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An illusion predicted by V1 population activity implicates cortical topography in shape perception

NATURE NEUROSCIENCE 2013年第10期16卷 1477-+页

作者： Michel, Melchi M. Chen, Yuzhi Geisler, Wilson S. Seidemann, Eyal Rutgers State Univ Dept Psychol New Brunswick NJ USA Univ Texas Austin Ctr Perceptual Syst Austin TX 78712 USA Univ Texas Austin Dept Psychol Austin TX 78712 USA Univ Texas Austin Neurobiol Sect Austin TX 78712 USA

Mammalian primary visual cortex (V1) is topographically organized such that the pattern of neural activation in V1 reflects the location and spatial extent of visual elements in the retinal image, but it is unclear whether this organization contributes to visual perception. We combined computational modeling, voltage-sensitive dye imaging (VSDI) in behaving monkeys and behavioral measurements in humans to investigate whether the large-scale topography of V1 population responses influences shape judgments. Specifically, we used a computational model to design visual stimuli that had the same physical shape, but were predicted to elicit variable V1 response spread. We confirmed these predictions with VSDI. Finally, we designed a behavioral task in which human observers judged the shapes of these stimuli and found that their judgments were systematically distorted by the spread of V1 activity. This illusion suggests that the topographic pattern of neural population responses in visual cortex contributes to visual perception.

关键词： neural decoding Striate cortex

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Deep brain stimulation-guided optogenetic rescue of parkinsonian symptoms

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NATURE COMMUNICATIONS 2020年第1期11卷 1-17页

作者： Valverde, Sebastien Vandecasteele, Marie Piette, Charlotte Derousseaux, Willy Gangarossa, Giuseppe Aristieta Arbelaiz, Asier Touboul, Jonathan Degos, Bertrand Venance, Laurent Coll France Dynam & Pathophysiol Neuronal Networks Team Ctr Interdisciplinary Res Biol CNRSUMR7241INSERMU1050MemoLife Labex F-75005 Paris France Brandeis Univ Dept Math Waltham MA 02254 USA Brandeis Univ Volen Natl Ctr Complex Syst Waltham MA USA Sorbonne Paris Nord Univ Avicenne Univ Hosp Dept Neurol F-93009 Bobigny France

Deep brain stimulation (DBS) of the subthalamic nucleus is a symptomatic treatment of Parkinson's disease but benefits only to a minority of patients due to stringent eligibility criteria. To investigate new targets for less invasive therapies, we aimed at elucidating key mechanisms supporting deep brain stimulation efficiency. Here, using in vivo electrophysiology, optogenetics, behavioral tasks and mathematical modeling, we found that subthalamic stimulation normalizes pathological hyperactivity of motor cortex pyramidal cells, while concurrently activating somatostatin and inhibiting parvalbumin interneurons. In vivo opto-activation of cortical somatostatin interneurons alleviates motor symptoms in a parkinsonian mouse model. A computational model highlights that a decrease in pyramidal neuron activity induced by DBS or by a stimulation of cortical somatostatin interneurons can restore information processing capabilities. Overall, these results demonstrate that activation of cortical somatostatin interneurons may constitute a less invasive alternative than subthalamic stimulation. Deep brain stimulation (DBS) is a symptomatic treatment of Parkinson's disease (PD) that benefits only a minority of patients. Here, the authors show that activation of cortical somatostatin interneurons alleviates motor symptoms in a mouse model of PD and may constitute a less invasive alternative than DBS.

关键词： Motor cortex neural decoding Parkinson's disease

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decoding individual differences in STEM learning from functional MRI data

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NATURE COMMUNICATIONS 2019年第1期10卷 2027页

作者： Cetron, Joshua S. Connolly, Andrew C. Diamond, Solomon G. May, Vicki V. Haxby, James, V Kraemer, David J. M. Dartmouth Coll Dept Educ Hanover NH 03755 USA Harvard Univ Dept Psychol 33 Kirkland St Cambridge MA 02138 USA Dartmouth Coll Dept Psychol & Brain Sci Hanover NH 03755 USA Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA

Traditional tests of concept knowledge generate scores to assess how well a learner understands a concept. Here, we investigated whether patterns of brain activity collected during a concept knowledge task could be used to compute a neural 'score' to complement traditional scores of an individual's conceptual understanding. Using a novel data-driven multivariate neuroimaging approach-informational network analysis-we successfully derived a neural score from patterns of activity across the brain that predicted individual differences in multiple concept knowledge tasks in the physics and engineering domain. These tasks include an fMRI paradigm, as well as two other previously validated concept inventories. The informational network score outperformed alternative neural scores computed using data-driven neuroimaging methods, including multivariate representational similarity analysis. This technique could be applied to quantify concept knowledge in a wide range of domains, including classroom-based education research, machine learning, and other areas of cognitive science.

关键词： Education Human behaviour neural decoding Problem solving

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decoding motor plans using a closed-loop ultrasonic brain-machine interface

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NATURE NEUROSCIENCE 2024年第1期27卷 196-207页

作者： Griggs, Whitney S. Norman, Sumner L. Deffieux, Thomas Segura, Florian Osmanski, Bruno-Felix Chau, Geeling Christopoulos, Vasileios Liu, Charles Tanter, Mickael Shapiro, Mikhail G. Andersen, Richard A. CALTECH Div Biol & Biol Engn Pasadena CA 91125 USA Univ California Los Angeles David Geffen Sch Med Los Angeles CA 90095 USA PSL Res Univ Phys Med Paris INSERM ESPCI ParisCNRS Paris France Inserm Technol Res Accelerator Biomed Ultrasound Paris France Iconeus Paris France CALTECH T&C Chen Brain Machine Interface Ctr Pasadena CA USA Univ Calif Riverside Dept Bioengn Riverside CA USA USC Dept Neurol Surg Keck Sch Med Los Angeles CA USA USC USC Neurorestorat Ctr Keck Sch Med Los Angeles CA USA Rancho Los Amigos Natl Rehabil Ctr Downey CA USA CALTECH Div Chem & Chem Engn Pasadena CA USA CALTECH Andrew & Peggy Cherng Dept Med Engn Pasadena CA USA Howard Hughes Med Inst Pasadena CA USA

Brain-machine interfaces (BMIs) enable people living with chronic paralysis to control computers, robots and more with nothing but thought. Existing BMIs have trade-offs across invasiveness, performance, spatial coverage and spatiotemporal resolution. Functional ultrasound (fUS) neuroimaging is an emerging technology that balances these attributes and may complement existing BMI recording technologies. In this study, we use fUS to demonstrate a successful implementation of a closed-loop ultrasonic BMI. We streamed fUS data from the posterior parietal cortex of two rhesus macaque monkeys while they performed eye and hand movements. After training, the monkeys controlled up to eight movement directions using the BMI. We also developed a method for pretraining the BMI using data from previous sessions. This enabled immediate control on subsequent days, even those that occurred months apart, without requiring extensive recalibration. These findings establish the feasibility of ultrasonic BMIs, paving the way for a new class of less-invasive (epidural) interfaces that generalize across extended time periods and promise to restore function to people with neurological impairments. Griggs, Norman et al. demonstrate a closed-loop ultrasound-based brain-machine interface (BMI) in rhesus macaques. The ultrasound BMI is capable of controlling eight independent movement directions and is stable across months without retraining.

关键词： Brain–machine interface Motor control neural decoding Ultrasound

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Population decoding of motor cortical activity using a generalized linear model with hidden states

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JOURNAL OF NEUROSCIENCE METHODS 2010年第2期189卷 267-280页

作者： Lawhern, Vernon Wu, Wei Hatsopoulos, Nicholas Paninski, Liam Florida State Univ Dept Stat Tallahassee FL 32306 USA Univ Chicago Dept Organismal Biol & Anat Comm Computat Neurosci Chicago IL 60637 USA Univ Chicago Dept Organismal Biol & Anat Comm Neurobiol Chicago IL 60637 USA Columbia Univ Dept Stat New York NY 10027 USA

Generalized linear models (GLMs) have been developed for modeling and decoding population neuronal spiking activity in the motor cortex. These models provide reasonable characterizations between neural activity and motor behavior. However, they lack a description of movement-related terms which are not observed directly in these experiments, such as muscular activation, the subject's level of attention, and other internal or external states. Here we propose to include a multi-dimensional hidden state to address these states in a GLM framework where the spike count at each time is described as a function of the hand state (position, velocity, and acceleration), truncated spike history, and the hidden state. The model can be identified by an Expectation-Maximization algorithm. We tested this new method in two datasets where spikes were simultaneously recorded using a multi-electrode array in the primary motor cortex of two monkeys. It was found that this method significantly improves the model-fitting over the classical GLM, for hidden dimensions varying from 1 to 4. This method also provides more accurate decoding of hand state (reducing the mean square error by up to 29% in some cases), while retaining real-time computational efficiency. These improvements on representation and decoding over the classical GLM model suggest that this new approach could contribute as a useful tool to motor cortical decoding and prosthetic applications. (C) 2010 Elsevier B.V. All rights reserved.

关键词： neural decoding Motor cortex Generalized linear model Hidden states State-space model

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Revealing neural correlates of behavior without behavioral measurements

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NATURE COMMUNICATIONS 2019年第1期10卷 4745页

作者： Rubin, Alon Sheintuch, Liron Brande-Eilat, Noa Pinchasof, Or Rechavi, Yoav Geva, Nitzan Ziv, Yaniv Weizmann Inst Sci Dept Neurobiol IL-76100 Rehovot Israel

Measuring neuronal tuning curves has been instrumental for many discoveries in neuroscience but requires a priori assumptions regarding the identity of the encoded variables. We applied unsupervised learning to large-scale neuronal recordings in behaving mice from circuits involved in spatial cognition and uncovered a highly-organized internal structure of ensemble activity patterns. This emergent structure allowed defining for each neuron an 'internal tuning-curve' that characterizes its activity relative to the network activity, rather than relative to any predefined external variable, revealing place-tuning and head-direction tuning without relying on measurements of place or head-direction. Similar investigation in prefrontal cortex revealed schematic representations of distances and actions, and exposed a previously unknown variable, the 'trajectory-phase'. The internal structure was conserved across mice, allowing using one animal's data to decode another animal's behavior. Thus, the internal structure of neuronal activity itself enables reconstructing internal representations and discovering new behavioral variables hidden within a neural code.

关键词： Learning and memory neural circuits neural decoding

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Studying the neural representations of uncertainty

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NATURE NEUROSCIENCE 2023年第11期26卷 1857-1867页

作者： Walker, Edgar Y. Pohl, Stephan Denison, Rachel N. Barack, David L. Lee, Jennifer Block, Ned Ma, Wei Ji Meyniel, Florent Univ Washington Dept Physiol & Biophys Computat Neurosci Ctr Seattle WA 98195 USA NYU Dept Philosophy New York NY USA Boston Univ Dept Psychol & Brain Sci Boston MA 02215 USA Univ Penn Dept Neurosci Philadelphia PA 19104 USA Univ Penn Dept Philosophy Philadelphia PA 19104 USA NYU Ctr Neural Sci New York NY 10003 USA NYU Dept Psychol 6 Washington Pl New York NY 10003 USA Univ Paris Saclay CNRS Cognit Neuroimaging Unit Cognit NeuroSpin CtrINSERMCEA Gif Sur Yvette France

The study of the brain's representations of uncertainty is a central topic in neuroscience. Unlike most quantities of which the neural representation is studied, uncertainty is a property of an observer's beliefs about the world, which poses specific methodological challenges. We analyze how the literature on the neural representations of uncertainty addresses those challenges and distinguish between 'code-driven' and 'correlational' approaches. Code-driven approaches make assumptions about the neural code for representing world states and the associated uncertainty. By contrast, correlational approaches search for relationships between uncertainty and neural activity without constraints on the neural representation of the world state that this uncertainty accompanies. To compare these two approaches, we apply several criteria for neural representations: sensitivity, specificity, invariance and functionality. Our analysis reveals that the two approaches lead to different but complementary findings, shaping new research questions and guiding future experiments. This Review explains how the neural coding of uncertainty is theoretically conceived and empirically tested. It compares the approaches of two largely separate research communities and proposes goals for the field that combine these approaches.

关键词： neural decoding neural encoding Perception

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A brain-to-text framework for decoding natural tonal sentences

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CELL REPORTS 2024年第11期43卷 114924页

作者： Zhang, Daohan Wang, Zhenjie Qian, Youkun Zhao, Zehao Liu, Yan Hao, Xiaotao Li, Wanxin Lu, Shuo Zhu, Honglin Chen, Luyao Xu, Kunyu Li, Yuanning Lu, Junfeng Fudan Univ Shanghai Med Coll Dept Neurosurg Huashan Hosp Shanghai 200040 Peoples R China Shanghai Key Lab Brain Funct & Restorat & Neural Shanghai 200040 Peoples R China Fudan Univ Huashan Hosp Shanghai Med Coll Natl Ctr Neurol Disorders Shanghai 200040 Peoples R China ShanghaiTech Univ Sch Biomed Engn Shanghai 201210 Peoples R China Sun Yat Sen Univ Dept Chinese Language & Literature Guangzhou 510080 Peoples R China Kings Coll London Fac Life Sci & Med London SE1 1UL England Beijing Normal Univ Sch Int Chinese Language Educ Beijing 100875 Peoples R China Fudan Univ Inst Modern Languages & Linguist Shanghai 200433 Peoples R China ShanghaiTech Univ State Key Lab Adv Med Mat & Devices Shanghai 201210 Peoples R China Fudan Univ Huashan Hosp MOE Frontiers Ctr Brain Sci Shanghai 200040 Peoples R China Shanghai Clin Res & Trial Ctr Shanghai 201210 Peoples R China

Speech brain-computer interfaces (BCIs) directly translate brain activity into speech sound and text. Despite successful applications in non-tonal languages, the distinct syllabic structures and pivotal lexical information conveyed through tonal nuances present challenges in BCI decoding for tonal languages like Mandarin Chinese. Here, we designed a brain-to-text framework to decode Mandarin sentences from invasive neural recordings. Our framework dissects speech onset, base syllables, and lexical tones, integrating them with contextual information through Bayesian likelihood and a Viterbi decoder. The results demonstrate accurate tone and syllable decoding during naturalistic speech production. The overall word error rate (WER) for 10 offline-decoded tonal sentences with a vocabulary of 40 high-frequency Chinese characters is 21% (chance: 95.3%) averaged across five participants, and tone decoding accuracy reaches 93% (chance: 25%), surpassing previous intracranial Mandarin tonal syllable decoders. This study provides a robust and generalizable approach for brain-to-text decoding of continuous tonal speech sentences.

关键词： electrocorticography ECoG brain-computer interface BCI tonal language natural speech deep neural networks neural decoding artificial intelligence Mandarin

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Machine translation of cortical activity to text with an encoder-decoder framework

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NATURE NEUROSCIENCE 2020年第4期23卷 575-+页

作者： Makin, Joseph G. Moses, David A. Chang, Edward F. UCSF Ctr Integrat Neurosci San Francisco CA 94143 USA UCSF Dept Neurol Surg San Francisco CA 94143 USA

A decade after speech was first decoded from human brain signals, accuracy and speed remain far below that of natural speech. Here we show how to decode the electrocorticogram with high accuracy and at natural-speech rates. Taking a cue from recent advances in machine translation, we train a recurrent neural network to encode each sentence-length sequence of neural activity into an abstract representation, and then to decode this representation, word by word, into an English sentence. For each participant, data consist of several spoken repeats of a set of 30-50 sentences, along with the contemporaneous signals from similar to 250 electrodes distributed over peri-Sylvian cortices. Average word error rates across a held-out repeat set are as low as 3%. Finally, we show how decoding with limited data can be improved with transfer learning, by training certain layers of the network under multiple participants' data.

关键词： Brain–machine interface Language neural decoding

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Learnable latent embeddings for joint behavioural and neural analysis

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NATURE 2023年第7960期617卷 360-+页

作者： Schneider, Steffen Lee, Jin Hwa Mathis, Mackenzie Weygandt Ecole Polytech Fed Lausanne Brain Mind Inst Geneva Switzerland Ecole Polytech Fed Lausanne Neuro Inst 10 Geneva Switzerland

Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioural data increases, there is growing interest in modelling neural dynamics during adaptive behaviours to probe neural representations(1-3). In particular, although neural latent embeddings can reveal underlying correlates of behaviour, we lack nonlinear techniques that can explicitly and flexibly leverage joint behaviour and neural data to uncover neural dynamics(3-5). Here, we fill this gap with a new encoding method, CEBRA, that jointly uses behavioural and neural data in a (supervised) hypothesis- or (self-supervised) discovery-driven manner to produce both consistent and high-performance latent spaces. We show that consistency can be used as a metric for uncovering meaningful differences, and the inferred latents can be used for decoding. We validate its accuracy and demonstrate our tool's utility for both calcium and electrophysiology datasets, across sensory and motor tasks and in simple or complex behaviours across species. It allows leverage of single- and multi-session datasets for hypothesis testing or can be used label free. Lastly, we show that CEBRA can be used for the mapping of space, uncovering complex kinematic features, for the production of consistent latent spaces across two-photon and Neuropixels data, and can provide rapid, high-accuracy decoding of natural videos from visual cortex.

关键词： Machine learning neural decoding

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