods of local population desynchrony.
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Affiliation(s)
- Charles B Beaman
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas at Houston, Houston, TX, 77030, USA
| | - Sarah L Eagleman
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas at Houston, Houston, TX, 77030, USA.,Department of Electrical and Computer Engineering, Rice University, George R. Brown School of Engineering, Houston, TX, 77005, USA
| | - Valentin Dragoi
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas at Houston, Houston, TX, 77030, USA.
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65
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Lange RD, Haefner RM. Characterizing and interpreting the influence of internal variables on sensory activity. Curr Opin Neurobiol 2017; 46:84-89. [PMID: 28841439 PMCID: PMC5660641 DOI: 10.1016/j.conb.2017.07.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Revised: 07/03/2017] [Accepted: 07/19/2017] [Indexed: 12/24/2022]
Abstract
The concept of a tuning curve has been central for our understanding of how the responses of cortical neurons depend on external stimuli. Here, we describe how the influence of unobserved internal variables on sensory responses, in particular correlated neural variability, can be understood in a similar framework. We suggest that this will lead to deeper insights into the relationship between stimulus, sensory responses, and behavior. We review related recent work and discuss its implication for distinguishing feedforward from feedback influences on sensory responses, and for the information contained in those responses.
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Affiliation(s)
- Richard D Lange
- Brain & Cognitive Sciences, University of Rochester, Rochester, NY 14627, USA.
| | - Ralf M Haefner
- Brain & Cognitive Sciences, University of Rochester, Rochester, NY 14627, USA.
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66
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Decoupled choice-driven and stimulus-related activity in parietal neurons may be misrepresented by choice probabilities. Nat Commun 2017; 8:715. [PMID: 28959018 PMCID: PMC5620044 DOI: 10.1038/s41467-017-00766-3] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2017] [Accepted: 07/26/2017] [Indexed: 11/09/2022] Open
Abstract
Trial-by-trial correlations between neural responses and choices (choice probabilities) are often interpreted to reflect a causal contribution of neurons to task performance. However, choice probabilities may arise from top-down, rather than bottom-up, signals. We isolated distinct sensory and decision contributions to single-unit activity recorded from the dorsal medial superior temporal (MSTd) and ventral intraparietal (VIP) areas of monkeys during perception of self-motion. Superficially, neurons in both areas show similar tuning curves during task performance. However, tuning in MSTd neurons primarily reflects sensory inputs, whereas choice-related signals dominate tuning in VIP neurons. Importantly, the choice-related activity of VIP neurons is not predictable from their stimulus tuning, and these factors are often confounded in choice probability measurements. This finding was confirmed in a subset of neurons for which stimulus tuning was measured during passive fixation. Our findings reveal decoupled stimulus and choice signals in the VIP area, and challenge our understanding of choice signals in the brain.Choice-related signals in neuronal activity may reflect bottom-up sensory processes, top-down decision-related influences, or a combination of the two. Here the authors report that choice-related activity in VIP neurons is not predictable from their stimulus tuning, and that dominant choice signals can bias the standard metric of choice preference (choice probability).
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67
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Bejjanki VR, da Silveira RA, Cohen JD, Turk-Browne NB. Noise correlations in the human brain and their impact on pattern classification. PLoS Comput Biol 2017; 13:e1005674. [PMID: 28841641 PMCID: PMC5589258 DOI: 10.1371/journal.pcbi.1005674] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2015] [Revised: 09/07/2017] [Accepted: 07/05/2017] [Indexed: 11/27/2022] Open
Abstract
Multivariate decoding methods, such as multivoxel pattern analysis (MVPA), are highly effective at extracting information from brain imaging data. Yet, the precise nature of the information that MVPA draws upon remains controversial. Most current theories emphasize the enhanced sensitivity imparted by aggregating across voxels that have mixed and weak selectivity. However, beyond the selectivity of individual voxels, neural variability is correlated across voxels, and such noise correlations may contribute importantly to accurate decoding. Indeed, a recent computational theory proposed that noise correlations enhance multivariate decoding from heterogeneous neural populations. Here we extend this theory from the scale of neurons to functional magnetic resonance imaging (fMRI) and show that noise correlations between heterogeneous populations of voxels (i.e., voxels selective for different stimulus variables) contribute to the success of MVPA. Specifically, decoding performance is enhanced when voxels with high vs. low noise correlations (measured during rest or in the background of the task) are selected during classifier training. Conversely, voxels that are strongly selective for one class in a GLM or that receive high classification weights in MVPA tend to exhibit high noise correlations with voxels selective for the other class being discriminated against. Furthermore, we use simulations to show that this is a general property of fMRI data and that selectivity and noise correlations can have distinguishable influences on decoding. Taken together, our findings demonstrate that if there is signal in the data, the resulting above-chance classification accuracy is modulated by the magnitude of noise correlations. A central challenge in cognitive neuroscience is decoding mental representations from patterns of brain activity. With functional magnetic resonance imaging (fMRI), multivariate decoding methods like multivoxel pattern analysis (MVPA) have produced numerous discoveries about the brain. However, what information these methods draw upon remains the subject of debate. Typically, each voxel is thought to contribute information through its selectivity (i.e., how differently it responds to the classes being decoded), with improved sensitivity reflecting the aggregation of selectivity across voxels. We show that this interpretation downplays an important factor: MVPA is also highly attuned to noise correlations between voxels with opposite selectivity. Across several analyses of an fMRI dataset, we demonstrate a positive relationship between the magnitude of noise correlations and multivariate decoding performance. Indeed, voxels more selective for one class, or heavily weighted in MVPA, tend to be more strongly correlated with voxels selective for the opposite class. Furthermore, using a model to simulate different levels of selectivity and noise correlations, we find that the benefit of noise correlations for decoding is a general property of fMRI data. These findings help elucidate the computational underpinnings of multivariate decoding in cognitive neuroscience and provide insight into the nature of neural representations.
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Affiliation(s)
- Vikranth R. Bejjanki
- Department of Psychology, Princeton University, Princeton, NJ, United States of America
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States of America
- Department of Psychology, Hamilton College, Clinton, NY, United States of America
- * E-mail:
| | - Rava Azeredo da Silveira
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States of America
- Department of Physics, Ecole Normale Supérieure, Paris, France
- Laboratoire de Physique Statistique, École Normale Supérieure, PSL Research University; Université Paris Diderot Sorbonne Paris-Cité; Sorbonne Universités UPMC Univ Paris 06; CNRS, Paris, France
| | - Jonathan D. Cohen
- Department of Psychology, Princeton University, Princeton, NJ, United States of America
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States of America
| | - Nicholas B. Turk-Browne
- Department of Psychology, Princeton University, Princeton, NJ, United States of America
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States of America
- Department of Psychology, Yale University, New Haven, CT, United States of America
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68
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Decision-Related Activity in Macaque V2 for Fine Disparity Discrimination Is Not Compatible with Optimal Linear Readout. J Neurosci 2017; 37:715-725. [PMID: 28100751 DOI: 10.1523/jneurosci.2445-16.2016] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Revised: 11/20/2016] [Accepted: 11/29/2016] [Indexed: 11/21/2022] Open
Abstract
Fine judgments of stereoscopic depth rely mainly on relative judgments of depth (relative binocular disparity) between objects, rather than judgments of the distance to where the eyes are fixating (absolute disparity). In macaques, visual area V2 is the earliest site in the visual processing hierarchy for which neurons selective for relative disparity have been observed (Thomas et al., 2002). Here, we found that, in macaques trained to perform a fine disparity discrimination task, disparity-selective neurons in V2 were highly selective for the task, and their activity correlated with the animals' perceptual decisions (unexplained by the stimulus). This may partially explain similar correlations reported in downstream areas. Although compatible with a perceptual role of these neurons for the task, the interpretation of such decision-related activity is complicated by the effects of interneuronal "noise" correlations between sensory neurons. Recent work has developed simple predictions to differentiate decoding schemes (Pitkow et al., 2015) without needing measures of noise correlations, and found that data from early sensory areas were compatible with optimal linear readout of populations with information-limiting correlations. In contrast, our data here deviated significantly from these predictions. We additionally tested this prediction for previously reported results of decision-related activity in V2 for a related task, coarse disparity discrimination (Nienborg and Cumming, 2006), thought to rely on absolute disparity. Although these data followed the predicted pattern, they violated the prediction quantitatively. This suggests that optimal linear decoding of sensory signals is not generally a good predictor of behavior in simple perceptual tasks. SIGNIFICANCE STATEMENT Activity in sensory neurons that correlates with an animal's decision is widely believed to provide insights into how the brain uses information from sensory neurons. Recent theoretical work developed simple predictions to differentiate decoding schemes, and found support for optimal linear readout of early sensory populations with information-limiting correlations. Here, we observed decision-related activity for neurons in visual area V2 of macaques performing fine disparity discrimination, as yet the earliest site for this task. These findings, and previously reported results from V2 in a different task, deviated from the predictions for optimal linear readout of a population with information-limiting correlations. Our results suggest that optimal linear decoding of early sensory information is not a general decoding strategy used by the brain.
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69
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Tajima S, Koida K, Tajima CI, Suzuki H, Aihara K, Komatsu H. Task-dependent recurrent dynamics in visual cortex. eLife 2017; 6:e26868. [PMID: 28737487 PMCID: PMC5544435 DOI: 10.7554/elife.26868] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Accepted: 07/10/2017] [Indexed: 11/13/2022] Open
Abstract
The capacity for flexible sensory-action association in animals has been related to context-dependent attractor dynamics outside the sensory cortices. Here, we report a line of evidence that flexibly modulated attractor dynamics during task switching are already present in the higher visual cortex in macaque monkeys. With a nonlinear decoding approach, we can extract the particular aspect of the neural population response that reflects the task-induced emergence of bistable attractor dynamics in a neural population, which could be obscured by standard unsupervised dimensionality reductions such as PCA. The dynamical modulation selectively increases the information relevant to task demands, indicating that such modulation is beneficial for perceptual decisions. A computational model that features nonlinear recurrent interaction among neurons with a task-dependent background input replicates the key properties observed in the experimental data. These results suggest that the context-dependent attractor dynamics involving the sensory cortex can underlie flexible perceptual abilities.
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Affiliation(s)
- Satohiro Tajima
- Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
- JST PRESTO, Japan Science and Technology Agency, Kawaguchi, Japan
| | - Kowa Koida
- EIIRIS, Toyohashi University of Technology, Toyohashi, Japan
| | - Chihiro I Tajima
- Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan
| | - Hideyuki Suzuki
- Department of Information and Physical Sciences, Graduate School of Information Science and Technology, Osaka University, Suita, Japan
| | - Kazuyuki Aihara
- Institute of Industrial Science, University of Tokyo, Tokyo, Japan
- National Institute for Physiological Sciences, Okazaki, Japan
| | - Hidehiko Komatsu
- National Institute for Physiological Sciences, Okazaki, Japan
- Brain Science Institute, Tamagawa University, Machida, Japan
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70
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Schledde B, Galashan FO, Przybyla M, Kreiter AK, Wegener D. Task-specific, dimension-based attentional shaping of motion processing in monkey area MT. J Neurophysiol 2017; 118:1542-1555. [PMID: 28659459 DOI: 10.1152/jn.00183.2017] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Revised: 06/15/2017] [Accepted: 06/15/2017] [Indexed: 11/22/2022] Open
Abstract
Nonspatially selective attention is based on the notion that specific features or objects in the visual environment are effectively prioritized in cortical visual processing. Feature-based attention (FBA), in particular, is a well-studied process that dynamically and selectively addresses neurons preferentially processing the attended feature attribute (e.g., leftward motion). In everyday life, however, behavior may require high sensitivity for an entire feature dimension (e.g., motion), but experimental evidence for a feature dimension-specific attentional modulation on a cellular level is lacking. Therefore, we investigated neuronal activity in macaque motion-selective mediotemporal area (MT) in an experimental setting requiring the monkeys to detect either a motion change or a color change. We hypothesized that neural activity in MT is enhanced when the task requires perceptual sensitivity to motion. In line with this, we found that mean firing rates were higher in the motion task and that response variability and latency were lower compared with values in the color task, despite identical visual stimulation. This task-specific, dimension-based modulation of motion processing emerged already in the absence of visual input, was independent of the relation between the attended and stimulating motion direction, and was accompanied by a spatially global reduction of neuronal variability. The results provide single-cell support for the hypothesis of a feature dimension-specific top-down signal emphasizing the processing of an entire feature class.NEW & NOTEWORTHY Cortical processing serving visual perception prioritizes information according to current task requirements. We provide evidence in favor of a dimension-based attentional mechanism addressing all neurons that process visual information in the task-relevant feature domain. Behavioral tasks required monkeys to attend either color or motion, causing modulations of response strength, variability, latency, and baseline activity of motion-selective monkey area MT neurons irrespective of the attended motion direction but specific to the attended feature dimension.
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Affiliation(s)
- Bastian Schledde
- Brain Research Institute, Center for Cognitive Sciences, University of Bremen, Bremen, Germany
| | - F Orlando Galashan
- Brain Research Institute, Center for Cognitive Sciences, University of Bremen, Bremen, Germany
| | - Magdalena Przybyla
- Brain Research Institute, Center for Cognitive Sciences, University of Bremen, Bremen, Germany
| | - Andreas K Kreiter
- Brain Research Institute, Center for Cognitive Sciences, University of Bremen, Bremen, Germany
| | - Detlef Wegener
- Brain Research Institute, Center for Cognitive Sciences, University of Bremen, Bremen, Germany
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71
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Pitkow X, Angelaki DE. Inference in the Brain: Statistics Flowing in Redundant Population Codes. Neuron 2017; 94:943-953. [PMID: 28595050 PMCID: PMC5543692 DOI: 10.1016/j.neuron.2017.05.028] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Revised: 05/10/2017] [Accepted: 05/19/2017] [Indexed: 12/25/2022]
Abstract
It is widely believed that the brain performs approximate probabilistic inference to estimate causal variables in the world from ambiguous sensory data. To understand these computations, we need to analyze how information is represented and transformed by the actions of nonlinear recurrent neural networks. We propose that these probabilistic computations function by a message-passing algorithm operating at the level of redundant neural populations. To explain this framework, we review its underlying concepts, including graphical models, sufficient statistics, and message-passing, and then describe how these concepts could be implemented by recurrently connected probabilistic population codes. The relevant information flow in these networks will be most interpretable at the population level, particularly for redundant neural codes. We therefore outline a general approach to identify the essential features of a neural message-passing algorithm. Finally, we argue that to reveal the most important aspects of these neural computations, we must study large-scale activity patterns during moderately complex, naturalistic behaviors.
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Affiliation(s)
- Xaq Pitkow
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA.
| | - Dora E Angelaki
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
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72
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Vahabie AH, Dehaqani MRA, Ahmadabadi MN, Araabi BN, Esteky H. Rapid face adaptation distributes representation in inferior-temporal cortex across time and neuronal dimensions. Sci Rep 2017; 7:1709. [PMID: 28490773 PMCID: PMC5431916 DOI: 10.1038/s41598-017-01864-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Accepted: 04/04/2017] [Indexed: 11/19/2022] Open
Abstract
Neuronal networks of the brain adapt their information processing according to the history of stimuli. Whereas most studies have linked adaptation to repetition suppression, recurrent connections within a network and disinhibition due to adaptation predict more complex response patterns. The main questions of this study are as follows: what is the effect of the selectivity of neurons on suppression/enhancement of neural responses? What are the consequences of adaptation on information representation in neural population and the temporal structure of response patterns? We studied rapid face adaptation using spiking activities of neurons in the inferior-temporal (IT) cortex. Investigating the responses of neurons, within a wide range from negative to positive face selectivity, showed that despite the peak amplitude suppression in highly positive selective neurons, responses were enhanced in most other neurons. This enhancement can be attributed to disinhibition due to adaptation. Delayed and distributed responses were observed for positive selective neurons. Principal component analysis of the IT population responses over time revealed that repetition of face stimuli resulted in temporal decorrelation of the network activity. The contributions of the main and higher neuronal dimensions were changed under an adaptation condition, where more neuronal dimensions were used to encode repeated face stimuli.
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Affiliation(s)
- Abdol-Hossein Vahabie
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences, P.O. Box 19395-5746, Tehran, Iran.
| | - Mohammad-Reza A Dehaqani
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences, P.O. Box 19395-5746, Tehran, Iran
| | - Majid Nili Ahmadabadi
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences, P.O. Box 19395-5746, Tehran, Iran.,Cognitive Systems Lab, Control and Intelligent Processing Centre of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, P.O. Box 14395-515, Tehran, Iran
| | - Babak Nadjar Araabi
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences, P.O. Box 19395-5746, Tehran, Iran.,Cognitive Systems Lab, Control and Intelligent Processing Centre of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, P.O. Box 14395-515, Tehran, Iran
| | - Hossein Esteky
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences, P.O. Box 19395-5746, Tehran, Iran.,Research Center for Brain and Cognitive Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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73
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Downer JD, Niwa M, Sutter ML. Hierarchical differences in population coding within auditory cortex. J Neurophysiol 2017; 118:717-731. [PMID: 28446588 PMCID: PMC5539454 DOI: 10.1152/jn.00899.2016] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Revised: 04/21/2017] [Accepted: 04/21/2017] [Indexed: 01/04/2023] Open
Abstract
Most models of auditory cortical (AC) population coding have focused on primary auditory cortex (A1). Thus our understanding of how neural coding for sounds progresses along the cortical hierarchy remains obscure. To illuminate this, we recorded from two AC fields: A1 and middle lateral belt (ML) of rhesus macaques. We presented amplitude-modulated (AM) noise during both passive listening and while the animals performed an AM detection task ("active" condition). In both fields, neurons exhibit monotonic AM-depth tuning, with A1 neurons mostly exhibiting increasing rate-depth functions and ML neurons approximately evenly distributed between increasing and decreasing functions. We measured noise correlation (rnoise) between simultaneously recorded neurons and found that whereas engagement decreased average rnoise in A1, engagement increased average rnoise in ML. This finding surprised us, because attentive states are commonly reported to decrease average rnoise We analyzed the effect of rnoise on AM coding in both A1 and ML and found that whereas engagement-related shifts in rnoise in A1 enhance AM coding, rnoise shifts in ML have little effect. These results imply that the effect of rnoise differs between sensory areas, based on the distribution of tuning properties among the neurons within each population. A possible explanation of this is that higher areas need to encode nonsensory variables (e.g., attention, choice, and motor preparation), which impart common noise, thus increasing rnoise Therefore, the hierarchical emergence of rnoise-robust population coding (e.g., as we observed in ML) enhances the ability of sensory cortex to integrate cognitive and sensory information without a loss of sensory fidelity.NEW & NOTEWORTHY Prevailing models of population coding of sensory information are based on a limited subset of neural structures. An important and under-explored question in neuroscience is how distinct areas of sensory cortex differ in their population coding strategies. In this study, we compared population coding between primary and secondary auditory cortex. Our findings demonstrate striking differences between the two areas and highlight the importance of considering the diversity of neural structures as we develop models of population coding.
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Affiliation(s)
- Joshua D Downer
- Center for Neuroscience and Department of Neurobiology, Physiology and Behavior, University of California, Davis, California
| | - Mamiko Niwa
- Center for Neuroscience and Department of Neurobiology, Physiology and Behavior, University of California, Davis, California
| | - Mitchell L Sutter
- Center for Neuroscience and Department of Neurobiology, Physiology and Behavior, University of California, Davis, California
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74
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Dissociation of Choice Formation and Choice-Correlated Activity in Macaque Visual Cortex. J Neurosci 2017; 37:5195-5203. [PMID: 28432137 DOI: 10.1523/jneurosci.3331-16.2017] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2016] [Revised: 03/23/2017] [Accepted: 03/27/2017] [Indexed: 11/21/2022] Open
Abstract
Responses of individual task-relevant sensory neurons can predict monkeys' trial-by-trial choices in perceptual decision-making tasks. Choice-correlated activity has been interpreted as evidence that the responses of these neurons are causally linked to perceptual judgments. To further test this hypothesis, we studied responses of orientation-selective neurons in V1 and V2 while two macaque monkeys performed a fine orientation discrimination task. Although both animals exhibited a high level of neuronal and behavioral sensitivity, only one exhibited choice-correlated activity. Surprisingly, this correlation was negative: when a neuron fired more vigorously, the animal was less likely to choose the orientation preferred by that neuron. Moreover, choice-correlated activity emerged late in the trial, earlier in V2 than in V1, and was correlated with anticipatory signals. Together, these results suggest that choice-correlated activity in task-relevant sensory neurons can reflect postdecision modulatory signals.SIGNIFICANCE STATEMENT When observers perform a difficult sensory discrimination, repeated presentations of the same stimulus can elicit different perceptual judgments. This behavioral variability often correlates with variability in the activity of sensory neurons driven by the stimulus. Traditionally, this correlation has been interpreted as suggesting a causal link between the activity of sensory neurons and perceptual judgments. More recently, it has been argued that the correlation instead may originate in recurrent input from other brain areas involved in the interpretation of sensory signals. Here, we call both hypotheses into question. We show that choice-related activity in sensory neurons can be highly variable across observers and can reflect modulatory processes that are dissociated from perceptual decision-making.
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75
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Karimi-Rouzbahani H, Bagheri N, Ebrahimpour R. Average activity, but not variability, is the dominant factor in the representation of object categories in the brain. Neuroscience 2017; 346:14-28. [DOI: 10.1016/j.neuroscience.2017.01.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Revised: 12/18/2016] [Accepted: 01/02/2017] [Indexed: 11/27/2022]
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76
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van den Berg R, Zylberberg A, Kiani R, Shadlen MN, Wolpert DM. Confidence Is the Bridge between Multi-stage Decisions. Curr Biol 2016; 26:3157-3168. [PMID: 27866891 PMCID: PMC5154755 DOI: 10.1016/j.cub.2016.10.021] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Revised: 09/18/2016] [Accepted: 10/12/2016] [Indexed: 11/30/2022]
Abstract
Demanding tasks often require a series of decisions to reach a goal. Recent progress in perceptual decision-making has served to unite decision accuracy, speed, and confidence in a common framework of bounded evidence accumulation, furnishing a platform for the study of such multi-stage decisions. In many instances, the strategy applied to each decision, such as the speed-accuracy trade-off, ought to depend on the accuracy of the previous decisions. However, as the accuracy of each decision is often unknown to the decision maker, we hypothesized that subjects may carry forward a level of confidence in previous decisions to affect subsequent decisions. Subjects made two perceptual decisions sequentially and were rewarded only if they made both correctly. The speed and accuracy of individual decisions were explained by noisy evidence accumulation to a terminating bound. We found that subjects adjusted their speed-accuracy setting by elevating the termination bound on the second decision in proportion to their confidence in the first. The findings reveal a novel role for confidence and a degree of flexibility, hitherto unknown, in the brain's ability to rapidly and precisely modify the mechanisms that control the termination of a decision.
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Affiliation(s)
- Ronald van den Berg
- Computational and Biological Learning Laboratory, Department of Engineering, Cambridge University, Cambridge CB2 1PZ, UK
| | - Ariel Zylberberg
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Kavli Institute of Brain Science, and Howard Hughes Medical Institute, Columbia University, New York, NY 10032, USA
| | - Roozbeh Kiani
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - Michael N Shadlen
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Kavli Institute of Brain Science, and Howard Hughes Medical Institute, Columbia University, New York, NY 10032, USA
| | - Daniel M Wolpert
- Computational and Biological Learning Laboratory, Department of Engineering, Cambridge University, Cambridge CB2 1PZ, UK.
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77
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Engel TA, Steinmetz NA, Gieselmann MA, Thiele A, Moore T, Boahen K. Selective modulation of cortical state during spatial attention. Science 2016; 354:1140-1144. [PMID: 27934763 DOI: 10.1126/science.aag1420] [Citation(s) in RCA: 122] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Accepted: 10/31/2016] [Indexed: 12/15/2022]
Abstract
Neocortical activity is permeated with endogenously generated fluctuations, but how these dynamics affect goal-directed behavior remains a mystery. We found that ensemble neural activity in primate visual cortex spontaneously fluctuated between phases of vigorous (On) and faint (Off) spiking synchronously across cortical layers. These On-Off dynamics, reflecting global changes in cortical state, were also modulated at a local scale during selective attention. Moreover, the momentary phase of local ensemble activity predicted behavioral performance. Our results show that cortical state is controlled locally within a cortical map according to cognitive demands and reveal the impact of these local changes in cortical state on goal-directed behavior.
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Affiliation(s)
- Tatiana A Engel
- Departments of Bioengineering and Electrical Engineering, Stanford University, Stanford, CA, USA. .,Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | | | - Marc A Gieselmann
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK
| | - Alexander Thiele
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK
| | - Tirin Moore
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.,Department of Neurobiology, Stanford University, Stanford, CA, USA
| | - Kwabena Boahen
- Departments of Bioengineering and Electrical Engineering, Stanford University, Stanford, CA, USA
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78
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Berberian N, MacPherson A, Giraud E, Richardson L, Thivierge JP. Neuronal pattern separation of motion-relevant input in LIP activity. J Neurophysiol 2016; 117:738-755. [PMID: 27881719 DOI: 10.1152/jn.00145.2016] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Accepted: 11/18/2016] [Indexed: 11/22/2022] Open
Abstract
In various regions of the brain, neurons discriminate sensory stimuli by decreasing the similarity between ambiguous input patterns. Here, we examine whether this process of pattern separation may drive the rapid discrimination of visual motion stimuli in the lateral intraparietal area (LIP). Starting with a simple mean-rate population model that captures neuronal activity in LIP, we show that overlapping input patterns can be reformatted dynamically to give rise to separated patterns of neuronal activity. The population model predicts that a key ingredient of pattern separation is the presence of heterogeneity in the response of individual units. Furthermore, the model proposes that pattern separation relies on heterogeneity in the temporal dynamics of neural activity and not merely in the mean firing rates of individual neurons over time. We confirm these predictions in recordings of macaque LIP neurons and show that the accuracy of pattern separation is a strong predictor of behavioral performance. Overall, results propose that LIP relies on neuronal pattern separation to facilitate decision-relevant discrimination of sensory stimuli.NEW & NOTEWORTHY A new hypothesis is proposed on the role of the lateral intraparietal (LIP) region of cortex during rapid decision making. This hypothesis suggests that LIP alters the representation of ambiguous inputs to reduce their overlap, thus improving sensory discrimination. A combination of computational modeling, theoretical analysis, and electrophysiological data shows that the pattern separation hypothesis links neural activity to behavior and offers novel predictions on the role of LIP during sensory discrimination.
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Affiliation(s)
- Nareg Berberian
- Center for Neural Dynamics and School of Psychology, University of Ottawa, Ottawa, Ontario, Canada; and
| | - Amanda MacPherson
- Department of Neuroscience, McGill University, Montréal, Québec, Canada
| | - Eloïse Giraud
- Center for Neural Dynamics and School of Psychology, University of Ottawa, Ottawa, Ontario, Canada; and
| | - Lydia Richardson
- Center for Neural Dynamics and School of Psychology, University of Ottawa, Ottawa, Ontario, Canada; and
| | - J-P Thivierge
- Center for Neural Dynamics and School of Psychology, University of Ottawa, Ottawa, Ontario, Canada; and
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79
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Neural correlates of working memory development in adolescent primates. Nat Commun 2016; 7:13423. [PMID: 27827365 PMCID: PMC5105196 DOI: 10.1038/ncomms13423] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2015] [Accepted: 10/03/2016] [Indexed: 12/05/2022] Open
Abstract
Working memory ability matures after puberty, in parallel with structural changes in the prefrontal cortex, but little is known about how changes in prefrontal neuronal activity mediate this cognitive improvement in primates. To address this issue, we compare behavioural performance and neurophysiological activity in monkeys as they transitioned from puberty into adulthood. Here we report that monkeys perform working memory tasks reliably during puberty and show modest improvement in adulthood. The adult prefrontal cortex is characterized by increased activity during the delay period of the task but no change in the representation of stimuli. Activity evoked by distracting stimuli also decreases in the adult prefrontal cortex. The increase in delay period activity relative to the baseline activity of prefrontal neurons is the best correlate of maturation and is not merely a consequence of improved performance. Our results reveal neural correlates of the working memory improvement typical of primate adolescence. Working memory is known to improve through adolescence into adulthood, yet the associated changes in neuronal activity are not well understood. Zhou and colleagues report increased delay period activity correlated with changes in performance on working memory tasks in monkeys as they transition into adulthood.
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80
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Kimura R, Saiki A, Fujiwara-Tsukamoto Y, Sakai Y, Isomura Y. Large-scale analysis reveals populational contributions of cortical spike rate and synchrony to behavioural functions. J Physiol 2016; 595:385-413. [PMID: 27488936 DOI: 10.1113/jp272794] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Accepted: 08/01/2016] [Indexed: 11/08/2022] Open
Abstract
KEY POINTS There have been few systematic population-wide analyses of relationships between spike synchrony within a period of several milliseconds and behavioural functions. In this study, we obtained a large amount of spike data from > 23,000 neuron pairs by multiple single-unit recording from deep layer neurons in motor cortical areas in rats performing a forelimb movement task. The temporal changes of spike synchrony in the whole neuron pairs were statistically independent of behavioural changes during the task performance, although some neuron pairs exhibited correlated changes in spike synchrony. Mutual information analyses revealed that spike synchrony made a smaller contribution than spike rate to behavioural functions. The strength of spike synchrony between two neurons was statistically independent of the spike rate-based preferences of the pair for behavioural functions. ABSTRACT Spike synchrony within a period of several milliseconds in presynaptic neurons enables effective integration of functional information in the postsynaptic neuron. However, few studies have systematically analysed the population-wide relationships between spike synchrony and behavioural functions. Here we obtained a sufficiently large amount of spike data among regular-spiking (putatively excitatory) and fast-spiking (putatively inhibitory) neuron subtypes (> 23,000 pairs) by multiple single-unit recording from deep layers in motor cortical areas (caudal forelimb area, rostral forelimb area) in rats performing a forelimb movement task. After holding a lever, rats pulled the lever either in response to a cue tone (external-trigger trials) or spontaneously without any cue (internal-trigger trials). Many neurons exhibited functional spike activity in association with forelimb movements, and the preference of regular-spiking neurons in the rostral forelimb area was more biased toward externally triggered movement than that in the caudal forelimb area. We found that a population of neuron pairs with spike synchrony does exist, and that some neuron pairs exhibit a dependence on movement phase during task performance. However, the population-wide analysis revealed that spike synchrony was statistically independent of the movement phase and the spike rate-based preferences of the pair for behavioural functions, whereas spike rates were clearly dependent on the movement phase. In fact, mutual information analyses revealed that the contribution of spike synchrony to the behavioural functions was small relative to the contribution of spike rate. Our large-scale analysis revealed that cortical spike rate, rather than spike synchrony, contributes to population coding for movement.
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Affiliation(s)
- Rie Kimura
- Brain Science Institute, Tamagawa University, Tokyo, Japan.,JST CREST, Tokyo, Japan.,Division of Visual Information Processing, National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki, Japan.,Department of Physiological Sciences, SOKENDAI (The Graduate University for Advanced Studies), Okazaki, Japan
| | - Akiko Saiki
- Brain Science Institute, Tamagawa University, Tokyo, Japan.,JST CREST, Tokyo, Japan
| | - Yoko Fujiwara-Tsukamoto
- Brain Science Institute, Tamagawa University, Tokyo, Japan.,JST CREST, Tokyo, Japan.,Laboratory of Neural Circuitry, Graduate School of Brain Science, Doshisha University, Kyoto, Japan.,Present address: Faculty of Human Life Studies, Department of Food and Nutrition, Hagoromo University of International Studies, Osaka, Japan
| | - Yutaka Sakai
- Brain Science Institute, Tamagawa University, Tokyo, Japan.,JST CREST, Tokyo, Japan
| | - Yoshikazu Isomura
- Brain Science Institute, Tamagawa University, Tokyo, Japan.,JST CREST, Tokyo, Japan
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81
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Churchland AK, Kiani R. Three challenges for connecting model to mechanism in decision-making. Curr Opin Behav Sci 2016; 11:74-80. [PMID: 27403450 DOI: 10.1016/j.cobeha.2016.06.008] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Recent years have seen a growing interest in understanding the neural mechanisms that support decision-making. The advent of new tools for measuring and manipulating neurons, alongside the inclusion of multiple new animal models and sensory systems has led to the generation of many novel datasets. The potential for these new approaches to constrain decision-making models is unprecedented. Here, we argue that to fully leverage these new approaches, three challenges must be met. First, experimenters must design well-controlled behavioral experiments that make it possible to distinguish competing behavioral strategies. Second, analyses of neural responses should think beyond single neurons, taking into account tradeoffs of single-trial versus trial-averaged approaches. Finally, quantitative model comparisons should be used, but must consider common obstacles.
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Affiliation(s)
| | - R Kiani
- Center for Neural Science, New York University, New York University
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82
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Rapid Adaptation Induces Persistent Biases in Population Codes for Visual Motion. J Neurosci 2016; 36:4579-90. [PMID: 27098699 DOI: 10.1523/jneurosci.4563-15.2016] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Accepted: 03/13/2016] [Indexed: 11/21/2022] Open
Abstract
UNLABELLED Each visual experience changes the neural response to subsequent stimuli. If the brain is unable to incorporate these encoding changes, the decoding, or perception, of subsequent stimuli is biased. Although the phenomenon of adaptation pervades the nervous system, its effects have been studied mainly in isolation, based on neuronal encoding changes induced by an isolated, prolonged stimulus. To understand how adaptation-induced biases arise and persist under continuous, naturalistic stimulation, we simultaneously recorded the responses of up to 61 neurons in the marmoset (Callithrix jacchus) middle temporal area to a sequence of directions that changed every 500 ms. We found that direction-specific adaptation following only 0.5 s of stimulation strongly affected encoding for up to 2 s by reducing both the gain and the spike count correlations between pairs of neurons with preferred directions close to the adapting direction. In addition, smaller changes in bandwidth and preferred direction were observed in some animals. Decoding individual trials of adaptation-affected activity in simultaneously recorded neurons predicted repulsive biases that are consistent with the direction aftereffect. Surprisingly, removing spike count correlations by trial shuffling did not impact decoding performance or bias. When adaptation had the largest effect on encoding, the decoder made the most errors. This suggests that neural and perceptual repulsion is not a mechanism to enhance perceptual performance but is instead a necessary consequence of optimizing neural encoding for the identification of a wide range of stimulus properties in diverse temporal contexts. SIGNIFICANCE STATEMENT Although perception depends upon decoding the pattern of activity across a neuronal population, the encoding properties of individual neurons are unreliable: a single neuron's response to repetitions of the same stimulus is variable, and depends on both its spatial and temporal context. In this manuscript, we describe the complete cascade of adaptation-induced effects in sensory encoding and show how they predict population decoding errors consistent with perceptual biases. We measure the time course of adaptation-induced changes to the response properties of neurons in isolation, and to the correlation structure across pairs of simultaneously recorded neurons. These results provide novel insight into how and for how long adaptation affects the neural code, particularly during continuous, naturalistic vision.
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83
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Inferring Cortical Variability from Local Field Potentials. J Neurosci 2016; 36:4121-35. [PMID: 27053217 DOI: 10.1523/jneurosci.2502-15.2016] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Accepted: 02/22/2016] [Indexed: 01/02/2023] Open
Abstract
UNLABELLED The responses of sensory neurons can be quite different to repeated presentations of the same stimulus. Here, we demonstrate a direct link between the trial-to-trial variability of cortical neuron responses and network activity that is reflected in local field potentials (LFPs). Spikes and LFPs were recorded with a multielectrode array from the middle temporal (MT) area of the visual cortex of macaques during the presentation of continuous optic flow stimuli. A maximum likelihood-based modeling framework was used to predict single-neuron spiking responses using the stimulus, the LFPs, and the activity of other recorded neurons. MT neuron responses were strongly linked to gamma oscillations (maximum at 40 Hz) as well as to lower-frequency delta oscillations (1-4 Hz), with consistent phase preferences across neurons. The predicted modulation associated with the LFP was largely complementary to that driven by visual stimulation, as well as the activity of other neurons, and accounted for nearly half of the trial-to-trial variability in the spiking responses. Moreover, the LFP model predictions accurately captured the temporal structure of noise correlations between pairs of simultaneously recorded neurons, and explained the variation in correlation magnitudes observed across the population. These results therefore identify signatures of network activity related to the variability of cortical neuron responses, and suggest their central role in sensory cortical function. SIGNIFICANCE STATEMENT The function of sensory neurons is nearly always cast in terms of representing sensory stimuli. However, recordings from visual cortex in awake animals show that a large fraction of neural activity is not predictable from the stimulus. We show that this variability is predictable given the simultaneously recorded measures of network activity, local field potentials. A model that combines elements of these signals with the stimulus processing of the neuron can predict neural responses dramatically better than current models, and can predict the structure of correlations across the cortical population. In identifying ways to understand stimulus processing in the context of ongoing network activity, this work thus provides a foundation to understand the role of sensory cortex in combining sensory and cognitive variables.
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84
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Abstract
Neurons in early visual cortical areas not only represent incoming visual information but are also engaged by higher level cognitive processes, including attention, working memory, imagery, and decision-making. Are these cognitive effects an epiphenomenon or are they functionally relevant for these mental operations? We review evidence supporting the hypothesis that the modulation of activity in early visual areas has a causal role in cognition. The modulatory influences allow the early visual cortex to act as a multiscale cognitive blackboard for read and write operations by higher visual areas, which can thereby efficiently exchange information. This blackboard architecture explains how the activity of neurons in the early visual cortex contributes to scene segmentation and working memory, and relates to the subject's inferences about the visual world. The architecture also has distinct advantages for the processing of visual routines that rely on a number of sequentially executed processing steps.
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Affiliation(s)
- Pieter R Roelfsema
- Netherlands Institute for Neuroscience, 1105 BA Amsterdam, The Netherlands; .,Department of Integrative Neurophysiology, VU University Amsterdam, 1081 HV Amsterdam, The Netherlands.,Psychiatry Department, Academic Medical Center, 1105 AZ Amsterdam, The Netherlands
| | - Floris P de Lange
- Donders Institute for Brain, Cognition and Behavior, Radboud University, 6525 EN Nijmegen, The Netherlands
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85
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Doiron B, Litwin-Kumar A, Rosenbaum R, Ocker GK, Josić K. The mechanics of state-dependent neural correlations. Nat Neurosci 2016; 19:383-93. [PMID: 26906505 DOI: 10.1038/nn.4242] [Citation(s) in RCA: 173] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2015] [Accepted: 01/12/2016] [Indexed: 12/12/2022]
Abstract
Simultaneous recordings from large neural populations are becoming increasingly common. An important feature of population activity is the trial-to-trial correlated fluctuation of spike train outputs from recorded neuron pairs. Similar to the firing rate of single neurons, correlated activity can be modulated by a number of factors, from changes in arousal and attentional state to learning and task engagement. However, the physiological mechanisms that underlie these changes are not fully understood. We review recent theoretical results that identify three separate mechanisms that modulate spike train correlations: changes in input correlations, internal fluctuations and the transfer function of single neurons. We first examine these mechanisms in feedforward pathways and then show how the same approach can explain the modulation of correlations in recurrent networks. Such mechanistic constraints on the modulation of population activity will be important in statistical analyses of high-dimensional neural data.
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Affiliation(s)
- Brent Doiron
- Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, USA
| | - Ashok Litwin-Kumar
- Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, USA.,Center for Theoretical Neuroscience, Columbia University, New York, New York, USA
| | - Robert Rosenbaum
- Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, USA.,Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, USA.,Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, Indiana, USA
| | - Gabriel K Ocker
- Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, USA.,Allen Institute for Brain Science, Seattle, Washington, USA
| | - Krešimir Josić
- Department of Mathematics, University of Houston, Houston, Texas, USA.,Department of Biology and Biochemistry, University of Houston, Houston, Texas, USA
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86
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Sabri MM, Adibi M, Arabzadeh E. Dynamics of Population Activity in Rat Sensory Cortex: Network Correlations Predict Anatomical Arrangement and Information Content. Front Neural Circuits 2016; 10:49. [PMID: 27458347 PMCID: PMC4933716 DOI: 10.3389/fncir.2016.00049] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2016] [Accepted: 06/22/2016] [Indexed: 12/14/2022] Open
Abstract
To study the spatiotemporal dynamics of neural activity in a cortical population, we implanted a 10 × 10 microelectrode array in the vibrissal cortex of urethane-anesthetized rats. We recorded spontaneous neuronal activity as well as activity evoked in response to sustained and brief sensory stimulation. To quantify the temporal dynamics of activity, we computed the probability distribution function (PDF) of spiking on one electrode given the observation of a spike on another. The spike-triggered PDFs quantified the strength, temporal delay, and temporal precision of correlated activity across electrodes. Nearby cells showed higher levels of correlation at short delays, whereas distant cells showed lower levels of correlation, which tended to occur at longer delays. We found that functional space built based on the strength of pairwise correlations predicted the anatomical arrangement of electrodes. Moreover, the correlation profile of electrode pairs during spontaneous activity predicted the "signal" and "noise" correlations during sensory stimulation. Finally, mutual information analyses revealed that neurons with stronger correlations to the network during spontaneous activity, conveyed higher information about the sensory stimuli in their evoked response. Given the 400-μm-distance between adjacent electrodes, our functional quantifications unravel the spatiotemporal dynamics of activity among nearby and distant cortical columns.
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Affiliation(s)
- Mohammad Mahdi Sabri
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM)Tehran, Iran; Eccles Institute of Neuroscience, John Curtin School of Medical Research, The Australian National UniversityCanberra, ACT, Australia; Australian Research Council Centre of Excellence for Integrative Brain Function, The Australian National University NodeCanberra, ACT, Australia
| | - Mehdi Adibi
- Eccles Institute of Neuroscience, John Curtin School of Medical Research, The Australian National UniversityCanberra, ACT, Australia; Australian Research Council Centre of Excellence for Integrative Brain Function, The Australian National University NodeCanberra, ACT, Australia; School of Psychology, University of New South WalesSydney, NSW, Australia
| | - Ehsan Arabzadeh
- Eccles Institute of Neuroscience, John Curtin School of Medical Research, The Australian National UniversityCanberra, ACT, Australia; Australian Research Council Centre of Excellence for Integrative Brain Function, The Australian National University NodeCanberra, ACT, Australia
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87
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Structures of Neural Correlation and How They Favor Coding. Neuron 2016; 89:409-22. [PMID: 26796692 DOI: 10.1016/j.neuron.2015.12.037] [Citation(s) in RCA: 97] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2014] [Revised: 08/15/2015] [Accepted: 12/21/2015] [Indexed: 01/11/2023]
Abstract
The neural representation of information suffers from "noise"-the trial-to-trial variability in the response of neurons. The impact of correlated noise upon population coding has been debated, but a direct connection between theory and experiment remains tenuous. Here, we substantiate this connection and propose a refined theoretical picture. Using simultaneous recordings from a population of direction-selective retinal ganglion cells, we demonstrate that coding benefits from noise correlations. The effect is appreciable already in small populations, yet it is a collective phenomenon. Furthermore, the stimulus-dependent structure of correlation is key. We develop simple functional models that capture the stimulus-dependent statistics. We then use them to quantify the performance of population coding, which depends upon interplays of feature sensitivities and noise correlations in the population. Because favorable structures of correlation emerge robustly in circuits with noisy, nonlinear elements, they will arise and benefit coding beyond the confines of retina.
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88
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Liu LD, Haefner RM, Pack CC. A neural basis for the spatial suppression of visual motion perception. eLife 2016; 5. [PMID: 27228283 PMCID: PMC4882155 DOI: 10.7554/elife.16167] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2016] [Accepted: 05/08/2016] [Indexed: 11/30/2022] Open
Abstract
In theory, sensory perception should be more accurate when more neurons contribute to the representation of a stimulus. However, psychophysical experiments that use larger stimuli to activate larger pools of neurons sometimes report impoverished perceptual performance. To determine the neural mechanisms underlying these paradoxical findings, we trained monkeys to discriminate the direction of motion of visual stimuli that varied in size across trials, while simultaneously recording from populations of motion-sensitive neurons in cortical area MT. We used the resulting data to constrain a computational model that explained the behavioral data as an interaction of three main mechanisms: noise correlations, which prevented stimulus information from growing with stimulus size; neural surround suppression, which decreased sensitivity for large stimuli; and a read-out strategy that emphasized neurons with receptive fields near the stimulus center. These results suggest that paradoxical percepts reflect tradeoffs between sensitivity and noise in neuronal populations. DOI:http://dx.doi.org/10.7554/eLife.16167.001 People usually find it easier to see things when they are big and bright, but there are occasionally exceptions. One example concerns moving objects: when they are small, we can identify their direction of motion easily, but this becomes much more difficult for larger objects. This decreased perceptual sensitivity appears to be linked to other mental processes. For example, studies have suggested that people with high IQs have more difficulty perceiving the motion of large objects, whereas people with various psychiatric disorders, such as schizophrenia, are better able to see such movement. Although several theories have been proposed, there is currently no good explanation for these findings. Liu et al. set out to determine why the part of the brain that is responsible for vision (the visual cortex) fails to register the direction of large moving objects and how this failure might relate to mental function in general. To do this, Liu et al. trained monkeys to report which direction different sized stimuli were moving on a screen. The electrical activity of nerve cells in the part of the visual cortex that deals with movement was recorded while the monkeys performed this task. The results of the experiments revealed that, on average, these cells responded strongly to large moving stimuli, even though the monkeys had trouble seeing their motion. However, nerve cells are “noisy” – they respond a bit differently every time they are presented with the same stimulus – and this noise was stronger for larger stimuli. By studying the mathematical relationship between the noise and what the animals perceived, Liu et al. found that the visual cortex attempts to suppress the noise and in the process often shuts off the responses to large stimuli entirely. This suppression is likely to cause the movement of large stimuli to be poorly perceived. If suppressing this kind of noise is really responsible for failures in perceiving motion, then this mechanism could also explain the connection between motion perception and other mental processes. Liu et al. are currently testing this idea. DOI:http://dx.doi.org/10.7554/eLife.16167.002
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Affiliation(s)
- Liu D Liu
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Ralf M Haefner
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, United States
| | - Christopher C Pack
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada
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89
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Haefner R, Berkes P, Fiser J. Perceptual Decision-Making as Probabilistic Inference by Neural Sampling. Neuron 2016; 90:649-60. [DOI: 10.1016/j.neuron.2016.03.020] [Citation(s) in RCA: 133] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2014] [Revised: 10/05/2015] [Accepted: 03/12/2016] [Indexed: 11/29/2022]
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90
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Predictive coding as a model of cognition. Cogn Process 2016; 17:279-305. [DOI: 10.1007/s10339-016-0765-6] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Accepted: 04/06/2016] [Indexed: 10/21/2022]
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91
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Abstract
Brain function involves the activity of neuronal populations. Much recent effort has been devoted to measuring the activity of neuronal populations in different parts of the brain under various experimental conditions. Population activity patterns contain rich structure, yet many studies have focused on measuring pairwise relationships between members of a larger population-termed noise correlations. Here we review recent progress in understanding how these correlations affect population information, how information should be quantified, and what mechanisms may give rise to correlations. As population coding theory has improved, it has made clear that some forms of correlation are more important for information than others. We argue that this is a critical lesson for those interested in neuronal population responses more generally: Descriptions of population responses should be motivated by and linked to well-specified function. Within this context, we offer suggestions of where current theoretical frameworks fall short.
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Affiliation(s)
- Adam Kohn
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York 10461; .,Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, New York 10461
| | - Ruben Coen-Cagli
- Department of Basic Neuroscience, University of Geneva, CH-1211 Geneva, Switzerland; ,
| | - Ingmar Kanitscheider
- Department of Basic Neuroscience, University of Geneva, CH-1211 Geneva, Switzerland; , .,Center of Learning and Memory, The University of Texas at Austin, Austin, Texas 78712; .,Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712
| | - Alexandre Pouget
- Department of Basic Neuroscience, University of Geneva, CH-1211 Geneva, Switzerland; , .,Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York 14627.,Gatsby Computational Neuroscience Unit, University College London, W1T 4JG London, United Kingdom
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92
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Cumming BG, Nienborg H. Feedforward and feedback sources of choice probability in neural population responses. Curr Opin Neurobiol 2016; 37:126-132. [PMID: 26922005 DOI: 10.1016/j.conb.2016.01.009] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Revised: 01/13/2016] [Accepted: 01/14/2016] [Indexed: 11/29/2022]
Abstract
How the processing of signals carried by sensory neurons supports perceptual decisions is a long-standing question in neuroscience. The ability to record neuronal activity in awake animals while they perform psychophysical tasks near threshold has been a key advance in studying these questions. Trial-to-trial correlations between the activity of sensory neurons and the decisions reported by animals ('choice probabilities'), even when measured across repeated presentations of an identical stimulus provide insights into this problem. But understanding the sources of such co-variability between sensory neurons and behavior has proven more difficult than it initially appeared. Below, we discuss our current understanding of what gives rise to these correlations.
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Affiliation(s)
- Bruce G Cumming
- National Eye Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Hendrikje Nienborg
- Werner Reichardt Centre for Integrative Neuroscience, University of Tuebingen, 72076 Tuebingen, Germany.
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93
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Stimulus-induced visual cortical networks are recapitulated by spontaneous local and interareal synchronization. Proc Natl Acad Sci U S A 2016; 113:E606-15. [PMID: 26787906 DOI: 10.1073/pnas.1513773113] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Intrinsic covariation of brain activity has been studied across many levels of brain organization. Between visual areas, neuronal activity covaries primarily among portions with similar retinotopic selectivity. We hypothesized that spontaneous interareal coactivation is subserved by neuronal synchronization. We performed simultaneous high-density electrocorticographic recordings across the dorsal aspect of several visual areas in one hemisphere in each of two awake monkeys to investigate spatial patterns of local and interareal synchronization. We show that stimulation-induced patterns of interareal coactivation were reactivated in the absence of stimulation for the visual quadrant covered. Reactivation occurred through both interareal cofluctuation of local activity and interareal phase synchronization. Furthermore, the trial-by-trial covariance of the induced responses recapitulated the pattern of interareal coupling observed during stimulation, i.e., the signal correlation. Reactivation-related synchronization showed distinct peaks in the theta, alpha, and gamma frequency bands. During passive states, this rhythmic reactivation was augmented by specific patterns of arrhythmic correspondence. These results suggest that networks of intrinsic covariation observed at multiple levels and with several recording techniques are related to synchronization and that behavioral state may affect the structure of intrinsic dynamics.
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94
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Chen SC, Morley JW, Solomon SG. Spatial precision of population activity in primate area MT. J Neurophysiol 2015; 114:869-78. [PMID: 26041825 PMCID: PMC4533107 DOI: 10.1152/jn.00152.2015] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2015] [Accepted: 06/01/2015] [Indexed: 11/22/2022] Open
Abstract
The middle temporal (MT) area is a cortical area integral to the "where" pathway of primate visual processing, signaling the movement and position of objects in the visual world. The receptive field of a single MT neuron is sensitive to the direction of object motion but is too large to signal precise spatial position. Here, we asked if the activity of MT neurons could be combined to support the high spatial precision required in the where pathway. With the use of multielectrode arrays, we recorded simultaneously neural activity at 24-65 sites in area MT of anesthetized marmoset monkeys. We found that although individual receptive fields span more than 5° of the visual field, the combined population response can support fine spatial discriminations (<0.2°). This is because receptive fields at neighboring sites overlapped substantially, and changes in spatial position are therefore projected onto neural activity in a large ensemble of neurons. This fine spatial discrimination is supported primarily by neurons with receptive fields flanking the target locations. Population performance is degraded (by 13-22%) when correlations in neural activity are ignored, further reflecting the contribution of population neural interactions. Our results show that population signals can provide high spatial precision despite large receptive fields, allowing area MT to represent both the motion and the position of objects in the visual world.
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Affiliation(s)
- Spencer C Chen
- Australian Research Council Centre of Excellence for Integrative Brain Function, The University of Sydney, New South Wales, Australia; School of Medical Sciences, The University of Sydney, New South Wales, Australia;
| | - John W Morley
- School of Medicine, University of Western Sydney, Penrith, New South Wales, Australia; and
| | - Samuel G Solomon
- School of Medical Sciences, The University of Sydney, New South Wales, Australia; Institute for Behavioural Neuroscience, University College London, London, United Kingdom
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95
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Abstract
Noise correlations (r(noise)) between neurons can affect a neural population's discrimination capacity, even without changes in mean firing rates of neurons. r(noise), the degree to which the response variability of a pair of neurons is correlated, has been shown to change with attention with most reports showing a reduction in r(noise). However, the effect of reducing r(noise) on sensory discrimination depends on many factors, including the tuning similarity, or tuning correlation (r(tuning)), between the pair. Theoretically, reducing r(noise) should enhance sensory discrimination when the pair exhibits similar tuning, but should impair discrimination when tuning is dissimilar. We recorded from pairs of neurons in primary auditory cortex (A1) under two conditions: while rhesus macaque monkeys (Macaca mulatta) actively performed a threshold amplitude modulation (AM) detection task and while they sat passively awake. We report that, for pairs with similar AM tuning, average r(noise) in A1 decreases when the animal performs the AM detection task compared with when sitting passively. For pairs with dissimilar tuning, the average r(noise) did not significantly change between conditions. This suggests that attention-related modulation can target selective subcircuits to decorrelate noise. These results demonstrate that engagement in an auditory task enhances population coding in primary auditory cortex by selectively reducing deleterious r(noise) and leaving beneficial r(noise) intact.
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96
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Pitkow X, Liu S, Angelaki DE, DeAngelis GC, Pouget A. How Can Single Sensory Neurons Predict Behavior? Neuron 2015; 87:411-23. [PMID: 26182422 PMCID: PMC4683594 DOI: 10.1016/j.neuron.2015.06.033] [Citation(s) in RCA: 86] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2014] [Revised: 03/29/2015] [Accepted: 06/23/2015] [Indexed: 11/20/2022]
Abstract
Single sensory neurons can be surprisingly predictive of behavior in discrimination tasks. We propose this is possible because sensory information extracted from neural populations is severely restricted, either by near-optimal decoding of a population with information-limiting correlations or by suboptimal decoding that is blind to correlations. These have different consequences for choice correlations, the correlations between neural responses and behavioral choices. In the vestibular and cerebellar nuclei and the dorsal medial superior temporal area, we found that choice correlations during heading discrimination are consistent with near-optimal decoding of neuronal responses corrupted by information-limiting correlations. In the ventral intraparietal area, the choice correlations are also consistent with the presence of information-limiting correlations, but this area does not appear to influence behavior, although the choice correlations are particularly large. These findings demonstrate how choice correlations can be used to assess the efficiency of the downstream readout and detect the presence of information-limiting correlations.
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Affiliation(s)
- Xaq Pitkow
- Department of Neuroscience, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA; Department of Electrical and Computer Engineering, Rice University, 6100 Main MS-366, Houston, TX 77005, USA.
| | - Sheng Liu
- Department of Neuroscience, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Dora E Angelaki
- Department of Neuroscience, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA; Department of Electrical and Computer Engineering, Rice University, 6100 Main MS-366, Houston, TX 77005, USA
| | - Gregory C DeAngelis
- Department of Brain and Cognitive Sciences, University of Rochester, 358 Meliora Hall, Rochester, NY 14607, USA
| | - Alexandre Pouget
- Department of Brain and Cognitive Sciences, University of Rochester, 358 Meliora Hall, Rochester, NY 14607, USA; Department of Neuroscience, University de Genève, 1 Rue Michel-Servet, 1211 Geneva 4, Switzerland
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97
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Clark K, Squire RF, Merrikhi Y, Noudoost B. Visual attention: Linking prefrontal sources to neuronal and behavioral correlates. Prog Neurobiol 2015; 132:59-80. [PMID: 26159708 DOI: 10.1016/j.pneurobio.2015.06.006] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2014] [Revised: 06/25/2015] [Accepted: 06/28/2015] [Indexed: 11/26/2022]
Abstract
Attention is a means of flexibly selecting and enhancing a subset of sensory input based on the current behavioral goals. Numerous signatures of attention have been identified throughout the brain, and now experimenters are seeking to determine which of these signatures are causally related to the behavioral benefits of attention, and the source of these modulations within the brain. Here, we review the neural signatures of attention throughout the brain, their theoretical benefits for visual processing, and their experimental correlations with behavioral performance. We discuss the importance of measuring cue benefits as a way to distinguish between impairments on an attention task, which may instead be visual or motor impairments, and true attentional deficits. We examine evidence for various areas proposed as sources of attentional modulation within the brain, with a focus on the prefrontal cortex. Lastly, we look at studies that aim to link sources of attention to its neuronal signatures elsewhere in the brain.
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Affiliation(s)
- Kelsey Clark
- Montana State University, Bozeman, MT, United States
| | - Ryan Fox Squire
- Stanford University, Stanford, CA, United States; Lumos Labs, San Francisco, CA, United States
| | - Yaser Merrikhi
- School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
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98
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Conen KE, Padoa-Schioppa C. Neuronal variability in orbitofrontal cortex during economic decisions. J Neurophysiol 2015; 114:1367-81. [PMID: 26084903 DOI: 10.1152/jn.00231.2015] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2015] [Accepted: 06/15/2015] [Indexed: 11/22/2022] Open
Abstract
Neuroeconomic models assume that economic decisions are based on the activity of offer value cells in the orbitofrontal cortex (OFC), but testing this assertion has proven difficult. In principle, the decision made on a given trial should correlate with the stochastic fluctuations of these cells. However, this correlation, measured as a choice probability (CP), is small. Importantly, a neuron's CP reflects not only its individual contribution to the decision (termed readout weight), but also the intensity and the structure of correlated variability across the neuronal population (termed noise correlation). A precise mathematical relation between CPs, noise correlations, and readout weights was recently derived by Haefner and colleagues (Haefner RM, Gerwinn S, Macke JH, Bethge M. Nat Neurosci 16: 235-242, 2013) for a linear decision model. In this framework, concurrent measurements of noise correlations and CPs can provide quantitative information on how a population of cells contributes to a decision. Here we examined neuronal variability in the OFC of rhesus monkeys during economic decisions. Noise correlations had similar structure but considerably lower strength compared with those typically measured in sensory areas during perceptual decisions. In contrast, variability in the activity of individual cells was high and comparable to that recorded in other cortical regions. Simulation analyses based on Haefner's equation showed that noise correlations measured in the OFC combined with a plausible readout of offer value cells reproduced the experimental measures of CPs. In other words, the results obtained for noise correlations and those obtained for CPs taken together support the hypothesis that economic decisions are primarily based on the activity of offer value cells.
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Affiliation(s)
- Katherine E Conen
- Department of Anatomy and Neurobiology, Washington University in St. Louis, St. Louis, Missouri
| | - Camillo Padoa-Schioppa
- Department of Anatomy and Neurobiology, Washington University in St. Louis, St. Louis, Missouri; Department of Economics, Washington University in St. Louis, St. Louis, Missouri; and Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri
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99
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Belief states as a framework to explain extra-retinal influences in visual cortex. Curr Opin Neurobiol 2015; 32:45-52. [DOI: 10.1016/j.conb.2014.10.013] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2014] [Revised: 10/24/2014] [Accepted: 10/26/2014] [Indexed: 12/13/2022]
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100
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Full correlation matrix analysis (FCMA): An unbiased method for task-related functional connectivity. J Neurosci Methods 2015; 251:108-19. [PMID: 26004849 DOI: 10.1016/j.jneumeth.2015.05.012] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2015] [Revised: 05/13/2015] [Accepted: 05/14/2015] [Indexed: 11/24/2022]
Abstract
BACKGROUND The analysis of brain imaging data often requires simplifying assumptions because exhaustive analyses are computationally intractable. Standard univariate and multivariate analyses of brain activity ignore interactions between regions and analyses of interactions (functional connectivity) reduce the computational challenge by using seed regions of interest or brain parcellations. NEW METHOD To meet this challenge, we developed full correlation matrix analysis (FCMA), which leverages and optimizes algorithms from parallel computing and machine learning to efficiently analyze the pairwise correlations of all voxels in the brain during different cognitive tasks, with the goal of identifying task-related interactions in an unbiased manner. RESULTS When applied to a localizer dataset on a small compute cluster, FCMA accelerated a naive, serial approach by four orders of magnitude, reducing running time from two years to one hour. In addition to this performance gain, FCMA emphasized different brain areas than existing methods. In particular, beyond replicating known category selectivity in visual cortex, FCMA also revealed a region of medial prefrontal cortex whose selectivity derived from differential patterns of functional connectivity across categories. COMPARISON WITH EXISTING METHOD(S) For benchmarking, we started with a naive approach and progressively built up to the complete FCMA procedure by adding optimized classifier algorithms, multi-threaded parallelism, and multi-node parallelism. To evaluate what can be learned with FCMA, we compared it against multivariate pattern analysis of activity and seed-based analysis of functional connectivity. CONCLUSIONS FCMA demonstrates how advances in computer science can alleviate computational bottlenecks in neuroscience. We have released a software toolbox to help others evaluate FCMA.
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