32 research outputs found

    Feature-specific prediction errors and surprise across macaque fronto-striatal circuits

    Full text link
    [EN] To adjust expectations efficiently, prediction errors need to be associated with the precise features that gave rise to the unexpected outcome, but this credit assignment may be problematic if stimuli differ on multiple dimensions and it is ambiguous which feature dimension caused the outcome. Here, we report a potential solution: neurons in four recorded areas of the anterior fronto-striatal networks encode prediction errors that are specific to feature values of different dimensions of attended multidimensional stimuli. The most ubiquitous prediction error occurred for the reward-relevant dimension. Feature-specific prediction error signals a) emerge on average shortly after non-specific prediction error signals, b) arise earliest in the anterior cingulate cortex and later in dorsolateral prefrontal cortex, caudate and ventral striatum, and c) contribute to feature-based stimulus selection after learning. Thus, a widely-distributed feature-specific eligibility trace may be used to update synaptic weights for improved feature-based attention.This work was supported by grant MOP 102482 from the Canadian Institutes of Health Research (T.W.) and the Natural Sciences and Engineering Research Council of Canada (T.W.), as well as by the Brain in Action CREATE-IRTG program (M.O. and T.W.), and by grant LPDS 2012-08 from the Deutsche Akademie der Naturforscher Leopoldina (S.W.). Imaging data provided by the Duke Center for In Vivo Microscopy, an NIH Biomedical Technology Resource (NIHP41EB015897, 1S10OD010683-01). The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of this manuscript. The authors would like to thank Hongying Wang for technical supportOemisch, M.; Westendorff, S.; Azimi, M.; Hassani, SA.; Ardid-Ramírez, JS.; Tiesinga, P.; Womelsdorf, T. (2019). Feature-specific prediction errors and surprise across macaque fronto-striatal circuits. Nature Communications. 10:1-15. https://doi.org/10.1038/s41467-018-08184-9S11510Farashahi, S., Rowe, K., Aslami, Z., Lee, D. & Soltani, A. Feature-based learning improves adaptability without compromising precision. Nat. Commun. 8, 1768 (2017).Hikosaka, O., Ghazizadeh, A., Griggs, W. & Amita, H. Parallel basal ganglia circuits for decision making. J. Neural Transm. 1–15 (2017). https://doi.org/10.1007/s00702-017-1691-1Leong, Y. C., Radulescu, A., Daniel, R., DeWoskin, V. & Niv, Y. Dynamic Interaction between reinforcement learning and attention in multidimensional environments. Neuron 93, 451–463 (2017).Niv, Y. et al. Reinforcement learning in multidimensional environments relies on attention mechanisms. J. Neurosci. 35, 8145–8157 (2015).Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction. Vol. 135. Cambridge: MIT Press (1998).Gottlieb, J. Attention, learning and the value of information. Neuron 76, 281–295 (2012).Pearce, J. & Hall, G. A model for Pavlovian learning: variation in the effectiveness of conditioned but not unconditioned stimuli. Psychol. Rev. 87, 532–552 (1980).Daddaoua, N., Lopes, M. & Gottlieb, J. Intrinsically motivated oculomotor exploration guided by uncertainty reduction and conditioned reinforcement in non-human primates. Sci. Rep. 6, 1–15 (2016).Dayan, P., Kakade, S. & Montague, P. R. Learning and selective attention. Nat. Neurosci. 3, 1218–1223 (2000).Hassani, S. A. et al. A computational psychiatry approach identifies how alpha-2A noradrenergic agonist Guanfacine affects feature-based reinforcement learning in the macaque. Sci. Rep. 7, 1–19 (2017).Wilson, R. C. & Niv, Y. Inferring relevance in a changing world. Front. Hum. Neurosci. 5, 1–14 (2012).Kruschke, J. K. & Hullinger, R. A. Evolution of attention in learning. Comput. Models Condition. (2010). https://doi.org/10.1017/CBO9780511760402.002Asaad, W. F., Lauro, P. M., Perge, J. A. & Eskandar, E. N. Prefrontal neurons encode a solution to the credit assignment problem. J. Neurosci. 37, 3311–3316 (2017).Haber, S. N. & Knutson, B. The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology 35, 4–26 (2010).Dias, R., Robbins, T. W. & Roberts, A. C. Dissociation in prefrontal cortex of affective and attentional shifts. Nature 380, 69–72 (1996).Glimcher, P. W. Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis. Proc. Natl Acad. Sci. USA 108 Suppl, 15647–15654 (2011).Bichot, N. P., Heard, M. T., DeGennaro, E. M. & Desimone, R. A source for feature-based attention in the prefrontal cortex. Neuron 88, 832–844 (2015).Kaping, D., Vinck, M., Hutchison, R. M., Everling, S. & Womelsdorf, T. Specific contributions of ventromedial, anterior cingulate, and lateral prefrontal cortex for attentional selection and stimulus valuation. PLoS Biol. 9, e1001224 (2011).Alexander, W. H. & Brown, J. W. Hierarchical error representation: a computational model of anterior cingulate and dorsolateral prefrontal cortex. Neural Comput. 27, 2354–2410 (2015).Sutton, R. S. & Barto, A. G. Reinforcement Learning: an Introduction. 322 (1998). https://doi.org/10.1109/TNN.1998.712192Roelfsema, P. R. & van Ooyen, A. Attention-gated reinforcement learning of internal representations for classification. Neural Comput. 17, 2176–2214 (2005).Rombouts, J. O., Bohte, S. M. & Roelfsema, P. R. How attention can create synaptic tags for the learning of working memories in sequential tasks. PLoS Comput. Biol. 11, 1–34 (2015).Balcarras, M., Ardid, S., Kaping, D., Everling, S. & Womelsdorf, T. Attentional selection can be predicted by reinforcement learning of task-relevant stimulus features weighted by value-independent stickiness. J. Cogn. Neurosci. 28, 333–349 (2016).Smith, A. C. et al. Dynamic analysis of learning in behavioral experiments. J. Neurosci. 24, 447–461 (2004).Kennerley, S. W., Behrens, T. E. J. & Wallis, J. D. Double dissociation of value computations in orbitofrontal and anterior cingulate neurons. Nat. Neurosci. 14, 1581–1589 (2011).Asaad, W. F. & Eskandar, E. N. Encoding of both positive and negative reward prediction errors by neurons of the primate lateral prefrontal cortex and caudate nucleus. J. Neurosci. 31, 17772–17787 (2011).Hayden, B. Y., Heilbronner, S. R., Pearson, J. M. & Platt, M. L. Surprise signals in anterior cingulate cortex: neuronal encoding of unsigned reward prediction errors driving adjustment in behavior. J. Neurosci. 31, 4178–4187 (2011).Schultz, W. Dopamine reward prediction error coding. Dialogues Clin. Neurosci. 18, 23–32 (2016).Izquierdo, A., Brigman, J. L., Radke, A. K., Rudebeck, P. H. & Holmes, A. The neural basis of reversal learning: an updated perspective. Neuroscience 345, 12–26 (2017).Fiorillo, C. D., Tobler, P. N. & Schultz, W. Discrete coding of reward probability and uncertainty by dopamine neurons. Science 299, 1898–1902 (2003).Schultz, W. Predictive reward signal of dopamine neurons. J. Neurophysiol. 80, 1–27 (1998).Ardid, S. et al. Mapping of functionally characterized cell classes onto canonical circuit operations in primate prefrontal cortex. J. Neurosci. 35, 2975–2991 (2015).Berke, J. D. Uncoordinated firing rate changes of striatal fast-spiking interneurons during behavioural task performance. J. Neurosci. 28, 10075–10080 (2008).Lansink, C. S., Goltstein, P. M., Lankelma, J. V. & Pennartz, C. M. A. Fast-spiking interneurons of the rat ventral striatum: temporal coordination of activity with principal cells and responsiveness to reward. Eur. J. Neurosci. 32, 494–508 (2010).Kawaguchi, Y. Physiological, morphological, and histochemical characterization of three classes of interneurons in rat neostriatum. J. Neurosci. 13, 4908–4923 (1993).Shen, C. et al. Anterior cingulate cortex cells identify process-specific errors of attentional control prior to transient prefrontal-cingulate inhibition. Cereb. Cortex 25, 2213–2228 (2015).Shenhav, A., Cohen, J. D. & Botvinick, M. M. Dorsal anterior cingulate cortex and the value of control. Nat. Neurosci. 19, 1286–1291 (2016).Quilodran, R., Rothé, M. & Procyk, E. Behavioral shifts and action valuation in the anterior cingulate cortex. Neuron 57, 314–325 (2008).Kennerley, S. W., Dahmubed, A. F., Lara, A. H. & Wallis, J. D. Neurons in the frontal lobe encode the value of multiple decision variables. J. Cogn. Neurosci. 21, 1162–1178 (2009).Womelsdorf, T., Johnston, K., Vinck, M. & Everling, S. Theta-activity in anterior cingulate cortex predicts task rules and their adjustments following errors. Proc. Natl Acad. Sci. 107, 5248–5253 (2010).Oemisch, M., Westendorff, S., Everling, S. & Womelsdorf, T. Interareal spike-train correlations of anterior cingulate and dorsal prefrontal cortex during attention shifts. J. Neurosci. 35, 13076–13089 (2015).Voloh, B., Valiante, T. A., Everling, S. & Womelsdorf, T. Theta-gamma coordination between anterior cingulate and prefrontal cortex indexes correct attention shifts. Proc. Natl Acad. Sci. USA 112, 8457–8462 (2015).Westendorff, S., Kaping, D., Everling, S. & Womelsdorf, T. Prefrontal and anterior cingulate cortex neurons encode attentional targets even when they do not apparently bias behavior. J. Neurophysiol. 116, 796–811 (2016).Womelsdorf, T. & Everling, S. Long-range attention networks: circuit motifs underlying endogenously controlled stimulus selection. Trends Neurosci. 38, 682–700 (2015).Medalla, M. & Barbas, H. Synapses with inhibitory neurons differentiate anterior cingulate from dorsolateral prefrontal pathways associated with cognitive control. Neuron 61, 609–620 (2009).Antzoulatos, E. G. & Miller, E. K. Increases in functional connectivity between prefrontal cortex and striatum during category learning. Neuron 83, 216–225 (2014).Womelsdorf, T., Ardid, S., Everling, S. & Valiante, T. A. Burst firing synchronizes prefrontal and anterior cingulate cortex during attentional control. Curr. Biol. 1–9 (2014). https://doi.org/10.1016/j.cub.2014.09.046Hunt, L. T. & Hayden, B. Y. A distributed, hierarchical and recurrent framework for reward-based choice. Nat. Rev. Neurosci. 18, 172–182 (2017).Kable, J. W. & Glimcher, P. W. The neurobiology of decision: consensus and controversy. Neuron 63, 733–745 (2009).Badre, D. & Nee, D. E. Frontal cortex and the hierarchical control of behavior. Trends Cogn. Sci. 22, 170–188 (2018).Tian, J. et al. Distributed and mixed information in monosynaptic inputs to dopamine neurons. Neuron 1374–1389 (2016). https://doi.org/10.1016/j.neuron.2016.08.018den Ouden, H. E. M., Kok, P. & de Lange, F. P. How prediction errors shape perception, attention, and motivation. Front. Psychol. 3, 1–12 (2012).Rigotti, M. et al. The importance of mixed selectivity in complex cognitive tasks. Nature 497, 585–590 (2013).Genovesio, A., Wise, S. P. & Passingham, R. E. Prefrontal—parietal function: from foraging to foresight. Trends Cogn. Sci. 18, 72–81 (2014).Donahue, C. H. & Lee, D. Dynamic routing of task-relevant signals for decision making in dorsolateral prefrontal cortex. Nat. Neurosci. 18, 1–9 (2015).Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013).Berke, J. D. Functional properties of striatal fast-spiking interneurons. Front. Syst. Neurosci. 5, 1–7 (2011).Hennequin, G., Agnes, E. J. & Vogels, T. P. Inhibitory plasticity: balance, control, and codependence. Annu. Rev. Neurosci. 40, 557–579 (2017).Wilson, F. A., O’Scalaidhe, S. P. & Goldman-Rakic, P. S. Functional synergism between putative gamma-aminobutyrate-containing neurons and pyramidal neurons in prefrontal cortex. Proc. Natl Acad. Sci. 91, 4009–4013 (1994).Lee, K. et al. Parvalbumin interneurons modulate striatal output and enhance performance during associative learning. Neuron 93, 1451–1463.e4 (2017).Vogels, T. P., Sprekeler, H., Zenke, F., Clopath, C. & Gerstner, W. Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks. Science 334, 1569–1573 (2011).Le Pelley, M. E., Mitchell, C. J., Beesley, T., George, D. N. & Wills, A. J. Attention and associative learning in humans: an integrative review. Psychol. Bull. 142, 1111–1140 (2016).Courville, A. C., Daw, N. D. & Touretzky, D. S. Bayesian theories of conditioning in a changing world. Trends Cogn. Sci. 10, 294–300 (2006).Gottlieb, J., Hayhoe, M., Hikosaka, O. & Rangel, A. Attention, reward, and information seeking. J. Neurosci. 34, 15497–15504 (2014).Rusch, T., Korn, C. W. & Gläscher, J. A two-way street between attention and learning. Neuron 93, 256–258 (2017).Takahashi, Y. K., Langdon, A. J., Niv, Y. & Schoenbaum, G. Temporal specificity of reward prediction errors signaled by putative dopamine neurons in rat VTA depends on ventral striatum. Neuron 91, 182–193 (2016).Watabe-Uchida, M., Eshel, N. & Uchida, N. Neural circuitry of reward prediction error. Annu. Rev. Neurosci. 40, 373–394 (2017).Krauzlis, R. J., Bollimunta, A., Arcizet, F. & Wang, L. Attention as an effect not a cause. Trends Cogn. Sci. 18, 457–464 (2014).Lovejoy, L. P. & Krauzlis, R. J. Inactivation of primate superior colliculus impairs covert selection of signals for perceptual judgments. Nat. Neurosci. 13, 261–266 (2010).Rasmussen, D., Voelker, A. & Eliasmith, C. A neural model of hierarchical reinforcement learning. PLoS ONE 12, e0180234 (2017).Fusi, S., Asaad, W. F., Miller, E. K. & Wang, X. J. A neural circuit model of flexible sensorimotor mapping: learning and forgetting on multiple timescales. Neuron 54, 319–333 (2007).Roelfsema, P. R. & Holtmaat, A. Control of synaptic plasticity in deep cortical networks. Nat. Rev. Neurosci. 19, 166–180 (2018).Calabrese, E. et al. A diffusion tensor MRI atlas of the postmortem rhesus macaque brain. Neuroimage 117, 408–416 (2015).Bakker, R., Tiesinga, P. & Kötter, R. The scalable brain atlas: instant web-based access to public brain atlases and related content. Neuroinformatics 13, 353–366 (2015)

    Mutual Validation of GNSS Height Measurements and High-precision Geometric-astronomical Leveling

    Get PDF
    The method of geometric-astronomical leveling is presented as a suited technique for the validation of GNSS (Global Navigation Satellite System) heights. In geometric-astronomical leveling, the ellipsoidal height differences are obtained by combining conventional spirit leveling and astronomical leveling. Astronomical leveling with recently developed digital zenith camera systems is capable of providing the geometry of equipotential surfaces of the gravity field accurate to a few 0.1 mm per km. This is comparable to the accuracy of spirit leveling. Consequently, geometric-astronomical leveling yields accurate ellipsoidal height differences that may serve as an independent check on GNSS height measurements at local scales. A test was performed in a local geodetic network near Hanover. GPS observations were simultaneously carried out at five stations over a time span of 48 h and processed considering state-of-the-art techniques and sophisticated new approaches to reduce station-dependent errors. The comparison of GPS height differences with those from geometric-astronomical leveling shows a promising agreement of some millimeters. The experiment indicates the currently achievable accuracy level of GPS height measurements and demonstrates the practical applicability of the proposed approach for the validation of GNSS height measurements as well as the evaluation of GNSS height processing strategies

    What is ‘anti’ about anti-reaches? Reference frames selectively affect reaction times and endpoint variability

    Get PDF
    Reach movement planning involves the representation of spatial target information in different reference frames. Neurons at parietal and premotor stages of the cortical sensorimotor system represent target information in eye- or hand-centered reference frames, respectively. How the different neuronal representations affect behavioral parameters of motor planning and control, i.e. which stage of neural representation is relevant for which aspect of behavior, is not obvious from the physiology. Here, we test with a behavioral experiment if different kinematic movement parameters are affected to a different degree by either an eye- or hand-reference frame. We used a generalized anti-reach task to test the influence of stimulus-response compatibility (SRC) in eye- and hand-reference frames on reach reaction times, movement times, and endpoint variability. While in a standard anti-reach task, the SRC is identical in the eye- and hand-reference frames, we could separate SRC for the two reference frames. We found that reaction times were influenced by the SRC in eye- and hand-reference frame. In contrast, movement times were only influenced by the SRC in hand-reference frame, and endpoint variability was only influenced by the SRC in eye-reference frame. Since movement time and endpoint variability are the result of planning and control processes, while reaction times are consequences of only the planning process, we suggest that SRC effects on reaction times are highly suited to investigate reference frames of movement planning, and that eye- and hand-reference frames have distinct effects on different phases of motor action and different kinematic movement parameters

    Frontal-to-Parietal Top-Down Causal Streams along the Dorsal Attention Network Exclusively Mediate Voluntary Orienting of Attention

    Get PDF
    Previous effective connectivity analyses of functional magnetic resonance imaging (fMRI) have revealed dynamic causal streams along the dorsal attention network (DAN) during voluntary attentional control in the human brain. During resting state, however, fMRI has shown that the DAN is also intrinsically configured by functional connectivity, even in the absence of explicit task demands, and that may conflict with effective connectivity studies. To resolve this contradiction, we performed an effective connectivity analysis based on partial Granger causality (pGC) on event-related fMRI data during Posner's cueing paradigm while optimizing experimental and imaging parameters for pGC analysis. Analysis by pGC can factor out exogenous or latent influences due to unmeasured variables. Typical regions along the DAN with greater activation during orienting than withholding of attention were selected as regions of interest (ROIs). pGC analysis on fMRI data from the ROIs showed that frontal-to-parietal top-down causal streams along the DAN appeared during (voluntary) orienting, but not during other, less-attentive and/or resting-like conditions. These results demonstrate that these causal streams along the DAN exclusively mediate voluntary covert orienting. These findings suggest that neural representations of attention in frontal regions are at the top of the hierarchy of the DAN for embodying voluntary attentional control

    Dynamic Effective Connectivity of Inter-Areal Brain Circuits

    Get PDF
    Anatomic connections between brain areas affect information flow between neuronal circuits and the synchronization of neuronal activity. However, such structural connectivity does not coincide with effective connectivity (or, more precisely, causal connectivity), related to the elusive question “Which areas cause the present activity of which others?”. Effective connectivity is directed and depends flexibly on contexts and tasks. Here we show that dynamic effective connectivity can emerge from transitions in the collective organization of coherent neural activity. Integrating simulation and semi-analytic approaches, we study mesoscale network motifs of interacting cortical areas, modeled as large random networks of spiking neurons or as simple rate units. Through a causal analysis of time-series of model neural activity, we show that different dynamical states generated by a same structural connectivity motif correspond to distinct effective connectivity motifs. Such effective motifs can display a dominant directionality, due to spontaneous symmetry breaking and effective entrainment between local brain rhythms, although all connections in the considered structural motifs are reciprocal. We show then that transitions between effective connectivity configurations (like, for instance, reversal in the direction of inter-areal interactions) can be triggered reliably by brief perturbation inputs, properly timed with respect to an ongoing local oscillation, without the need for plastic synaptic changes. Finally, we analyze how the information encoded in spiking patterns of a local neuronal population is propagated across a fixed structural connectivity motif, demonstrating that changes in the active effective connectivity regulate both the efficiency and the directionality of information transfer. Previous studies stressed the role played by coherent oscillations in establishing efficient communication between distant areas. Going beyond these early proposals, we advance here that dynamic interactions between brain rhythms provide as well the basis for the self-organized control of this “communication-through-coherence”, making thus possible a fast “on-demand” reconfiguration of global information routing modalities

    Dopamine Receptors Influence Internally Generated Oscillations during Rule Processing in Primate Prefrontal Cortex

    No full text
    Neural oscillations in distinct frequency bands in the prefrontal cortex (pFC) are associated with specialized roles during cognitive control. How dopamine modulates oscillations to structure pFC functions remains unknown. We trained macaques to switch between two numerical rules and recorded local field potentials from pFC while applying dopamine receptor targeting drugs using microiontophoresis. We show that the D1 and D2 family receptors (D1Rs and D2Rs, respectively) specifically altered internally generated prefrontal oscillations, whereas sensory-evoked potentials remained unchanged. Blocking D1Rs or stimulating D2Rs increased low-frequency theta and alpha oscillations known to be involved in learning and memory. In contrast, only D1R inhibition enhanced high-frequency beta oscillations, whereas only D2R stimulation increased gamma oscillations linked to top-down and bottom-up attentional processing. These findings suggest that dopamine alters neural oscillations relevant for executive functioning through dissociable actions at the receptor level
    corecore