9 results on '"Eric Dewitt"'
Search Results
2. Standardized and reproducible measurement of decision-making in mice
- Author
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The International Brain Laboratory, Valeria Aguillon-Rodriguez, Dora Angelaki, Hannah Bayer, Niccolo Bonacchi, Matteo Carandini, Fanny Cazettes, Gaelle Chapuis, Anne K Churchland, Yang Dan, Eric Dewitt, Mayo Faulkner, Hamish Forrest, Laura Haetzel, Michael Häusser, Sonja B Hofer, Fei Hu, Anup Khanal, Christopher Krasniak, Ines Laranjeira, Zachary F Mainen, Guido Meijer, Nathaniel J Miska, Thomas D Mrsic-Flogel, Masayoshi Murakami, Jean-Paul Noel, Alejandro Pan-Vazquez, Cyrille Rossant, Joshua Sanders, Karolina Socha, Rebecca Terry, Anne E Urai, Hernando Vergara, Miles Wells, Christian J Wilson, Ilana B Witten, Lauren E Wool, and Anthony M Zador
- Subjects
behavior ,reproducibility ,decision making ,Medicine ,Science ,Biology (General) ,QH301-705.5 - Abstract
Progress in science requires standardized assays whose results can be readily shared, compared, and reproduced across laboratories. Reproducibility, however, has been a concern in neuroscience, particularly for measurements of mouse behavior. Here, we show that a standardized task to probe decision-making in mice produces reproducible results across multiple laboratories. We adopted a task for head-fixed mice that assays perceptual and value-based decision making, and we standardized training protocol and experimental hardware, software, and procedures. We trained 140 mice across seven laboratories in three countries, and we collected 5 million mouse choices into a publicly available database. Learning speed was variable across mice and laboratories, but once training was complete there were no significant differences in behavior across laboratories. Mice in different laboratories adopted similar reliance on visual stimuli, on past successes and failures, and on estimates of stimulus prior probability to guide their choices. These results reveal that a complex mouse behavior can be reproduced across multiple laboratories. They establish a standard for reproducible rodent behavior, and provide an unprecedented dataset and open-access tools to study decision-making in mice. More generally, they indicate a path toward achieving reproducibility in neuroscience through collaborative open-science approaches.
- Published
- 2021
- Full Text
- View/download PDF
3. Neuromatch Academy: Teaching Computational Neuroscience with Global Accessibility
- Author
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Madineh Sedigh-Sarvestani, Marius Pachitariu, Paul Schrater, Xaq Pitkow, Yueqi Guo, Ashley L. Juavinett, Brad Wyble, Kathryn Bonnen, Carsen Stringer, John D. Murray, Elnaz Alikarami, Jeffrey C. Erlich, Emma Vaughan, Maryam Vaziri-Pashkam, Grace W. Lindsay, Aina Puce, Alexandre Hyafil, Konrad P. Kording, Sean Escola, Melvin Selim Atay, Patrick J. Mineault, Megan A. K. Peters, Matthew R. Krause, Eleanor Batty, Davide Valeriani, Helena Ledmyr, Byron V. Galbraith, Songting Li, Titipat Achakulvisut, Gunnar Blohm, Elizabeth Straley, Michael Waskom, Eric Dewitt, Tara van Viegen, and Athena Akrami
- Subjects
Computational neuroscience ,Cognitive Neuroscience ,Universal design ,05 social sciences ,Neurosciences ,Experimental and Cognitive Psychology ,Community management ,050105 experimental psychology ,03 medical and health sciences ,0302 clinical medicine ,Neuropsychology and Physiological Psychology ,ComputingMilieux_COMPUTERSANDEDUCATION ,Mathematics education ,Humans ,0501 psychology and cognitive sciences ,Psychology ,030217 neurology & neurosurgery - Abstract
Neuromatch Academy (NMA) designed and ran a fully online 3-week Computational Neuroscience Summer School for 1757 students with 191 teaching assistants (TAs) working in virtual inverted (or flipped) classrooms and on small group projects. Fourteen languages, active community management, and low cost allowed for an unprecedented level of inclusivity and universal accessibility.
- Published
- 2021
4. Neuromatch Academy: a 3-week, online summer school in computational neuroscience
- Author
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Bernard ’t Hart, Titipat Achakulvisut, Ayoade Adeyemi, Athena Akrami, Bradly Alicea, Alicia Alonso-Andres, Diego Alzate-Correa, Arash Ash, Jesus Ballesteros, Aishwarya Balwani, Eleanor Batty, Ulrik Beierholm, Ari Benjamin, Upinder Bhalla, Gunnar Blohm, Joachim Blohm, Kathryn Bonnen, Marco Brigham, Bingni Brunton, John Butler, Brandon Caie, N Gajic, Sharbatanu Chatterjee, Spyridon Chavlis, Ruidong Chen, You Cheng, H.m. Chow, Raymond Chua, Yunwei Dai, Isaac David, Eric DeWitt, Julien Denis, Alish Dipani, Arianna Dorschel, Jan Drugowitsch, Kshitij Dwivedi, Sean Escola, Haoxue Fan, Roozbeh Farhoodi, Yicheng Fei, Pierre-Étienne Fiquet, Lorenzo Fontolan, Jeremy Forest, Yuki Fujishima, Byron Galbraith, Mario Galdamez, Richard Gao, Julijana Gjorgjieva, Alexander Gonzalez, Qinglong Gu, Yueqi Guo, Ziyi Guo, Pankaj Gupta, Busra Gurbuz, Caroline Haimerl, Jordan Harrod, Alexandre Hyafil, Martin Irani, Daniel Jacobson, Michelle Johnson, Ilenna Jones, Gili Karni, Robert Kass, Hyosub Kim, Andreas Kist, Randal Koene, Konrad Kording, Matthew Krause, Arvind Kumar, Norma Kühn, Ray Lc, Matthew Laporte, Junseok Lee, Songting Li, Sikun Lin, Yang Lin, Shuze Liu, Tony Liu, Jesse Livezey, Linlin Lu, Jakob Macke, Kelly Mahaffy, A Martins, Nicolás Martorell, Manolo Martínez, Marcelo Mattar, Jorge Menendez, Kenneth Miller, Patrick Mineault, Nosratullah Mohammadi, Yalda Mohsenzadeh, Elenor Morgenroth, Taha Morshedzadeh, Alice Mosberger, Madhuvanthi Muliya, Marieke Mur, John Murray, Yashas Nd, Richard Naud, Prakriti Nayak, Anushka Oak, Itzel Castillo, Seyedmehdi Orouji, Jorge Otero-Millan, Marius Pachitariu, Biraj Pandey, Renato Paredes, Jesse Parent, Il Park, Megan Peters, Xaq Pitkow, Panayiota Poirazi, Haroon Popal, Sandhya Prabhakaran, Tian Qiu, Srinidhi Ragunathan, Raul Rodriguez-Cruces, David Rolnick, Ashish Sahoo, Saeed Salehinajafabadi, Cristina Savin, Shreya Saxena, Paul Schrater, Karen Schroeder, Alice Schwarze, Madineh Sedigh-Sarvestani, K Sekhar, Reza Shadmehr, Maryam Shanechi, Siddhant Sharma, Eric Shea-Brown, Krishna Shenoy, Carolina Shimabukuro, Sergey Shuvaev, Man Sin, Maurice Smith, Nicholas Steinmetz, Karolina Stosio, Elizabeth Straley, Gabrielle Strandquist, Carsen Stringer, Rimjhim Tomar, Ngoc Tran, Sofia Triantafillou, Lawrence Udeigwe, Davide Valeriani, Vincent Valton, Maryam Vaziri-Pashkam, Peter Vincent, Gal Vishne, Pascal Wallisch, Peiyuan Wang, Claire Ward, Michael Waskom, Kunlin Wei, Anqi Wu, Zhengwei Wu, Brad Wyble, Lei Zhang, Daniel Zysman, Federico Uquillas, and Tara van Viegen
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lectures ,Computational Neuroscience ,summer school ,online learning ,Online and Distance Education ,Mathematics ,tutorials - Abstract
Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function.
- Published
- 2022
5. Neuromatch Academy: a 3-week, online summer school in computational neuroscience
- Author
-
Bernard Marius 't Hart, Titipat Achakulvisut, Athena Akrami, Bradly Alicea, Ulrik Beierholm, Gunnar Blohm, Kathryn Bonnen, John S Butler, Brandon Caie, You Cheng, Hiu Mei Chow, Isaac David, Eric DeWitt, Jan Drugowitsch, Kshitij Dwivedi, Pierre-Étienne Fiquet, Jeremy Forest, Byron Galbraith, Qingling Gu, PANKAJ GUPTA, Alexandre Hyafil, Konrad Kording, Arvind Kumar, Patrick Mineault, John D. Murray, Megan A. K. Peters, Paul Schrater, Carsen Stringer, Pascal Wallisch, and Brad Wyble
- Abstract
Neuromatch Academy (https://neuromatch.io/academy) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function.
- Published
- 2021
6. Author response: Standardized and reproducible measurement of decision-making in mice
- Author
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Zachary F. Mainen, Anne K. Churchland, Dora E. Angelaki, Matteo Carandini, Hamish Forrest, Guido T. Meijer, Thomas D. Mrsic-Flogel, Masayoshi Murakami, Lauren E Wool, Christian J Wilson, Hannah Bayer, Joshua I. Sanders, Eric Dewitt, Fanny Cazettes, Ilana B. Witten, Mayo Faulkner, Anthony M. Zador, Laura Haetzel, Gaelle Chapuis, Christopher Krasniak, Cyrille Rossant, Jean-Paul Noel, Anup Khanal, Niccolò Bonacchi, Alejandro Pan-Vazquez, Miles J. Wells, Michael Häusser, Ines Laranjeira, Nathaniel J Miska, Hernando Vergara, Karolina Socha, Rebecca Terry, Anne E Urai, Valeria Aguillon-Rodriguez, Yang Dan, Fei Hu, and Sonja B. Hofer
- Published
- 2021
7. Standardized and reproducible measurement of decision-making in mice
- Author
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Anup Khanal, Christian J Wilson, Jean-Paul Noel, Karolina Socha, Alejandro Pan-Vazquez, Ines Laranjeira, Fanny Cazettes, Anne E Urai, Anthony M. Zador, Gaelle Chapuis, Anne K. Churchland, Ilana B. Witten, Hamish Forrest, Cyrille Rossant, Niccolò Bonacchi, Zachary F. Mainen, Mayo Faulkner, Thomas D. Mrsic-Flogel, Rebecca Terry, Eric Dewitt, Dora E. Angelaki, Guido T. Meijer, Sonja B. Hofer, Miles J. Wells, Masayoshi Murakami, Matteo Carandini, Valeria Aguillon-Rodriguez, Hannah Bayer, Nathaniel J Miska, Lauren E Wool, Joshua I. Sanders, Laura Haetzel, Fei Hu, Christopher Krasniak, Hernando Vergara, Michael Häusser, and Yang Dan
- Subjects
0301 basic medicine ,Male ,Visual perception ,Biomedical Research ,Time Factors ,Mouse ,Rodent ,Computer science ,Inbred C57BL ,computer.software_genre ,Task (project management) ,neuroscience ,Mice ,0302 clinical medicine ,Models ,Biology (General) ,Observer Variation ,0303 health sciences ,Behavior, Animal ,biology ,General Neuroscience ,General Medicine ,Tools and Resources ,Models, Animal ,Visual Perception ,Medicine ,Female ,Cues ,QH301-705.5 ,Science ,Decision Making ,Machine learning ,Basic Behavioral and Social Science ,General Biochemistry, Genetics and Molecular Biology ,decision making ,03 medical and health sciences ,biology.animal ,Behavioral and Social Science ,Animals ,Learning ,reproducibility ,mouse ,030304 developmental biology ,Protocol (science) ,Behavior ,General Immunology and Microbiology ,behavior ,Animal ,business.industry ,Neurosciences ,Reproducibility of Results ,Mice, Inbred C57BL ,030104 developmental biology ,Biochemistry and Cell Biology ,Artificial intelligence ,business ,computer ,International Brain Laboratory ,Photic Stimulation ,030217 neurology & neurosurgery ,Neuroscience - Abstract
Progress in science requires standardized assays whose results can be readily shared, compared, and reproduced across laboratories. Reproducibility, however, has been a concern in neuroscience, particularly for measurements of mouse behavior. Here, we show that a standardized task to probe decision-making in mice produces reproducible results across multiple laboratories. We adopted a task for head-fixed mice that assays perceptual and value-based decision making, and we standardized training protocol and experimental hardware, software, and procedures. We trained 140 mice across seven laboratories in three countries, and we collected 5 million mouse choices into a publicly available database. Learning speed was variable across mice and laboratories, but once training was complete there were no significant differences in behavior across laboratories. Mice in different laboratories adopted similar reliance on visual stimuli, on past successes and failures, and on estimates of stimulus prior probability to guide their choices. These results reveal that a complex mouse behavior can be reproduced across multiple laboratories. They establish a standard for reproducible rodent behavior, and provide an unprecedented dataset and open-access tools to study decision-making in mice. More generally, they indicate a path toward achieving reproducibility in neuroscience through collaborative open-science approaches., eLife digest In science, it is of vital importance that multiple studies corroborate the same result. Researchers therefore need to know all the details of previous experiments in order to implement the procedures as exactly as possible. However, this is becoming a major problem in neuroscience, as animal studies of behavior have proven to be hard to reproduce, and most experiments are never replicated by other laboratories. Mice are increasingly being used to study the neural mechanisms of decision making, taking advantage of the genetic, imaging and physiological tools that are available for mouse brains. Yet, the lack of standardized behavioral assays is leading to inconsistent results between laboratories. This makes it challenging to carry out large-scale collaborations which have led to massive breakthroughs in other fields such as physics and genetics. To help make these studies more reproducible, the International Brain Laboratory (a collaborative research group) et al. developed a standardized approach for investigating decision making in mice that incorporates every step of the process; from the training protocol to the software used to analyze the data. In the experiment, mice were shown images with different contrast and had to indicate, using a steering wheel, whether it appeared on their right or left. The mice then received a drop of sugar water for every correction decision. When the image contrast was high, mice could rely on their vision. However, when the image contrast was very low or zero, they needed to consider the information of previous trials and choose the side that had recently appeared more frequently. This method was used to train 140 mice in seven laboratories from three different countries. The results showed that learning speed was different across mice and laboratories, but once training was complete the mice behaved consistently, relying on visual stimuli or experiences to guide their choices in a similar way. These results show that complex behaviors in mice can be reproduced across multiple laboratories, providing an unprecedented dataset and open-access tools for studying decision making. This work could serve as a foundation for other groups, paving the way to a more collaborative approach in the field of neuroscience that could help to tackle complex research challenges.
- Published
- 2020
8. The Canadian Northwest in 1811 : a study in the historical geography of the Old Northwest of the fur trade on the eve of the first agricultural settlement
- Author
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Ross, Eric DeWitt
- Subjects
910.9 - Abstract
The fur traders of 1811 saw the Northwest as three distinct regions: New South Wales [Hudson Bay Lowlands], the Stony Region [C'anadian Shield] and the Great [Interiorj Plains. To them, the least important of these was New South Wales which was by then functioning primarily as a base from which the Hudson's Bay Company conducted its trade in the Northwest. It was relatively poor in both furs and provisions. Except for migrating ducks and geese, game was both scarce and unpredictable and the three factories maintained there by the company were largely dependent upon provisions from Europe and pemmican from the interior. Apart from the forested margin along its western edge, particularly in the Lake Athabasca area, the Stony Region had always been relatively poor in furs and provisions but, by 1811, it was becoming so hunted out that it was coming to be regarded as more of a barrier than as a productive region in itself - a barrier which had to be crossed to reach the rich provision areas of the grasslands and fur countries of the northern plains. Many of the natives of this area, taking advantage of their geographical position, became "middlemen" and carried the furs and provisions of wealthier countries across their homeland to trade on Hudson Bay. The broken rivers of the Stony Region added to its barrier- like appearance. navigable, river and Mackenzie. In sharp contrast were the three large, highly systems of the Great Plains, the Red, Saskatchewan, These not only enabled the traders to exploit the furs and provisions of the Great Plains but also the fur lands along the western margin of the Stony Region. Two important routes enabled the traders to cross the Stony Region to enter the rivers of the Great Plains. One led from Fort William to Lake Winnipeg and the other from York Factory to Lake Winnipeg. Each was approached from one of the two great waterways which led from the Atlantic into the heart of North America, the Saint Lawrence - Great Lakes system and Hudson Strait and Bay. By 1811, each entrance was controlled by a single fur monopoly, the Great Lakes by the North West Company and Hudson Bay by the Hudson's Bay Company. The latter route was the more economic and the North West Company was then attempting to come to an agreement with the Hudson's Bay Company in order to be able to use it as well. In the Northwest, itself, the two companies competed side by side nearly everywhere in the Red and Saskatchewan countries and in much of the Stony Region. In the Athabasca [ Mackenzi l basin Country, however, the North West Company had so far succeeded in excluding the Hudson's Bay Company from this, the richest fur area in the whole Northwest. The Canadian Company also enjoyed a monopoly in the area beyond the Rockies known as New Caledonia, in which it was then extending its activities. Most of the important trading posts were situated near good fisheries. Exceptions were the bayside factories and a number of posts near the open plains where buffalo were plentiful. All of the principal provision depots and goods stores in the interior were established on lakes forming Part of the "Valley of the Lakes" which separated the Stony Region from the Great Plains. These were Rainy Lake House, Fort Bas- de -la- Riviere, Fort Cumberland, Fort Ile -a -la- Crosse and Fort Chipewyan. In each case, the provisions were brought down stream to the depot with the minimum of effort. Ducks and geese were also plentiful along these lakes during the spring and autumn. The lakeside posts, in common with most posts in the Northwest, were situated near the river junctions. Other posts were usually placed near a sharp elbow in a river or perhaps along its headwaters. In each of the latter cases, the house would probably also be near a portage or an overland Pass. Fierce competition between rival factions led to some fairly irrational choices of location as well. Generally speaking, relations between the trading factions were best where the Indians were most hostile and poorest where the Indians were most friendly. That is, they were best on the plains, where the Indians were not dependent upon the traders and could afford to be reckless in their dealings with them, and poorest in the forest, where the inhabitants could no longer live without the traders' goods. Along the periphery of the trade, which in 1811 corresponded roughly with the borders of the Northwest, relations were poorest of all. For the natives along the trade frontier were anxious that the trade should spread no further geographically because they did not want the natives beyond them- selves, who in nearly every case were enemies, to receive guns and ammunition. Moreover, the peripheral tribes often carried on a very lucrative trade in European goods with their more distant enemy -neighbours, and realized that any extension of the trade might destroy their position as middlemen. To the traders, these middlemen were, at best, mere nuisances who added little to the trade, and they were anxious to penetrate to the Indians of the country beyond them. The peripheral tribes, of course, tried to obstruct the progress of the traders and open hostility was often the result. In order to carry on his trade and, indeed, merely to exist in this harsh new land, the European had to borrow many skills and techniques from the natives. From them, he learned how to use the birch bark canoe and to make pemmican. It was these two things which enabled him to develop his vast transportation system which, more than anything else, permitted him to earn his living in the Northwest. Canoe travel was expensive and only a luxury product like furs could bear the high cost. By 1811, the Napoleonic wars had so depressed the fur markets, that little other than beaver was then worth carrying. The wars had also been responsible for an increasingly serious personnel shortage in the Hudson's Bay Company. This had contributed much to the company's ineffectiveness in dealing with Canadian competition. The company had also been labouring under the handicap of an overly riged organization. Nevertheless, in 1811, it faced the future with confidence. For not only had a more flexible organization recently been adopted by there was real hope that the chronic personnel shortage would soon he solved. Lork Selkirk had just concluded an agreement with the company to supply a large number of men each year in return for a vast grant of land along the Red River for the purpose of establishing an agricultural settlement. In the years to come, it was hoped that the settlement would also provide a source of recruits. The vanguard of the settlers were then wintering along the Nelson above York Factory. During the next century, hundreds of thousands would follow them. And they would change the face of the Northwest. The bold checkered pattern of agriculture would spread across much of the Great Plains, pushing the traders northward and eastward into the Stony Region until all that remained of the old Northwest which had been theirs, was a pile of stones in some farmer's field near the meeting place of two streams, or the fragile remains of a copper kettle below a waterfall, marking the place where a canoe had capsized and a voyageur's song had ended.
- Published
- 1962
9. The impact of learning on perceptual decisions and its implication for speed-accuracy tradeoffs
- Author
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Zachary F. Mainen, Jan Drugowitsch, M. Inês Vicente, André G. Mendonça, Eric Dewitt, and Alexandre Pouget
- Subjects
0301 basic medicine ,Computer science ,General Physics and Astronomy ,computer.software_genre ,Choice Behavior ,Task (project management) ,Bayes' theorem ,0302 clinical medicine ,lcsh:Science ,Reinforcement ,media_common ,0303 health sciences ,Multidisciplinary ,Behavior, Animal ,Uncertainty ,Sensory processing ,Reinforcement, Psychology ,psychological phenomena and processes ,Science ,media_common.quotation_subject ,Decision Making ,Sensory system ,Machine learning ,Bayesian inference ,General Biochemistry, Genetics and Molecular Biology ,Article ,Learning and memory ,03 medical and health sciences ,Memory ,Perception ,Reaction Time ,Animals ,Learning ,030304 developmental biology ,business.industry ,Computational Biology ,Bayes Theorem ,General Chemistry ,Models, Theoretical ,Olfactory system ,Rats ,ddc:616.8 ,030104 developmental biology ,Speed accuracy ,Computational neuroscience ,lcsh:Q ,Artificial intelligence ,Noise (video) ,business ,computer ,Psychomotor Performance ,030217 neurology & neurosurgery - Abstract
In standard models of perceptual decision-making, noisy sensory evidence is considered to be the primary source of choice errors and the accumulation of evidence needed to overcome this noise gives rise to speed-accuracy tradeoffs. Here, we investigated how the history of recent choices and their outcomes interact with these processes using a combination of theory and experiment. We found that the speed and accuracy of performance of rats on olfactory decision tasks could be best explained by a Bayesian model that combines reinforcement-based learning with accumulation of uncertain sensory evidence. This model predicted the specific pattern of trial history effects that were found in the data. The results suggest that learning is a critical factor contributing to speed-accuracy tradeoffs in decision-making, and that task history effects are not simply biases but rather the signatures of an optimal learning strategy., Here, the authors show that rats’ performance on olfactory decision tasks is best explained by a Bayesian model that combines reinforcement-based learning with accumulation of uncertain sensory evidence. The results suggest that learning is a critical factor contributing to speed-accuracy tradeoffs.
- Published
- 2018
- Full Text
- View/download PDF
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