1
|
Rankin G, Chirila AM, Emanuel AJ, Zhang Z, Woolf CJ, Drugowitsch J, Ginty DD. Nerve injury disrupts temporal processing in the spinal cord dorsal horn through alterations in PV + interneurons. Cell Rep 2024; 43:113718. [PMID: 38294904 DOI: 10.1016/j.celrep.2024.113718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 11/13/2023] [Accepted: 01/11/2024] [Indexed: 02/02/2024] Open
Abstract
How mechanical allodynia following nerve injury is encoded in patterns of neural activity in the spinal cord dorsal horn (DH) remains incompletely understood. We address this in mice using the spared nerve injury model of neuropathic pain and in vivo electrophysiological recordings. Surprisingly, despite dramatic behavioral over-reactivity to mechanical stimuli following nerve injury, an overall increase in sensitivity or reactivity of DH neurons is not observed. We do, however, observe a marked decrease in correlated neural firing patterns, including the synchrony of mechanical stimulus-evoked firing, across the DH. Alterations in DH temporal firing patterns are recapitulated by silencing DH parvalbumin+ (PV+) interneurons, previously implicated in mechanical allodynia, as are allodynic pain-like behaviors. These findings reveal decorrelated DH network activity, driven by alterations in PV+ interneurons, as a prominent feature of neuropathic pain and suggest restoration of proper temporal activity as a potential therapeutic strategy to treat chronic neuropathic pain.
Collapse
Affiliation(s)
- Genelle Rankin
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Anda M Chirila
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Alan J Emanuel
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Zihe Zhang
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA; F.M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA 02115, USA
| | - Clifford J Woolf
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA; F.M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA 02115, USA
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
| | - David D Ginty
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA.
| |
Collapse
|
2
|
Lowet AS, Zheng Q, Meng M, Matias S, Drugowitsch J, Uchida N. An opponent striatal circuit for distributional reinforcement learning. bioRxiv 2024:2024.01.02.573966. [PMID: 38260354 PMCID: PMC10802299 DOI: 10.1101/2024.01.02.573966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Machine learning research has achieved large performance gains on a wide range of tasks by expanding the learning target from mean rewards to entire probability distributions of rewards - an approach known as distributional reinforcement learning (RL)1. The mesolimbic dopamine system is thought to underlie RL in the mammalian brain by updating a representation of mean value in the striatum2,3, but little is known about whether, where, and how neurons in this circuit encode information about higher-order moments of reward distributions4. To fill this gap, we used high-density probes (Neuropixels) to acutely record striatal activity from well-trained, water-restricted mice performing a classical conditioning task in which reward mean, reward variance, and stimulus identity were independently manipulated. In contrast to traditional RL accounts, we found robust evidence for abstract encoding of variance in the striatum. Remarkably, chronic ablation of dopamine inputs disorganized these distributional representations in the striatum without interfering with mean value coding. Two-photon calcium imaging and optogenetics revealed that the two major classes of striatal medium spiny neurons - D1 and D2 MSNs - contributed to this code by preferentially encoding the right and left tails of the reward distribution, respectively. We synthesize these findings into a new model of the striatum and mesolimbic dopamine that harnesses the opponency between D1 and D2 MSNs5-15 to reap the computational benefits of distributional RL.
Collapse
Affiliation(s)
- Adam S Lowet
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
- Program in Neuroscience, Harvard University, Boston, MA, USA
| | - Qiao Zheng
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Melissa Meng
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Sara Matias
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Jan Drugowitsch
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Naoshige Uchida
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| |
Collapse
|
3
|
Noel JP, Bill J, Ding H, Vastola J, DeAngelis GC, Angelaki DE, Drugowitsch J. Causal inference during closed-loop navigation: parsing of self- and object-motion. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220344. [PMID: 37545300 PMCID: PMC10404925 DOI: 10.1098/rstb.2022.0344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 06/20/2023] [Indexed: 08/08/2023] Open
Abstract
A key computation in building adaptive internal models of the external world is to ascribe sensory signals to their likely cause(s), a process of causal inference (CI). CI is well studied within the framework of two-alternative forced-choice tasks, but less well understood within the cadre of naturalistic action-perception loops. Here, we examine the process of disambiguating retinal motion caused by self- and/or object-motion during closed-loop navigation. First, we derive a normative account specifying how observers ought to intercept hidden and moving targets given their belief about (i) whether retinal motion was caused by the target moving, and (ii) if so, with what velocity. Next, in line with the modelling results, we show that humans report targets as stationary and steer towards their initial rather than final position more often when they are themselves moving, suggesting a putative misattribution of object-motion to the self. Further, we predict that observers should misattribute retinal motion more often: (i) during passive rather than active self-motion (given the lack of an efference copy informing self-motion estimates in the former), and (ii) when targets are presented eccentrically rather than centrally (given that lateral self-motion flow vectors are larger at eccentric locations during forward self-motion). Results support both of these predictions. Lastly, analysis of eye movements show that, while initial saccades toward targets were largely accurate regardless of the self-motion condition, subsequent gaze pursuit was modulated by target velocity during object-only motion, but not during concurrent object- and self-motion. These results demonstrate CI within action-perception loops, and suggest a protracted temporal unfolding of the computations characterizing CI. This article is part of the theme issue 'Decision and control processes in multisensory perception'.
Collapse
Affiliation(s)
- Jean-Paul Noel
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - Johannes Bill
- Department of Neurobiology, Harvard University, Boston, MA 02115, USA
- Department of Psychology, Harvard University, Boston, MA 02115, USA
| | - Haoran Ding
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - John Vastola
- Department of Neurobiology, Harvard University, Boston, MA 02115, USA
| | - Gregory C. DeAngelis
- Department of Brain and Cognitive Sciences, Center for Visual Science, University of Rochester, Rochester, NY 14611, USA
| | - Dora E. Angelaki
- Center for Neural Science, New York University, New York, NY 10003, USA
- Tandon School of Engineering, New York University, New York, NY 10003, USA
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard University, Boston, MA 02115, USA
- Center for Brain Science, Harvard University, Boston, MA 02115, USA
| |
Collapse
|
4
|
Bukwich M, Campbell MG, Zoltowski D, Kingsbury L, Tomov MS, Stern J, Kim HR, Drugowitsch J, Linderman SW, Uchida N. Competitive integration of time and reward explains value-sensitive foraging decisions and frontal cortex ramping dynamics. bioRxiv 2023:2023.09.05.556267. [PMID: 37732217 PMCID: PMC10508756 DOI: 10.1101/2023.09.05.556267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
The ability to make advantageous decisions is critical for animals to ensure their survival. Patch foraging is a natural decision-making process in which animals decide when to leave a patch of depleting resources to search for a new one. To study the algorithmic and neural basis of patch foraging behavior in a controlled laboratory setting, we developed a virtual foraging task for head-fixed mice. Mouse behavior could be explained by ramp-to-threshold models integrating time and rewards antagonistically. Accurate behavioral modeling required inclusion of a slowly varying "patience" variable, which modulated sensitivity to time. To investigate the neural basis of this decision-making process, we performed dense electrophysiological recordings with Neuropixels probes broadly throughout frontal cortex and underlying subcortical areas. We found that decision variables from the reward integrator model were represented in neural activity, most robustly in frontal cortical areas. Regression modeling followed by unsupervised clustering identified a subset of neurons with ramping activity. These neurons' firing rates ramped up gradually in single trials over long time scales (up to tens of seconds), were inhibited by rewards, and were better described as being generated by a continuous ramp rather than a discrete stepping process. Together, these results identify reward integration via a continuous ramping process in frontal cortex as a likely candidate for the mechanism by which the mammalian brain solves patch foraging problems.
Collapse
Affiliation(s)
- Michael Bukwich
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, 02138
- Center for Brain Science, Harvard University, Cambridge, MA, 02138
- Current address: Sainsbury Wellcome Centre, University College London, London, W1T 4JG, UK
| | - Malcolm G Campbell
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, 02138
- Center for Brain Science, Harvard University, Cambridge, MA, 02138
| | - David Zoltowski
- Department of Statistics, Stanford University, Stanford, CA, 94305
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305
| | - Lyle Kingsbury
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, 02138
- Center for Brain Science, Harvard University, Cambridge, MA, 02138
| | - Momchil S Tomov
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, 02138
- Center for Brain Science, Harvard University, Cambridge, MA, 02138
- Current address: Motional AD LLC, Boston, MA 02210
| | - Joshua Stern
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, 02138
- Center for Brain Science, Harvard University, Cambridge, MA, 02138
| | - HyungGoo R Kim
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, 02138
- Center for Brain Science, Harvard University, Cambridge, MA, 02138
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, Republic of Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, MA, 02115
| | - Scott W Linderman
- Department of Statistics, Stanford University, Stanford, CA, 94305
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305
| | - Naoshige Uchida
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, 02138
- Center for Brain Science, Harvard University, Cambridge, MA, 02138
| |
Collapse
|
5
|
Rankin G, Chirila AM, Emanuel AJ, Zhang Z, Woolf CJ, Drugowitsch J, Ginty DD. Nerve injury disrupts temporal processing in the spinal cord dorsal horn through alterations in PV + interneurons. bioRxiv 2023:2023.03.20.533541. [PMID: 36993199 PMCID: PMC10055222 DOI: 10.1101/2023.03.20.533541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
How mechanical allodynia following nerve injury is encoded in patterns of neural activity in the spinal cord dorsal horn (DH) is not known. We addressed this using the spared nerve injury model of neuropathic pain and in vivo electrophysiological recordings. Surprisingly, despite dramatic behavioral over-reactivity to mechanical stimuli following nerve injury, an overall increase in sensitivity or reactivity of DH neurons was not observed. We did, however, observe a marked decrease in correlated neural firing patterns, including the synchrony of mechanical stimulus-evoked firing, across the DH. Alterations in DH temporal firing patterns were recapitulated by silencing DH parvalbumin + (PV + ) inhibitory interneurons, previously implicated in mechanical allodynia, as were allodynic pain-like behaviors in mice. These findings reveal decorrelated DH network activity, driven by alterations in PV + interneurons, as a prominent feature of neuropathic pain, and suggest that restoration of proper temporal activity is a potential treatment for chronic neuropathic pain.
Collapse
|
6
|
Kutschireiter A, Basnak MA, Wilson RI, Drugowitsch J. Bayesian inference in ring attractor networks. Proc Natl Acad Sci U S A 2023; 120:e2210622120. [PMID: 36812206 PMCID: PMC9992764 DOI: 10.1073/pnas.2210622120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 01/12/2023] [Indexed: 02/24/2023] Open
Abstract
Working memories are thought to be held in attractor networks in the brain. These attractors should keep track of the uncertainty associated with each memory, so as to weigh it properly against conflicting new evidence. However, conventional attractors do not represent uncertainty. Here, we show how uncertainty could be incorporated into an attractor, specifically a ring attractor that encodes head direction. First, we introduce a rigorous normative framework (the circular Kalman filter) for benchmarking the performance of a ring attractor under conditions of uncertainty. Next, we show that the recurrent connections within a conventional ring attractor can be retuned to match this benchmark. This allows the amplitude of network activity to grow in response to confirmatory evidence, while shrinking in response to poor-quality or strongly conflicting evidence. This "Bayesian ring attractor" performs near-optimal angular path integration and evidence accumulation. Indeed, we show that a Bayesian ring attractor is consistently more accurate than a conventional ring attractor. Moreover, near-optimal performance can be achieved without exact tuning of the network connections. Finally, we use large-scale connectome data to show that the network can achieve near-optimal performance even after we incorporate biological constraints. Our work demonstrates how attractors can implement a dynamic Bayesian inference algorithm in a biologically plausible manner, and it makes testable predictions with direct relevance to the head direction system as well as any neural system that tracks direction, orientation, or periodic rhythms.
Collapse
Affiliation(s)
| | | | - Rachel I. Wilson
- Department of Neurobiology, Harvard Medical School, Boston, MA02115
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, MA02115
| |
Collapse
|
7
|
Gao W, Lin Y, Shen J, Han J, Song X, Lu Y, Zhan H, Li Q, Ge H, Lin Z, Shi W, Drugowitsch J, Tang H, Chen X. Diverse effects of gaze direction on heading perception in humans. Cereb Cortex 2023:7024719. [PMID: 36734278 DOI: 10.1093/cercor/bhac541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 12/24/2022] [Accepted: 12/27/2022] [Indexed: 02/04/2023] Open
Abstract
Gaze change can misalign spatial reference frames encoding visual and vestibular signals in cortex, which may affect the heading discrimination. Here, by systematically manipulating the eye-in-head and head-on-body positions to change the gaze direction of subjects, the performance of heading discrimination was tested with visual, vestibular, and combined stimuli in a reaction-time task in which the reaction time is under the control of subjects. We found the gaze change induced substantial biases in perceived heading, increased the threshold of discrimination and reaction time of subjects in all stimulus conditions. For the visual stimulus, the gaze effects were induced by changing the eye-in-world position, and the perceived heading was biased in the opposite direction of gaze. In contrast, the vestibular gaze effects were induced by changing the eye-in-head position, and the perceived heading was biased in the same direction of gaze. Although the bias was reduced when the visual and vestibular stimuli were combined, integration of the 2 signals substantially deviated from predictions of an extended diffusion model that accumulates evidence optimally over time and across sensory modalities. These findings reveal diverse gaze effects on the heading discrimination and emphasize that the transformation of spatial reference frames may underlie the effects.
Collapse
Affiliation(s)
- Wei Gao
- Department of Neurology and Psychiatry of the Second Affiliated Hospital, College of Biomedical Engineering and Instrument Science, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, 268 Kaixuan Road, Jianggan District, Hangzhou 310029, China
| | - Yipeng Lin
- Department of Neurology and Psychiatry of the Second Affiliated Hospital, College of Biomedical Engineering and Instrument Science, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, 268 Kaixuan Road, Jianggan District, Hangzhou 310029, China
| | - Jiangrong Shen
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, Xihu District, Hangzhou 310027, China
| | - Jianing Han
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, Xihu District, Hangzhou 310027, China
| | - Xiaoxiao Song
- Department of Liberal Arts, School of Art Administration and Education, China Academy of Art, 218 Nanshan Road, Shangcheng District, Hangzhou 310002, China
| | - Yukun Lu
- Department of Neurology and Psychiatry of the Second Affiliated Hospital, College of Biomedical Engineering and Instrument Science, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, 268 Kaixuan Road, Jianggan District, Hangzhou 310029, China
| | - Huijia Zhan
- Department of Neurology and Psychiatry of the Second Affiliated Hospital, College of Biomedical Engineering and Instrument Science, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, 268 Kaixuan Road, Jianggan District, Hangzhou 310029, China
| | - Qianbing Li
- Department of Neurology and Psychiatry of the Second Affiliated Hospital, College of Biomedical Engineering and Instrument Science, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, 268 Kaixuan Road, Jianggan District, Hangzhou 310029, China
| | - Haoting Ge
- Department of Neurology and Psychiatry of the Second Affiliated Hospital, College of Biomedical Engineering and Instrument Science, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, 268 Kaixuan Road, Jianggan District, Hangzhou 310029, China
| | - Zheng Lin
- Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, 88 Jiefang Road, Shangcheng District, Hangzhou 310009, China
| | - Wenlei Shi
- Center for the Study of the History of Chinese Language and Center for the Study of Language and Cognition, Zhejiang University, 866 Yuhangtang Road, Xihu District, Hangzhou 310058, China
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Longwood Avenue 220, Boston, MA 02116, United States
| | - Huajin Tang
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, Xihu District, Hangzhou 310027, China
| | - Xiaodong Chen
- Department of Neurology and Psychiatry of the Second Affiliated Hospital, College of Biomedical Engineering and Instrument Science, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, 268 Kaixuan Road, Jianggan District, Hangzhou 310029, China
| |
Collapse
|
8
|
Noel JP, Bill J, Ding H, Vastola J, DeAngelis GC, Angelaki DE, Drugowitsch J. Causal inference during closed-loop navigation: parsing of self- and object-motion. bioRxiv 2023:2023.01.27.525974. [PMID: 36778376 PMCID: PMC9915492 DOI: 10.1101/2023.01.27.525974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
A key computation in building adaptive internal models of the external world is to ascribe sensory signals to their likely cause(s), a process of Bayesian Causal Inference (CI). CI is well studied within the framework of two-alternative forced-choice tasks, but less well understood within the cadre of naturalistic action-perception loops. Here, we examine the process of disambiguating retinal motion caused by self- and/or object-motion during closed-loop navigation. First, we derive a normative account specifying how observers ought to intercept hidden and moving targets given their belief over (i) whether retinal motion was caused by the target moving, and (ii) if so, with what velocity. Next, in line with the modeling results, we show that humans report targets as stationary and steer toward their initial rather than final position more often when they are themselves moving, suggesting a misattribution of object-motion to the self. Further, we predict that observers should misattribute retinal motion more often: (i) during passive rather than active self-motion (given the lack of an efference copy informing self-motion estimates in the former), and (ii) when targets are presented eccentrically rather than centrally (given that lateral self-motion flow vectors are larger at eccentric locations during forward self-motion). Results confirm both of these predictions. Lastly, analysis of eye-movements show that, while initial saccades toward targets are largely accurate regardless of the self-motion condition, subsequent gaze pursuit was modulated by target velocity during object-only motion, but not during concurrent object- and self-motion. These results demonstrate CI within action-perception loops, and suggest a protracted temporal unfolding of the computations characterizing CI.
Collapse
Affiliation(s)
- Jean-Paul Noel
- Center for Neural Science, New York University, New York City, NY, United States
| | - Johannes Bill
- Department of Neurobiology, Harvard Medical School, Boston, MA, United States
- Department of Psychology, Harvard University, Cambridge, MA, United States
| | - Haoran Ding
- Center for Neural Science, New York University, New York City, NY, United States
| | - John Vastola
- Department of Neurobiology, Harvard Medical School, Boston, MA, United States
| | - Gregory C. DeAngelis
- Department of Brain and Cognitive Sciences, Center for Visual Science, University of Rochester, Rochester, NY, United States
| | - Dora E. Angelaki
- Center for Neural Science, New York University, New York City, NY, United States
- Tandon School of Engineering, New York University, New York City, NY, United states
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, MA, United States
- Center for Brain Science, Harvard University, Boston, MA, United States
| |
Collapse
|
9
|
Drevet J, Drugowitsch J, Wyart V. Efficient stabilization of imprecise statistical inference through conditional belief updating. Nat Hum Behav 2022; 6:1691-1704. [PMID: 36138224 DOI: 10.1038/s41562-022-01445-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 08/11/2022] [Indexed: 01/14/2023]
Abstract
Statistical inference is the optimal process for forming and maintaining accurate beliefs about uncertain environments. However, human inference comes with costs due to its associated biases and limited precision. Indeed, biased or imprecise inference can trigger variable beliefs and unwarranted changes in behaviour. Here, by studying decisions in a sequential categorization task based on noisy visual stimuli, we obtained converging evidence that humans reduce the variability of their beliefs by updating them only when the reliability of incoming sensory information is judged as sufficiently strong. Instead of integrating the evidence provided by all stimuli, participants actively discarded as much as a third of stimuli. This conditional belief updating strategy shows good test-retest reliability, correlates with perceptual confidence and explains human behaviour better than previously described strategies. This seemingly suboptimal strategy not only reduces the costs of imprecise computations but also, counterintuitively, increases the accuracy of resulting decisions.
Collapse
Affiliation(s)
- Julie Drevet
- Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale (Inserm), Paris, France.
- Département d'Études Cognitives, École Normale Supérieure, Université PSL, Paris, France.
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Valentin Wyart
- Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale (Inserm), Paris, France.
- Département d'Études Cognitives, École Normale Supérieure, Université PSL, Paris, France.
| |
Collapse
|
10
|
Abstract
Identifying the structure of motion relations in the environment is critical for navigation, tracking, prediction, and pursuit. Yet, little is known about the mental and neural computations that allow the visual system to infer this structure online from a volatile stream of visual information. We propose online hierarchical Bayesian inference as a principled solution for how the brain might solve this complex perceptual task. We derive an online Expectation-Maximization algorithm that explains human percepts qualitatively and quantitatively for a diverse set of stimuli, covering classical psychophysics experiments, ambiguous motion scenes, and illusory motion displays. We thereby identify normative explanations for the origin of human motion structure perception and make testable predictions for future psychophysics experiments. The proposed online hierarchical inference model furthermore affords a neural network implementation which shares properties with motion-sensitive cortical areas and motivates targeted experiments to reveal the neural representations of latent structure.
Collapse
Affiliation(s)
- Johannes Bill
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA. .,Department of Psychology, Harvard University, Cambridge, MA, USA.
| | - Samuel J Gershman
- Department of Psychology, Harvard University, Cambridge, MA, USA.,Center for Brain Science, Harvard University, Cambridge, MA, USA.,Center for Brains, Minds, and Machines, MIT, Cambridge, MA, USA
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA.,Center for Brain Science, Harvard University, Cambridge, MA, USA
| |
Collapse
|
11
|
Rouault M, Weiss A, Lee JK, Drugowitsch J, Chambon V, Wyart V. Controllability boosts neural and cognitive signatures of changes-of-mind in uncertain environments. eLife 2022; 11:75038. [PMID: 36097814 PMCID: PMC9470160 DOI: 10.7554/elife.75038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
In uncertain environments, seeking information about alternative choice options is essential for adaptive learning and decision-making. However, information seeking is usually confounded with changes-of-mind about the reliability of the preferred option. Here, we exploited the fact that information seeking requires control over which option to sample to isolate its behavioral and neurophysiological signatures. We found that changes-of-mind occurring with control require more evidence against the current option, are associated with reduced confidence, but are nevertheless more likely to be confirmed on the next decision. Multimodal neurophysiological recordings showed that these changes-of-mind are preceded by stronger activation of the dorsal attention network in magnetoencephalography, and followed by increased pupil-linked arousal during the presentation of decision outcomes. Together, these findings indicate that information seeking increases the saliency of evidence perceived as the direct consequence of one's own actions.
Collapse
Affiliation(s)
- Marion Rouault
- Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale (Inserm), Paris, France.,Institut Jean Nicod, Centre National de la Recherche Scientifique (CNRS), Paris, France.,Département d'Études Cognitives, École Normale Supérieure, Université Paris Sciences et Lettres (PSL University), Paris, France
| | - Aurélien Weiss
- Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale (Inserm), Paris, France.,Département d'Études Cognitives, École Normale Supérieure, Université Paris Sciences et Lettres (PSL University), Paris, France.,Université de Paris, Paris, France
| | - Junseok K Lee
- Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale (Inserm), Paris, France.,Département d'Études Cognitives, École Normale Supérieure, Université Paris Sciences et Lettres (PSL University), Paris, France
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | - Valerian Chambon
- Institut Jean Nicod, Centre National de la Recherche Scientifique (CNRS), Paris, France.,Département d'Études Cognitives, École Normale Supérieure, Université Paris Sciences et Lettres (PSL University), Paris, France
| | - Valentin Wyart
- Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale (Inserm), Paris, France.,Département d'Études Cognitives, École Normale Supérieure, Université Paris Sciences et Lettres (PSL University), Paris, France
| |
Collapse
|
12
|
Kutschireiter A, Rast L, Drugowitsch J. Projection Filtering with Observed State Increments with Applications in Continuous-Time Circular Filtering. IEEE Trans Signal Process 2022; 70:686-700. [PMID: 36338544 PMCID: PMC9634992 DOI: 10.1109/tsp.2022.3143471] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Angular path integration is the ability of a system to estimate its own heading direction from potentially noisy angular velocity (or increment) observations. Non-probabilistic algorithms for angular path integration, which rely on a summation of these noisy increments, do not appropriately take into account the reliability of such observations, which is essential for appropriately weighing one's current heading direction estimate against incoming information. In a probabilistic setting, angular path integration can be formulated as a continuous-time nonlinear filtering problem (circular filtering) with observed state increments. The circular symmetry of heading direction makes this inference task inherently nonlinear, thereby precluding the use of popular inference algorithms such as Kalman filters, rendering the problem analytically inaccessible. Here, we derive an approximate solution to circular continuous-time filtering, which integrates state increment observations while maintaining a fixed representation through both state propagation and observational updates. Specifically, we extend the established projection-filtering method to account for observed state increments and apply this framework to the circular filtering problem. We further propose a generative model for continuous-time angular-valued direct observations of the hidden state, which we integrate seamlessly into the projection filter. Applying the resulting scheme to a model of probabilistic angular path integration, we derive an algorithm for circular filtering, which we term the circular Kalman filter. Importantly, this algorithm is analytically accessible, interpretable, and outperforms an alternative filter based on a Gaussian approximation.
Collapse
|
13
|
Krause EL, Drugowitsch J. A large majority of awake hippocampal sharp-wave ripples feature spatial trajectories with momentum. Neuron 2021; 110:722-733.e8. [PMID: 34863366 DOI: 10.1016/j.neuron.2021.11.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 10/06/2021] [Accepted: 11/12/2021] [Indexed: 01/02/2023]
Abstract
During periods of rest, hippocampal place cells feature bursts of activity called sharp-wave ripples (SWRs). Heuristic approaches have revealed that a small fraction of SWRs appear to "simulate" trajectories through the environment, called awake hippocampal replay. However, the functional role of a majority of these SWRs remains unclear. We find, using Bayesian model comparison of state-space models to characterize the spatiotemporal dynamics embedded in SWRs, that almost all SWRs of foraging rodents simulate such trajectories. Furthermore, these trajectories feature momentum, or inertia in their velocities, that mirrors the animals' natural movement, in contrast to replay events during sleep, which lack such momentum. Last, we show that past analyses of replayed trajectories for navigational planning were biased by the heuristic SWR sub-selection. Our findings thus identify the dominant function of awake SWRs as simulating trajectories with momentum and provide a principled foundation for future work on their computational function.
Collapse
Affiliation(s)
- Emma L Krause
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA.
| |
Collapse
|
14
|
Abstract
Traditional accumulation-to-bound decision-making models assume that all choice options are processed with equal attention. In real life decisions, however, humans alternate their visual fixation between individual items to efficiently gather relevant information (Yang et al., 2016). These fixations also causally affect one's choices, biasing them toward the longer-fixated item (Krajbich et al., 2010). We derive a normative decision-making model in which attention enhances the reliability of information, consistent with neurophysiological findings (Cohen and Maunsell, 2009). Furthermore, our model actively controls fixation changes to optimize information gathering. We show that the optimal model reproduces fixation-related choice biases seen in humans and provides a Bayesian computational rationale for this phenomenon. This insight led to additional predictions that we could confirm in human data. Finally, by varying the relative cognitive advantage conferred by attention, we show that decision performance is benefited by a balanced spread of resources between the attended and unattended items.
Collapse
Affiliation(s)
- Anthony I Jang
- Department of Neurobiology, Harvard Medical SchoolBostonUnited States
| | - Ravi Sharma
- Division of Biostatistics and Bioinformatics, Department of Family Medicine and Public Health, UC San Diego School of MedicineLa JollaUnited States
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical SchoolBostonUnited States
| |
Collapse
|
15
|
Kafashan M, Jaffe AW, Chettih SN, Nogueira R, Arandia-Romero I, Harvey CD, Moreno-Bote R, Drugowitsch J. Scaling of sensory information in large neural populations shows signatures of information-limiting correlations. Nat Commun 2021; 12:473. [PMID: 33473113 PMCID: PMC7817840 DOI: 10.1038/s41467-020-20722-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 12/16/2020] [Indexed: 01/29/2023] Open
Abstract
How is information distributed across large neuronal populations within a given brain area? Information may be distributed roughly evenly across neuronal populations, so that total information scales linearly with the number of recorded neurons. Alternatively, the neural code might be highly redundant, meaning that total information saturates. Here we investigate how sensory information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex. We show that information scales sublinearly due to correlated noise in these populations. We compartmentalized noise correlations into information-limiting and nonlimiting components, then extrapolate to predict how information grows with even larger neural populations. We predict that tens of thousands of neurons encode 95% of the information about visual stimulus direction, much less than the number of neurons in primary visual cortex. These findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most sensory information from smaller subpopulations.
Collapse
Affiliation(s)
| | - Anna W Jaffe
- Department of Neurobiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Selmaan N Chettih
- Department of Neurobiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Ramon Nogueira
- Center for Theoretical Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Iñigo Arandia-Romero
- ISAAC Lab, Aragón Institute of Engineering Research, University of Zaragoza, Zaragoza, Spain
- IAS-Research Center for Life, Mind, and Society, Department of Logic and Philosophy of Science, University of the Basque Country, UPV-EHU, Donostia-San Sebastián, Spain
| | | | - Rubén Moreno-Bote
- Center for Brain and Cognition and Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Serra Húnter Fellow Programme and ICREA Academia, Universitat Pompeu Fabra, Barcelona, Spain
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, MA, 02115, USA.
| |
Collapse
|
16
|
Lowet AS, Zheng Q, Matias S, Drugowitsch J, Uchida N. Distributional Reinforcement Learning in the Brain. Trends Neurosci 2020; 43:980-997. [PMID: 33092893 PMCID: PMC8073212 DOI: 10.1016/j.tins.2020.09.004] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 08/14/2020] [Accepted: 09/08/2020] [Indexed: 12/11/2022]
Abstract
Learning about rewards and punishments is critical for survival. Classical studies have demonstrated an impressive correspondence between the firing of dopamine neurons in the mammalian midbrain and the reward prediction errors of reinforcement learning algorithms, which express the difference between actual reward and predicted mean reward. However, it may be advantageous to learn not only the mean but also the complete distribution of potential rewards. Recent advances in machine learning have revealed a biologically plausible set of algorithms for reconstructing this reward distribution from experience. Here, we review the mathematical foundations of these algorithms as well as initial evidence for their neurobiological implementation. We conclude by highlighting outstanding questions regarding the circuit computation and behavioral readout of these distributional codes.
Collapse
Affiliation(s)
- Adam S Lowet
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Qiao Zheng
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Sara Matias
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA.
| | - Naoshige Uchida
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
| |
Collapse
|
17
|
Abstract
In multialternative risky choice, we are often faced with the opportunity to allocate our limited information-gathering capacity between several options before receiving feedback. In such cases, we face a natural trade-off between breadth-spreading our capacity across many options-and depth-gaining more information about a smaller number of options. Despite its broad relevance to daily life, including in many naturalistic foraging situations, the optimal strategy in the breadth-depth trade-off has not been delineated. Here, we formalize the breadth-depth dilemma through a finite-sample capacity model. We find that, if capacity is small (∼10 samples), it is optimal to draw one sample per alternative, favoring breadth. However, for larger capacities, a sharp transition is observed, and it becomes best to deeply sample a very small fraction of alternatives, which roughly decreases with the square root of capacity. Thus, ignoring most options, even when capacity is large enough to shallowly sample all of them, is a signature of optimal behavior. Our results also provide a rich casuistic for metareasoning in multialternative decisions with bounded capacity using close-to-optimal heuristics.
Collapse
Affiliation(s)
- Rubén Moreno-Bote
- Center for Brain and Cognition, Universitat Pompeu Fabra, 08002 Barcelona, Spain;
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08002 Barcelona, Spain
- Serra Húnter Fellow Programme, Universitat Pompeu Fabra, 08002 Barcelona, Spain
- Catalan Institution for Research and Advanced Studies-Academia, Universitat Pompeu Fabra, 08002 Barcelona, Spain
| | - Jorge Ramírez-Ruiz
- Center for Brain and Cognition, Universitat Pompeu Fabra, 08002 Barcelona, Spain
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08002 Barcelona, Spain
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115
| | - Benjamin Y Hayden
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN 55455
- Center for Neural Engineering, University of Minnesota, Minneapolis, MN 55455
| |
Collapse
|
18
|
Neubarth NL, Emanuel AJ, Liu Y, Springel MW, Handler A, Zhang Q, Lehnert BP, Guo C, Orefice LL, Abdelaziz A, DeLisle MM, Iskols M, Rhyins J, Kim SJ, Cattel SJ, Regehr W, Harvey CD, Drugowitsch J, Ginty DD. Meissner corpuscles and their spatially intermingled afferents underlie gentle touch perception. Science 2020; 368:eabb2751. [PMID: 32554568 PMCID: PMC7354383 DOI: 10.1126/science.abb2751] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 04/28/2020] [Indexed: 12/12/2022]
Abstract
Meissner corpuscles are mechanosensory end organs that densely occupy mammalian glabrous skin. We generated mice that selectively lacked Meissner corpuscles and found them to be deficient in both perceiving the gentlest detectable forces acting on glabrous skin and fine sensorimotor control. We found that Meissner corpuscles are innervated by two mechanoreceptor subtypes that exhibit distinct responses to tactile stimuli. The anatomical receptive fields of these two mechanoreceptor subtypes homotypically tile glabrous skin in a manner that is offset with respect to one another. Electron microscopic analysis of the two Meissner afferents within the corpuscle supports a model in which the extent of lamellar cell wrappings of mechanoreceptor endings determines their force sensitivity thresholds and kinetic properties.
Collapse
Affiliation(s)
- Nicole L Neubarth
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Alan J Emanuel
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Yin Liu
- Department of Biochemistry, Stanford University, 279 Campus Drive, Stanford, CA 94305, USA
- Howard Hughes Medical Institute, Stanford University, 279 Campus Drive, Stanford, CA 94305, USA
| | - Mark W Springel
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Annie Handler
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Qiyu Zhang
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Brendan P Lehnert
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Chong Guo
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Lauren L Orefice
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Amira Abdelaziz
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Michelle M DeLisle
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Michael Iskols
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Julia Rhyins
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Soo J Kim
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Stuart J Cattel
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Wade Regehr
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Christopher D Harvey
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - David D Ginty
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA.
- Howard Hughes Medical Institute, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| |
Collapse
|
19
|
Abstract
Diffusion decision models (DDMs) are immensely successful models for decision making under uncertainty and time pressure. In the context of perceptual decision making, these models typically start with two input units, organized in a neuron-antineuron pair. In contrast, in the brain, sensory inputs are encoded through the activity of large neuronal populations. Moreover, while DDMs are wired by hand, the nervous system must learn the weights of the network through trial and error. There is currently no normative theory of learning in DDMs and therefore no theory of how decision makers could learn to make optimal decisions in this context. Here, we derive such a rule for learning a near-optimal linear combination of DDM inputs based on trial-by-trial feedback. The rule is Bayesian in the sense that it learns not only the mean of the weights but also the uncertainty around this mean in the form of a covariance matrix. In this rule, the rate of learning is proportional (respectively, inversely proportional) to confidence for incorrect (respectively, correct) decisions. Furthermore, we show that, in volatile environments, the rule predicts a bias toward repeating the same choice after correct decisions, with a bias strength that is modulated by the previous choice's difficulty. Finally, we extend our learning rule to cases for which one of the choices is more likely a priori, which provides insights into how such biases modulate the mechanisms leading to optimal decisions in diffusion models.
Collapse
Affiliation(s)
- Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115;
| | - André G Mendonça
- Champalimaud Research, Champalimaud Centre for the Unknown, 1400-038 Lisbon, Portugal
| | - Zachary F Mainen
- Champalimaud Research, Champalimaud Centre for the Unknown, 1400-038 Lisbon, Portugal
| | - Alexandre Pouget
- Department of Basic Neuroscience, University of Geneva, CH-1211 Geneva, Switzerland
| |
Collapse
|
20
|
Shan H, Moreno-Bote R, Drugowitsch J. Family of closed-form solutions for two-dimensional correlated diffusion processes. Phys Rev E 2019; 100:032132. [PMID: 31640022 DOI: 10.1103/physreve.100.032132] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Indexed: 11/07/2022]
Abstract
Diffusion processes with boundaries are models of transport phenomena with wide applicability across many fields. These processes are described by their probability density functions (PDFs), which often obey Fokker-Planck equations (FPEs). While obtaining analytical solutions is often possible in the absence of boundaries, obtaining closed-form solutions to the FPE is more challenging once absorbing boundaries are present. As a result, analyses of these processes have largely relied on approximations or direct simulations. In this paper, we studied two-dimensional, time-homogeneous, spatially correlated diffusion with linear, axis-aligned, absorbing boundaries. Our main result is the explicit construction of a full family of closed-form solutions for their PDFs using the method of images. We found that such solutions can be built if and only if the correlation coefficient ρ between the two diffusing processes takes one of a numerable set of values. Using a geometric argument, we derived the complete set of ρ's where such solutions can be found. Solvable ρ's are given by ρ=-cos(π/k), where k∈Z^{+}∪{+∞}. Solutions were validated in simulations. Qualitative behaviors of the process appear to vary smoothly over ρ, allowing extrapolation from our solutions to cases with unsolvable ρ's.
Collapse
Affiliation(s)
- Haozhe Shan
- Program in Neuroscience, Harvard University, Boston, Massachusetts 02115, USA and Center for Brain Science, Harvard University, Cambridge, Massachusetts 02138, USA
| | - Rubén Moreno-Bote
- Department of Information and Communications Technologies, Pompeu Fabra University, 08002 Barcelona, Spain; Center for Brain and Cognition, Pompeu Fabra University, 08002 Barcelona, Spain; and Serra Húnter Fellow Programme, Pompeu Fabra University, 08002 Barcelona, Spain
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, Massachusetts 02115, USA and Center for Brain Science, Harvard University, Cambridge, Massachusetts 02138, USA
| |
Collapse
|
21
|
|
22
|
Xu C, Theisen E, Maloney R, Peng J, Santiago I, Yapp C, Werkhoven Z, Rumbaut E, Shum B, Tarnogorska D, Borycz J, Tan L, Courgeon M, Griffin T, Levin R, Meinertzhagen IA, de Bivort B, Drugowitsch J, Pecot MY. Control of Synaptic Specificity by Establishing a Relative Preference for Synaptic Partners. Neuron 2019; 103:865-877.e7. [PMID: 31300277 PMCID: PMC6728174 DOI: 10.1016/j.neuron.2019.06.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Revised: 04/19/2019] [Accepted: 06/11/2019] [Indexed: 02/07/2023]
Abstract
The ability of neurons to identify correct synaptic partners is fundamental to the proper assembly and function of neural circuits. Relative to other steps in circuit formation such as axon guidance, our knowledge of how synaptic partner selection is regulated is severely limited. Drosophila Dpr and DIP immunoglobulin superfamily (IgSF) cell-surface proteins bind heterophilically and are expressed in a complementary manner between synaptic partners in the visual system. Here, we show that in the lamina, DIP mis-expression is sufficient to promote synapse formation with Dpr-expressing neurons and that disrupting DIP function results in ectopic synapse formation. These findings indicate that DIP proteins promote synapses to form between specific cell types and that in their absence, neurons synapse with alternative partners. We propose that neurons have the capacity to synapse with a broad range of cell types and that synaptic specificity is achieved by establishing a preference for specific partners.
Collapse
Affiliation(s)
- Chundi Xu
- Department of Neurobiology, Harvard Medical School, 220 Longwood Ave, Boston, MA 02115, USA.
| | - Emma Theisen
- Department of Neurobiology, Harvard Medical School, 220 Longwood Ave, Boston, MA 02115, USA
| | - Ryan Maloney
- Department of Neurobiology, Harvard Medical School, 220 Longwood Ave, Boston, MA 02115, USA
| | - Jing Peng
- Department of Neurobiology, Harvard Medical School, 220 Longwood Ave, Boston, MA 02115, USA
| | - Ivan Santiago
- Department of Neurobiology, Harvard Medical School, 220 Longwood Ave, Boston, MA 02115, USA
| | - Clarence Yapp
- Image and Data Analysis Core, Harvard Medical School, Boston, MA 02115, USA
| | - Zachary Werkhoven
- Center for Brain Science and Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Elijah Rumbaut
- Department of Neurobiology, Harvard Medical School, 220 Longwood Ave, Boston, MA 02115, USA
| | - Bryan Shum
- Department of Neurobiology, Harvard Medical School, 220 Longwood Ave, Boston, MA 02115, USA
| | - Dorota Tarnogorska
- Department of Psychology and Neuroscience, Life Sciences Centre, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Jolanta Borycz
- Department of Psychology and Neuroscience, Life Sciences Centre, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Liming Tan
- Department of Biological Chemistry, HHMI, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Maximilien Courgeon
- Department of Biology, New York University, 100 Washington Square East, New York, NY 10003, USA
| | - Tessa Griffin
- Department of Neurobiology, Harvard Medical School, 220 Longwood Ave, Boston, MA 02115, USA
| | - Raina Levin
- Department of Neurobiology, Harvard Medical School, 220 Longwood Ave, Boston, MA 02115, USA
| | - Ian A Meinertzhagen
- Department of Psychology and Neuroscience, Life Sciences Centre, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Benjamin de Bivort
- Center for Brain Science and Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, 220 Longwood Ave, Boston, MA 02115, USA
| | - Matthew Y Pecot
- Department of Neurobiology, Harvard Medical School, 220 Longwood Ave, Boston, MA 02115, USA.
| |
Collapse
|
23
|
Nogueira R, Abolafia JM, Drugowitsch J, Balaguer-Ballester E, Sanchez-Vives MV, Moreno-Bote R. Lateral orbitofrontal cortex anticipates choices and integrates prior with current information. Nat Commun 2017; 8:14823. [PMID: 28337990 PMCID: PMC5376669 DOI: 10.1038/ncomms14823] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 02/06/2017] [Indexed: 12/28/2022] Open
Abstract
Adaptive behavior requires integrating prior with current information to anticipate upcoming events. Brain structures related to this computation should bring relevant signals from the recent past into the present. Here we report that rats can integrate the most recent prior information with sensory information, thereby improving behavior on a perceptual decision-making task with outcome-dependent past trial history. We find that anticipatory signals in the orbitofrontal cortex about upcoming choice increase over time and are even present before stimulus onset. These neuronal signals also represent the stimulus and relevant second-order combinations of past state variables. The encoding of choice, stimulus and second-order past state variables resides, up to movement onset, in overlapping populations. The neuronal representation of choice before stimulus onset and its build-up once the stimulus is presented suggest that orbitofrontal cortex plays a role in transforming immediate prior and stimulus information into choices using a compact state-space representation. The orbitofrontal cortex encodes outcomes, expected rewards and values, but it is unclear how this region uses this information to inform action selection. Here, the authors show that lateral orbitofrontal cortex anticipates upcoming choices and combines recent prior information with current sensory information.
Collapse
Affiliation(s)
- Ramon Nogueira
- Center for Brain and Cognition and Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona 08018, Spain.,Research Unit, Parc Sanitari Sant Joan de Déu, Esplugues de Llobregat, Barcelona 08950, Spain
| | - Juan M Abolafia
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain
| | - Jan Drugowitsch
- Département des Neurosciences Fondamentales, Université de Genève, Geneva 4 1211, Switzerland.,Department of Neurobiology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Emili Balaguer-Ballester
- Department of Computing and Informatics, Faculty of Science and Technology, Bournemouth University, Poole BH12 5BB, UK.,Bernstein Center for Computational Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim D-68159, Germany
| | - Maria V Sanchez-Vives
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain.,ICREA, Barcelona 08010, Spain
| | - Rubén Moreno-Bote
- Center for Brain and Cognition and Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona 08018, Spain.,Research Unit, Parc Sanitari Sant Joan de Déu, Esplugues de Llobregat, Barcelona 08950, Spain.,Serra Húnter Fellow Programme, Universitat Pompeu Fabra, Barcelona 08018, Spain
| |
Collapse
|
24
|
Drugowitsch J, Wyart V, Devauchelle AD, Koechlin E. Computational Precision of Mental Inference as Critical Source of Human Choice Suboptimality. Neuron 2016. [PMID: 27916454 DOI: 10.1016/j.neuron.2016.11.005.] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Making decisions in uncertain environments often requires combining multiple pieces of ambiguous information from external cues. In such conditions, human choices resemble optimal Bayesian inference, but typically show a large suboptimal variability whose origin remains poorly understood. In particular, this choice suboptimality might arise from imperfections in mental inference rather than in peripheral stages, such as sensory processing and response selection. Here, we dissociate these three sources of suboptimality in human choices based on combining multiple ambiguous cues. Using a novel quantitative approach for identifying the origin and structure of choice variability, we show that imperfections in inference alone cause a dominant fraction of suboptimal choices. Furthermore, two-thirds of this suboptimality appear to derive from the limited precision of neural computations implementing inference rather than from systematic deviations from Bayes-optimal inference. These findings set an upper bound on the accuracy and ultimate predictability of human choices in uncertain environments.
Collapse
Affiliation(s)
- Jan Drugowitsch
- Laboratoire de Neurosciences Cognitives, Inserm unit 960, Département d'Études Cognitives, École Normale Supérieure, PSL Research University, 75005 Paris, France; Département des Neurosciences Fondamentales, Université de Genève, CH-1211 Geneva, Switzerland; Department of Neurobiology, Harvard Medical School, Boston, MA 24615, USA.
| | - Valentin Wyart
- Laboratoire de Neurosciences Cognitives, Inserm unit 960, Département d'Études Cognitives, École Normale Supérieure, PSL Research University, 75005 Paris, France.
| | - Anne-Dominique Devauchelle
- Laboratoire de Neurosciences Cognitives, Inserm unit 960, Département d'Études Cognitives, École Normale Supérieure, PSL Research University, 75005 Paris, France
| | - Etienne Koechlin
- Laboratoire de Neurosciences Cognitives, Inserm unit 960, Département d'Études Cognitives, École Normale Supérieure, PSL Research University, 75005 Paris, France
| |
Collapse
|
25
|
Drugowitsch J, Wyart V, Devauchelle AD, Koechlin E. Computational Precision of Mental Inference as Critical Source of Human Choice Suboptimality. Neuron 2016; 92:1398-1411. [PMID: 27916454 DOI: 10.1016/j.neuron.2016.11.005] [Citation(s) in RCA: 125] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Revised: 08/04/2016] [Accepted: 10/28/2016] [Indexed: 11/21/2022]
Affiliation(s)
- Jan Drugowitsch
- Laboratoire de Neurosciences Cognitives, Inserm unit 960, Département d'Études Cognitives, École Normale Supérieure, PSL Research University, 75005 Paris, France; Département des Neurosciences Fondamentales, Université de Genève, CH-1211 Geneva, Switzerland; Department of Neurobiology, Harvard Medical School, Boston, MA 24615, USA.
| | - Valentin Wyart
- Laboratoire de Neurosciences Cognitives, Inserm unit 960, Département d'Études Cognitives, École Normale Supérieure, PSL Research University, 75005 Paris, France.
| | - Anne-Dominique Devauchelle
- Laboratoire de Neurosciences Cognitives, Inserm unit 960, Département d'Études Cognitives, École Normale Supérieure, PSL Research University, 75005 Paris, France
| | - Etienne Koechlin
- Laboratoire de Neurosciences Cognitives, Inserm unit 960, Département d'Études Cognitives, École Normale Supérieure, PSL Research University, 75005 Paris, France
| |
Collapse
|
26
|
Pouget A, Drugowitsch J, Kepecs A. Confidence and certainty: distinct probabilistic quantities for different goals. Nat Neurosci 2016; 19:366-74. [PMID: 26906503 PMCID: PMC5378479 DOI: 10.1038/nn.4240] [Citation(s) in RCA: 231] [Impact Index Per Article: 28.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2015] [Accepted: 01/08/2016] [Indexed: 11/09/2022]
Abstract
When facing uncertainty, adaptive behavioral strategies demand that the brain performs probabilistic computations. In this probabilistic framework, the notion of certainty and confidence would appear to be closely related, so much so that it is tempting to conclude that these two concepts are one and the same. We argue that there are computational reasons to distinguish between these two concepts. Specifically, we propose that confidence should be defined as the probability that a decision or a proposition, overt or covert, is correct given the evidence, a critical quantity in complex sequential decisions. We suggest that the term certainty should be reserved to refer to the encoding of all other probability distributions over sensory and cognitive variables. We also discuss strategies for studying the neural codes for confidence and certainty and argue that clear definitions of neural codes are essential to understanding the relative contributions of various cortical areas to decision making.
Collapse
Affiliation(s)
- Alexandre Pouget
- Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York, USA
- Gatsby Computational Neuroscience Unit, London, UK
| | - Jan Drugowitsch
- Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
| | - Adam Kepecs
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA
| |
Collapse
|
27
|
Arandia-Romero I, Tanabe S, Drugowitsch J, Kohn A, Moreno-Bote R. Multiplicative and Additive Modulation of Neuronal Tuning with Population Activity Affects Encoded Information. Neuron 2016; 89:1305-1316. [PMID: 26924437 DOI: 10.1016/j.neuron.2016.01.044] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Revised: 12/09/2015] [Accepted: 01/16/2016] [Indexed: 10/22/2022]
Abstract
Numerous studies have shown that neuronal responses are modulated by stimulus properties and also by the state of the local network. However, little is known about how activity fluctuations of neuronal populations modulate the sensory tuning of cells and affect their encoded information. We found that fluctuations in ongoing and stimulus-evoked population activity in primate visual cortex modulate the tuning of neurons in a multiplicative and additive manner. While distributed on a continuum, neurons with stronger multiplicative effects tended to have less additive modulation and vice versa. The information encoded by multiplicatively modulated neurons increased with greater population activity, while that of additively modulated neurons decreased. These effects offset each other so that population activity had little effect on total information. Our results thus suggest that intrinsic activity fluctuations may act as a "traffic light" that determines which subset of neurons is most informative.
Collapse
Affiliation(s)
- Iñigo Arandia-Romero
- Department of Information and Communication Technologies, Universidad Pompeu Fabra, Barcelona 08018, Spain; Research Unit, Parc Sanitari Sant Joan de Deu, Esplugues de Llobregat, Barcelona 08950, Spain
| | - Seiji Tanabe
- Dominick Purpura Department of Neuroscience and Ophthalmology and Visual Science, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Jan Drugowitsch
- Département des Neurosciences Fondamentales, Université de Genève, 1211 Geneva 4, Switzerland
| | - Adam Kohn
- Dominick Purpura Department of Neuroscience and Ophthalmology and Visual Science, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Rubén Moreno-Bote
- Department of Information and Communication Technologies, Universidad Pompeu Fabra, Barcelona 08018, Spain; Research Unit, Parc Sanitari Sant Joan de Deu, Esplugues de Llobregat, Barcelona 08950, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Esplugues de Llobregat, Barcelona 08950, Spain; Serra Húnter Fellow Programme, Universidad Pompeu Fabra, Barcelona 08018, Spain.
| |
Collapse
|
28
|
Drugowitsch J. Fast and accurate Monte Carlo sampling of first-passage times from Wiener diffusion models. Sci Rep 2016; 6:20490. [PMID: 26864391 PMCID: PMC4750067 DOI: 10.1038/srep20490] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2015] [Accepted: 01/05/2016] [Indexed: 11/09/2022] Open
Abstract
We present a new, fast approach for drawing boundary crossing samples from Wiener diffusion models. Diffusion models are widely applied to model choices and reaction times in two-choice decisions. Samples from these models can be used to simulate the choices and reaction times they predict. These samples, in turn, can be utilized to adjust the models' parameters to match observed behavior from humans and other animals. Usually, such samples are drawn by simulating a stochastic differential equation in discrete time steps, which is slow and leads to biases in the reaction time estimates. Our method, instead, facilitates known expressions for first-passage time densities, which results in unbiased, exact samples and a hundred to thousand-fold speed increase in typical situations. In its most basic form it is restricted to diffusion models with symmetric boundaries and non-leaky accumulation, but our approach can be extended to also handle asymmetric boundaries or to approximate leaky accumulation.
Collapse
Affiliation(s)
- Jan Drugowitsch
- University of Geneva, Department of Basic Neuroscience, 1211 Geneva, Switzerland
| |
Collapse
|
29
|
Abstract
While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification.
Collapse
Affiliation(s)
- Rubén Moreno-Bote
- Department of Technologies of Information and Communication, University Pompeu Fabra, 08018 Barcelona, Spain.,Serra Húnter Fellow Programme, 08018, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), 08018, Barcelona, Spain
| | - Jan Drugowitsch
- Department of Basic Neuroscience, University of Geneva, Switzerland
| |
Collapse
|
30
|
Drugowitsch J, DeAngelis GC, Angelaki DE, Pouget A. Tuning the speed-accuracy trade-off to maximize reward rate in multisensory decision-making. eLife 2015; 4:e06678. [PMID: 26090907 PMCID: PMC4487075 DOI: 10.7554/elife.06678] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Accepted: 06/18/2015] [Indexed: 01/04/2023] Open
Abstract
For decisions made under time pressure, effective decision making based on uncertain or ambiguous evidence requires efficient accumulation of evidence over time, as well as appropriately balancing speed and accuracy, known as the speed/accuracy trade-off. For simple unimodal stimuli, previous studies have shown that human subjects set their speed/accuracy trade-off to maximize reward rate. We extend this analysis to situations in which information is provided by multiple sensory modalities. Analyzing previously collected data (Drugowitsch et al., 2014), we show that human subjects adjust their speed/accuracy trade-off to produce near-optimal reward rates. This trade-off can change rapidly across trials according to the sensory modalities involved, suggesting that it is represented by neural population codes rather than implemented by slow neuronal mechanisms such as gradual changes in synaptic weights. Furthermore, we show that deviations from the optimal speed/accuracy trade-off can be explained by assuming an incomplete gradient-based learning of these trade-offs.
Collapse
Affiliation(s)
- Jan Drugowitsch
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, United States
- Institut National de la Santé et de la Recherche Médicale, École Normale 12 Supérieure, Paris, France
- Département des Neurosciences Fondamentales, Université de Genève, Geneva, Switzerland
| | - Gregory C DeAngelis
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, United States
| | - Dora E Angelaki
- Department of Neuroscience, Baylor College of Medicine, Houston, United States
| | - Alexandre Pouget
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, United States
- Département des Neurosciences Fondamentales, Université de Genève, Geneva, Switzerland
| |
Collapse
|
31
|
Kneissler J, Drugowitsch J, Friston K, Butz MV. Simultaneous learning and filtering without delusions: a Bayes-optimal combination of Predictive Inference and Adaptive Filtering. Front Comput Neurosci 2015; 9:47. [PMID: 25983690 PMCID: PMC4415408 DOI: 10.3389/fncom.2015.00047] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Accepted: 04/05/2015] [Indexed: 11/13/2022] Open
Abstract
Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF). PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than 10-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.
Collapse
Affiliation(s)
- Jan Kneissler
- Chair of Cognitive Modeling, Department of Computer Science, Faculty of Science, Eberhard Karls University of Tübingen Tübingen, Germany
| | - Jan Drugowitsch
- Départment des Neurosciences Fondamentales, Université de Genève Geneva, Switzerland
| | - Karl Friston
- The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London London, UK
| | - Martin V Butz
- Chair of Cognitive Modeling, Department of Computer Science, Faculty of Science, Eberhard Karls University of Tübingen Tübingen, Germany
| |
Collapse
|
32
|
Abstract
Humans and animals can integrate sensory evidence from various sources to make decisions in a statistically near-optimal manner, provided that the stimulus presentation time is fixed across trials. Little is known about whether optimality is preserved when subjects can choose when to make a decision (reaction-time task), nor when sensory inputs have time-varying reliability. Using a reaction-time version of a visual/vestibular heading discrimination task, we show that behavior is clearly sub-optimal when quantified with traditional optimality metrics that ignore reaction times. We created a computational model that accumulates evidence optimally across both cues and time, and trades off accuracy with decision speed. This model quantitatively explains subjects's choices and reaction times, supporting the hypothesis that subjects do, in fact, accumulate evidence optimally over time and across sensory modalities, even when the reaction time is under the subject's control.
Collapse
Affiliation(s)
- Jan Drugowitsch
- Department of Brain and Cognitive Sciences, University of Rochester, New York, United States
- Institut National de la Santé et de la Recherche Médicale, École Normale Supérieure, Paris, France
- Département des Neurosciences Fondamentales, Université de Genève, Geneva, Switzerland
| | - Gregory C DeAngelis
- Department of Brain and Cognitive Sciences, University of Rochester, New York, United States
| | - Eliana M Klier
- Department of Neuroscience, Baylor College of Medicine, Houston, United States
| | - Dora E Angelaki
- Department of Neuroscience, Baylor College of Medicine, Houston, United States
| | - Alexandre Pouget
- Department of Brain and Cognitive Sciences, University of Rochester, New York, United States
- Département des Neurosciences Fondamentales, Université de Genève, Geneva, Switzerland
| |
Collapse
|
33
|
Abstract
In an uncertain and ambiguous world, effective decision making requires that subjects form and maintain a belief about the correctness of their choices, a process called meta-cognition. Prediction of future outcomes and self-monitoring are only effective if belief closely matches behavioral performance. Equality between belief and performance is also critical for experimentalists to gain insight into the subjects' belief by simply measuring their performance. Assuming that the decision maker holds the correct model of the world, one might indeed expect that belief and performance should go hand in hand. Unfortunately, we show here that this is rarely the case when performance is defined as the percentage of correct responses for a fixed stimulus, a standard definition in psychophysics. In this case, belief equals performance only for a very narrow family of tasks, whereas in others they will only be very weakly correlated. As we will see it is possible to restore this equality in specific circumstances but this remedy is only effective for a decision-maker, not for an experimenter. We furthermore show that belief and performance do not match when conditioned on task difficulty--as is common practice when plotting the psychometric curve--highlighting common pitfalls in previous neuroscience work. Finally, we demonstrate that miscalibration and the hard-easy effect observed in humans' and other animals' certainty judgments could be explained by a mismatch between the experimenter's and decision maker's expected distribution of task difficulties. These results have important implications for experimental design and are of relevance for theories that aim to unravel the nature of meta-cognition.
Collapse
Affiliation(s)
- Jan Drugowitsch
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York, United States of America
- Institut National de la Santé et de la Recherche Médicale, École Normale Supérieure, Paris, France
- Département des Neurosciences Fondamentales, Université de Genève, Geneva, Switzerland
| | - Rubén Moreno-Bote
- Research Unit, Parc Sanitari Sant Joan de Déu and Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
| | - Alexandre Pouget
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York, United States of America
- Département des Neurosciences Fondamentales, Université de Genève, Geneva, Switzerland
| |
Collapse
|
34
|
Kneissler J, Stalph PO, Drugowitsch J, Butz MV. Filtering sensory information with XCSF: improving learning robustness and robot arm control performance. Evol Comput 2013; 22:139-158. [PMID: 23746295 DOI: 10.1162/evco_a_00108] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
It has been shown previously that the control of a robot arm can be efficiently learned using the XCSF learning classifier system, which is a nonlinear regression system based on evolutionary computation. So far, however, the predictive knowledge about how actual motor activity changes the state of the arm system has not been exploited. In this paper, we utilize the forward velocity kinematics knowledge of XCSF to alleviate the negative effect of noisy sensors for successful learning and control. We incorporate Kalman filtering for estimating successive arm positions, iteratively combining sensory readings with XCSF-based predictions of hand position changes over time. The filtered arm position is used to improve both trajectory planning and further learning of the forward velocity kinematics. We test the approach on a simulated kinematic robot arm model. The results show that the combination can improve learning and control performance significantly. However, it also shows that variance estimates of XCSF prediction may be underestimated, in which case self-delusional spiraling effects can hinder effective learning. Thus, we introduce a heuristic parameter, which can be motivated by theory, and which limits the influence of XCSF's predictions on its own further learning input. As a result, we obtain drastic improvements in noise tolerance, allowing the system to cope with more than 10 times higher noise levels.
Collapse
Affiliation(s)
- Jan Kneissler
- Department of Computer Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany
| | | | | | | |
Collapse
|
35
|
Arandia-Romero I, Drugowitsch J, Kohn A, Moreno-Bote R. Spontaneous population activity fluctuations boost sensory tuning curves and gate information processing. BMC Neurosci 2013. [PMCID: PMC3704598 DOI: 10.1186/1471-2202-14-s1-p281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Iñigo Arandia-Romero
- Research Unit, Parc Sanitari Sant Joan de Deu and Universitat de Barcelona, Esplugues de Llobregat, Barcelona, 08950, Spain,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Esplugues de Llobregat, Barcelona, 08950, Spain
| | - Jan Drugowitsch
- Institut National de la Santé et de la Recherche Médicale & École Normale Supérieure, Paris, 75005, France
| | - Adam Kohn
- Dominick Purpura Department of Neuroscience,Ophthalmology and Visual Science, Albert Einstein College of Medicine, Bronx, New York, 10461, USA
| | - Rubén Moreno-Bote
- Research Unit, Parc Sanitari Sant Joan de Deu and Universitat de Barcelona, Esplugues de Llobregat, Barcelona, 08950, Spain,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Esplugues de Llobregat, Barcelona, 08950, Spain
| |
Collapse
|
36
|
Nogueira R, Drugowitsch J, Navarra J, Moreno-Bote R. Trial by trial decoding of decisions in monkey MT cortex from small neuronal populations. BMC Neurosci 2013. [PMCID: PMC3704555 DOI: 10.1186/1471-2202-14-s1-p280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
|
37
|
Drugowitsch J, Pouget A. Probabilistic vs. non-probabilistic approaches to the neurobiology of perceptual decision-making. Curr Opin Neurobiol 2012; 22:963-9. [PMID: 22884815 DOI: 10.1016/j.conb.2012.07.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2012] [Revised: 07/19/2012] [Accepted: 07/19/2012] [Indexed: 10/28/2022]
Abstract
Optimal binary perceptual decision making requires accumulation of evidence in the form of a probability distribution that specifies the probability of the choices being correct given the evidence so far. Reward rates can then be maximized by stopping the accumulation when the confidence about either option reaches a threshold. Behavioral and neuronal evidence suggests that humans and animals follow such a probabilitistic decision strategy, although its neural implementation has yet to be fully characterized. Here we show that that diffusion decision models and attractor network models provide an approximation to the optimal strategy only under certain circumstances. In particular, neither model type is sufficiently flexible to encode the reliability of both the momentary and the accumulated evidence, which is a pre-requisite to accumulate evidence of time-varying reliability. Probabilistic population codes, by contrast, can encode these quantities and, as a consequence, have the potential to implement the optimal strategy accurately.
Collapse
Affiliation(s)
- Jan Drugowitsch
- Institut National de la Santé et de la Recherche Médicale, École Normale Supérieure, 75005 Paris, France
| | | |
Collapse
|
38
|
Loiacono D, Drugowitsch J, Barry A, Lanzi PL. Analysis and Improvements of the Classifier Error Estimate in XCSF. Lecture Notes in Computer Science 2008. [DOI: 10.1007/978-3-540-88138-4_7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
|