2024
, 2024
Perceptual decision coding is inherently coupled to action in the mouse cortex
Lecture
Sunday, December 29, 2024
Hour: 12:00 - 13:15
Location:
Max and Lillian Candiotty Building
Perceptual decision coding is inherently coupled to action in the mouse cortex
Michael Sokoletsky PhD Defense
<p>Student Seminar-PhD Thesis Defense</p>
<p>How do animals make perceptual decisions about sensory stimuli to guide motor actions? One hypothesis is that dedicated "perceptual decision" cells process sensory information and drive the appropriate action. Alternatively, perceptual decisions result from competition among cells driving different actions, making decisions inherently coupled to actions. To distinguish between these hypotheses, we designed a vibrotactile detection task in which mice flexibly switched between standard and reversed contingency blocks, respectively requiring them to lick after stimulus presence or absence. Optogenetic inactivation of somatosensory and secondary motor cortices reduced stimulus sensitivity without impairing the ability to lick. However, widefield and two-photon imaging found that differences in cortical activity across perceptual decisions were almost exclusively action-coupled. In addition, we identified a subset of cells that encoded the current contingency block in a gated manner, enabling mice to flexibly make decisions without relying on action-independent decision coding.</p>
Deep language models as a cognitive model for natural language processing in the human brain
Lecture
Thursday, December 26, 2024
Hour: 12:30 - 13:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Deep language models as a cognitive model for natural language processing in the human brain
Prof. Uri Hasson
<p>Special Seminar</p>
<p>Naturalistic experimental paradigms in cognitive neuroscience arose from a pressure to test, in real-world contexts, the validity of models we derive from highly controlled laboratory experiments. In many cases, however, such efforts led to the realization that models (i.e., explanatory principles) developed under particular experimental manipulations fail to capture many aspects of reality (variance) in the real world. Recent advances in artificial neural networks provide an alternative computational framework for modeling cognition in natural contexts. In this talk, I will ask whether the human brain's underlying computations are similar or different from the underlying computations in deep neural networks, focusing on the underlying neural process that supports natural language processing in adults and language development in children. I will provide evidence for some shared computational principles between deep language models and the neural code for natural language processing in the human brain. This indicates that, to some extent, the brain relies on overparameterized optimization methods to comprehend and produce language. At the same time, I will present evidence that the brain differs from deep language models as speakers try to convey new ideas and thoughts. Finally, I will discuss our ongoing attempt to use deep acoustic-to-speech-to-language models to model language acquisition in children. </p>
Anterior-Posterior Insula Circuit Mediates Retrieval of a Conditioned Immune Response in Mice
Lecture
Tuesday, December 24, 2024
Hour: 12:30 - 13:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Anterior-Posterior Insula Circuit Mediates Retrieval of a Conditioned Immune Response in Mice
Prof. Kobi Rosenblum
<p>The brain can form associations between sensory information of inner and/or outer world (e.g. Pavlovian conditioning) but also between sensory information and the immune system. The phenomenon which was described in the last century is termed conditioned immune response (CIR) but very little is known about neuronal mechanisms subserving it. The conditioned stimulus can be a given taste and the unconditioned stimulus is an agent that induces or reduces a specific immune response. Over the last years, we and others revealed molecular and cellular mechanisms underlying taste valance representation in the anterior insular cortex (aIC). Recently, a circuit in the posterior insular cortex (pIC) encoding the internal representation of a given immune response was identified. Together, it allowed us to hypothesize and prove that the internal reciprocal connections between the anterior and posterior insula encode CIR. One can look at CIR as a noon declarative form of Nocebo effect and thus we demonstrate for the first time a detailed circuit mechanism for Placebo/Nocebo effect in the cortex.</p>
"Hot and Cold Thoughts"
Lecture
Tuesday, December 10, 2024
Hour: 12:30 - 13:30
Location:
Gerhard M.J. Schmidt Lecture Hall
"Hot and Cold Thoughts"
Prof. Oded Rechavi
<p>I will present two new and unpublished stories about what happens when the nervous system perceives temperature shifts</p><p></p>
Neuroprotective and Anticonvulsant Effects of Cannabinoids with Neurotrauma
Lecture
Thursday, December 5, 2024
Hour: 12:30 - 13:30
Location:
Neuroprotective and Anticonvulsant Effects of Cannabinoids with Neurotrauma
Prof.Linda Friedman
<p>Traumatic brain (TBI) injuries result in profound local hypoperfusion, ischemia, chronic inflammation and refractory seizures(post-traumatic epilepsy (PTE)), and restrict drug delivery to the site of impact so that peripheral treatment alone would have limited access to the site of injury during the most critical phases of neurotrauma. Cannabidiol (CBD), the major non-psychotropic cannabinoid, has anti-convulsant, anti-inflammatory, anti-nociceptive, antioxidant, and immuno-suppressive properties not fully understood. In pre-juvenile rats, microinjection of CBD attenuated kainate(KA)-induced seizures to a greater extent than intraperitoneal injection, indicating that local drug administration was more effective. In adult rats after experimental TBI, our modified CBD-infused implant applied extradural with oil injection supplementation restored vestibulomotorand cognitive functions compared to systemic treatment alone. We questioned whether the CBD or the low concentrations of THC in the extract was responsible for behavioral and cellular recovery.We hypothesized that an optimal ratio of cannabidiol (CBD) to tetrahydrocannabinol (THC) is required to protect against neuropathological consequences following TBI greater than either substance alone. Varied CBD:THC extract concentrations were compared with hempCBD lacking THC (CBD0). Neurons, glia, and parvalbumin interneurons (PV-INs) were evaluated. Weight loss was observed following high doses of THC dominant cannabis, THC100:1. Neuroscoresand vestibulomotorperformance were restored more with CBD:THC300:1-10:1. However, THC dominant treatments resulted in early onset to spontaneous seizures post-TBI. In a non-reward T-maze, the CBD10:1group had the highest alternation rates; TBI + vehicle, CBD0, CBD1:1, and THC100:1treatment groups had the lowest. The novel object recognition memory task showed CBD300:1treated animals had the best performance, while TBI or THC100:1treated groups had the worst. The forced swim test (FST) showed immobility time was highest after TBI and lowest after THC100:1treatment. The elevated plus maze (EPM) revealed the CBD0group spent the most time in closed arms. Both tests indicate that reduced anxiety was THC dependent. All combinations resulted in reduced injury but CBD10:1and THC20:1gave the most protection and THC100:1the least. Reduced anxiety level was THC dependent but higher doses were pro-convulsant cautioning THC dosing. Reduced GFAP labeling was highest with CBD dominant cannabis supporting its neuroprotective role against inflammation. Rescue of diminished bilateral PV-INs was observed within the hippocampus and medial prefrontal cortex (mPFC) with CBD dominant treatment (CBD300, CBD0) supporting their anticonvulsant effect. Loss of PV-INs with THC dominant treatment supports their proconvulsant effect. Thus, CBD and THC have different beneficial therapeutic effects indicating an optimal concentration ratio is critical for optimal neuropathological therapeutics.</p><p>Light refreshments before the seminar</p>
The Evolution of 7T (and Beyond) MRI in Basic Research and Clinical Practice
Lecture
Tuesday, December 3, 2024
Hour: 12:30 - 13:30
Location:
Gerhard M.J. Schmidt Lecture Hall
The Evolution of 7T (and Beyond) MRI in Basic Research and Clinical Practice
Prof. Noam Harel
Center for Magnetic Resonance Research, University of Minnesota, Minneapolis
The Center for Magnetic Resonance Research (CMRR) has been at the forefront of magnetic resonance imaging (MRI) innovation, pioneering ultra-high field (7 Tesla and above) technologies that are revolutionizing brain research and clinical care. This presentation will explore CMRR's groundbreaking journey, from the first functional MRI study to development of high-resolution fMRI capabilities revealing cortical columns within the human cortex. The presentation will also explore the translation of these technologies into clinical practice, with a focus on the unique visualization capabilities of 7T MRI, particularly for enhancing the precision of Deep Brain Stimulation (DBS) procedures.By exploring the progression from the 7T system to the world’s first 10.5T human MRI, this presentation will illustrate how these transformative technologies have pushed the limits of imaging science, uncovering new insights into brain function and advancing personalized clinical care at the intersection of technology, research, and medicine.
The role of neurons in the direction-selective retinal circuit in visual processing in the retina and in the visual thalamus
Lecture
Monday, October 14, 2024
Hour: 15:00 - 17:00
Location:
Gerhard M.J. Schmidt Lecture Hall
The role of neurons in the direction-selective retinal circuit in visual processing in the retina and in the visual thalamus
Alina Heukamp-Prof. Michal Rivlin Lab
Student Seminar-PhD Thesis Defense
The role of neurons in the direction-selective retinal circuit in visual processing in the retina and in the visual thalamus
The lateral geniculate nucleus (LGN) of the thalamus is a major retinal target, involved in processing and relaying visual information, including direction selectivity (DS) and orientation selectivity (OS). How DS and OS are organized in the LGN is poorly understood, as well as whether this information is directly inherited from the retina or generated de novo within the LGN. Using extracellular recordings from across the mouse LGN, we studied DS and OS responses and their topographic organization. We found that DS responses are absent in the central visual field, and that their preferred directions are topographically aligned to match translational optic flow patterns in the remaining visual field. OS responses were uniformly distributed throughout the visual field. By eliminating retinal DS in transgenic mice, we found that DS- but not OS-responses in the LGN were dependent on retinal DS. Thus, LGN DS is inherited from the retina, but retinogeniculate transfer may be topography-dependent, optimizing representations that support visually-guided behaviors.
Designing Language Models to Think Like Humans
Lecture
Thursday, July 11, 2024
Hour: 11:00 - 12:00
Location:
Gerhard M.J. Schmidt Lecture Hall
Designing Language Models to Think Like Humans
Dr. Chen Shani
Post-doctoral researcher NLP group
Stanford University
While language models (LMs) show impressive text manipulation capabilities, they also lack commonsense and reasoning abilities and are known to be brittle. In this talk, I will suggest a different LMs design paradigm, inspired by how humans understand it. I will present two papers, both shedding light on human-inspired NLP architectures aimed at delving deeper into the meaning beyond words.
The first paper [1] accounts for the lack of commonsense and reasoning abilities by proposing a paradigm shift in language understanding, drawing inspiration from embodied cognitive linguistics (ECL). In this position paper we propose a new architecture that treats language as inherently executable, grounded in embodied interaction, and driven by metaphoric reasoning.
The second paper [2] shows that LMs are brittle and far from human performance in their concept-understanding and abstraction capabilities. We argue this is due to their token-based objectives, and implement a concept-aware post-processing manipulation, showing it matches human intuition better. We then pave the way for more concept-aware training paradigms.
[1] Language (Re)modelling: Towards Embodied Language Understanding
Ronen Tamari, Chen Shani, Tom Hope, Miriam R L Petruck, Omri Abend, and Dafna Shahaf. 2020.
In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pages 6268–6281, Online. Association for Computational Linguistics.
[2] Towards Concept-Aware Large Language Models
Shani, Chen, Jilles Vreeken, and Dafna Shahaf.
In Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 13158-13170. 2023.
Bio: Chen Shani is a post-doctoral researcher at Stanford's NLP group, collaborating with Prof. Dan Jurafsky. Previously, she pursued her Ph.D. at the Hebrew University under the guidance of Prof. Dafna Shahaf and worked at Amazon Research. Her focus lies at the intersection of humans and NLP, where she implements insights from human cognition to improve NLP systems.
This decision, not just the average decision: Factors contributing to one single perceptual judgment
Lecture
Tuesday, July 9, 2024
Hour: 12:30 - 13:30
Location:
Gerhard M.J. Schmidt Lecture Hall
This decision, not just the average decision: Factors contributing to one single perceptual judgment
Prof. Mathew E. Diamond
Cognitive Neuroscience, SISSA Trieste, Italy
While cognitive neuroscientists have uncovered principles of perceptual decision-making by analyzing choices and neuronal firing across thousands of trials, we do not yet know the behavioral or neuronal dynamics underlying one SINGLE choice. For instance, why might a subject judge a given stimulus in category A 70% of the time but in category B 30%? Until we can work out precisely what determines single-decisions – this choice, right now – the mechanisms of real-world decision-making will remain unknown. In tactile psychophysical tasks with rats and humans, we are trying to sort out factors that explain the variability in judgments (across trials) to the identical stimulus input. We identify four factors: (i) trial-to-trial fluctuations in sensory coding, (ii) temporal context, namely, the history of preceding stimuli and choices, (iii) attention, and (iv) bias (predictions originating in beliefs about the environment’s probabilistic structure). The strategy is to bring these factors under experimental control, rather than leaving them to vary according to uninterrogated states within the subject. Psychophysics from rats and humans show that large chunks of variability are accounted for by these factors; evidence from cortical neuronal populations in rats provides some mechanistic grounding.
Reading Minds & Machines-AND-The Wisdom of a Crowd of Brains
Lecture
Tuesday, June 25, 2024
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Reading Minds & Machines-AND-The Wisdom of a Crowd of Brains
Prof. Michal Irani
Dept of Computer Science & Applied Mathematics, WIS
1. Can we reconstruct images that a person saw, directly from his/her fMRI brain recordings?
2. Can we reconstruct the training data that a deep-network trained on, directly from the parameters of the network?
The answer to both of these intriguing questions is “Yes!”
In this talk I will show how these can be done. I will then show how exploring the two domains in tandem can potentially lead to significant breakthroughs in both fields. More specifically:
(i) I will show how combining the power of Brains & Machines can potentially be used to bridge the gap between those two domains.
(ii) Combining the power of Multiple Brains (scanned on different fMRI scanners with NO shared stimuli) can lead to new breakthroughs and discoveries in Brain-Science. We refer to this as “the Wisdom of a Crowd of Brains”. In particular, we show that a Universal Encoder can be trained on multiple brains with no shared data, and that information can be functionally mapped between different brains.
Pages
2024
, 2024
Perceptual decision coding is inherently coupled to action in the mouse cortex
Lecture
Sunday, December 29, 2024
Hour: 12:00 - 13:15
Location:
Max and Lillian Candiotty Building
Perceptual decision coding is inherently coupled to action in the mouse cortex
Michael Sokoletsky PhD Defense
<p>Student Seminar-PhD Thesis Defense</p>
<p>How do animals make perceptual decisions about sensory stimuli to guide motor actions? One hypothesis is that dedicated "perceptual decision" cells process sensory information and drive the appropriate action. Alternatively, perceptual decisions result from competition among cells driving different actions, making decisions inherently coupled to actions. To distinguish between these hypotheses, we designed a vibrotactile detection task in which mice flexibly switched between standard and reversed contingency blocks, respectively requiring them to lick after stimulus presence or absence. Optogenetic inactivation of somatosensory and secondary motor cortices reduced stimulus sensitivity without impairing the ability to lick. However, widefield and two-photon imaging found that differences in cortical activity across perceptual decisions were almost exclusively action-coupled. In addition, we identified a subset of cells that encoded the current contingency block in a gated manner, enabling mice to flexibly make decisions without relying on action-independent decision coding.</p>
Deep language models as a cognitive model for natural language processing in the human brain
Lecture
Thursday, December 26, 2024
Hour: 12:30 - 13:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Deep language models as a cognitive model for natural language processing in the human brain
Prof. Uri Hasson
<p>Special Seminar</p>
<p>Naturalistic experimental paradigms in cognitive neuroscience arose from a pressure to test, in real-world contexts, the validity of models we derive from highly controlled laboratory experiments. In many cases, however, such efforts led to the realization that models (i.e., explanatory principles) developed under particular experimental manipulations fail to capture many aspects of reality (variance) in the real world. Recent advances in artificial neural networks provide an alternative computational framework for modeling cognition in natural contexts. In this talk, I will ask whether the human brain's underlying computations are similar or different from the underlying computations in deep neural networks, focusing on the underlying neural process that supports natural language processing in adults and language development in children. I will provide evidence for some shared computational principles between deep language models and the neural code for natural language processing in the human brain. This indicates that, to some extent, the brain relies on overparameterized optimization methods to comprehend and produce language. At the same time, I will present evidence that the brain differs from deep language models as speakers try to convey new ideas and thoughts. Finally, I will discuss our ongoing attempt to use deep acoustic-to-speech-to-language models to model language acquisition in children. </p>
Anterior-Posterior Insula Circuit Mediates Retrieval of a Conditioned Immune Response in Mice
Lecture
Tuesday, December 24, 2024
Hour: 12:30 - 13:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Anterior-Posterior Insula Circuit Mediates Retrieval of a Conditioned Immune Response in Mice
Prof. Kobi Rosenblum
<p>The brain can form associations between sensory information of inner and/or outer world (e.g. Pavlovian conditioning) but also between sensory information and the immune system. The phenomenon which was described in the last century is termed conditioned immune response (CIR) but very little is known about neuronal mechanisms subserving it. The conditioned stimulus can be a given taste and the unconditioned stimulus is an agent that induces or reduces a specific immune response. Over the last years, we and others revealed molecular and cellular mechanisms underlying taste valance representation in the anterior insular cortex (aIC). Recently, a circuit in the posterior insular cortex (pIC) encoding the internal representation of a given immune response was identified. Together, it allowed us to hypothesize and prove that the internal reciprocal connections between the anterior and posterior insula encode CIR. One can look at CIR as a noon declarative form of Nocebo effect and thus we demonstrate for the first time a detailed circuit mechanism for Placebo/Nocebo effect in the cortex.</p>
"Hot and Cold Thoughts"
Lecture
Tuesday, December 10, 2024
Hour: 12:30 - 13:30
Location:
Gerhard M.J. Schmidt Lecture Hall
"Hot and Cold Thoughts"
Prof. Oded Rechavi
<p>I will present two new and unpublished stories about what happens when the nervous system perceives temperature shifts</p><p></p>
Neuroprotective and Anticonvulsant Effects of Cannabinoids with Neurotrauma
Lecture
Thursday, December 5, 2024
Hour: 12:30 - 13:30
Location:
Neuroprotective and Anticonvulsant Effects of Cannabinoids with Neurotrauma
Prof.Linda Friedman
<p>Traumatic brain (TBI) injuries result in profound local hypoperfusion, ischemia, chronic inflammation and refractory seizures(post-traumatic epilepsy (PTE)), and restrict drug delivery to the site of impact so that peripheral treatment alone would have limited access to the site of injury during the most critical phases of neurotrauma. Cannabidiol (CBD), the major non-psychotropic cannabinoid, has anti-convulsant, anti-inflammatory, anti-nociceptive, antioxidant, and immuno-suppressive properties not fully understood. In pre-juvenile rats, microinjection of CBD attenuated kainate(KA)-induced seizures to a greater extent than intraperitoneal injection, indicating that local drug administration was more effective. In adult rats after experimental TBI, our modified CBD-infused implant applied extradural with oil injection supplementation restored vestibulomotorand cognitive functions compared to systemic treatment alone. We questioned whether the CBD or the low concentrations of THC in the extract was responsible for behavioral and cellular recovery.We hypothesized that an optimal ratio of cannabidiol (CBD) to tetrahydrocannabinol (THC) is required to protect against neuropathological consequences following TBI greater than either substance alone. Varied CBD:THC extract concentrations were compared with hempCBD lacking THC (CBD0). Neurons, glia, and parvalbumin interneurons (PV-INs) were evaluated. Weight loss was observed following high doses of THC dominant cannabis, THC100:1. Neuroscoresand vestibulomotorperformance were restored more with CBD:THC300:1-10:1. However, THC dominant treatments resulted in early onset to spontaneous seizures post-TBI. In a non-reward T-maze, the CBD10:1group had the highest alternation rates; TBI + vehicle, CBD0, CBD1:1, and THC100:1treatment groups had the lowest. The novel object recognition memory task showed CBD300:1treated animals had the best performance, while TBI or THC100:1treated groups had the worst. The forced swim test (FST) showed immobility time was highest after TBI and lowest after THC100:1treatment. The elevated plus maze (EPM) revealed the CBD0group spent the most time in closed arms. Both tests indicate that reduced anxiety was THC dependent. All combinations resulted in reduced injury but CBD10:1and THC20:1gave the most protection and THC100:1the least. Reduced anxiety level was THC dependent but higher doses were pro-convulsant cautioning THC dosing. Reduced GFAP labeling was highest with CBD dominant cannabis supporting its neuroprotective role against inflammation. Rescue of diminished bilateral PV-INs was observed within the hippocampus and medial prefrontal cortex (mPFC) with CBD dominant treatment (CBD300, CBD0) supporting their anticonvulsant effect. Loss of PV-INs with THC dominant treatment supports their proconvulsant effect. Thus, CBD and THC have different beneficial therapeutic effects indicating an optimal concentration ratio is critical for optimal neuropathological therapeutics.</p><p>Light refreshments before the seminar</p>
The Evolution of 7T (and Beyond) MRI in Basic Research and Clinical Practice
Lecture
Tuesday, December 3, 2024
Hour: 12:30 - 13:30
Location:
Gerhard M.J. Schmidt Lecture Hall
The Evolution of 7T (and Beyond) MRI in Basic Research and Clinical Practice
Prof. Noam Harel
Center for Magnetic Resonance Research, University of Minnesota, Minneapolis
The Center for Magnetic Resonance Research (CMRR) has been at the forefront of magnetic resonance imaging (MRI) innovation, pioneering ultra-high field (7 Tesla and above) technologies that are revolutionizing brain research and clinical care. This presentation will explore CMRR's groundbreaking journey, from the first functional MRI study to development of high-resolution fMRI capabilities revealing cortical columns within the human cortex. The presentation will also explore the translation of these technologies into clinical practice, with a focus on the unique visualization capabilities of 7T MRI, particularly for enhancing the precision of Deep Brain Stimulation (DBS) procedures.By exploring the progression from the 7T system to the world’s first 10.5T human MRI, this presentation will illustrate how these transformative technologies have pushed the limits of imaging science, uncovering new insights into brain function and advancing personalized clinical care at the intersection of technology, research, and medicine.
The role of neurons in the direction-selective retinal circuit in visual processing in the retina and in the visual thalamus
Lecture
Monday, October 14, 2024
Hour: 15:00 - 17:00
Location:
Gerhard M.J. Schmidt Lecture Hall
The role of neurons in the direction-selective retinal circuit in visual processing in the retina and in the visual thalamus
Alina Heukamp-Prof. Michal Rivlin Lab
Student Seminar-PhD Thesis Defense
The role of neurons in the direction-selective retinal circuit in visual processing in the retina and in the visual thalamus
The lateral geniculate nucleus (LGN) of the thalamus is a major retinal target, involved in processing and relaying visual information, including direction selectivity (DS) and orientation selectivity (OS). How DS and OS are organized in the LGN is poorly understood, as well as whether this information is directly inherited from the retina or generated de novo within the LGN. Using extracellular recordings from across the mouse LGN, we studied DS and OS responses and their topographic organization. We found that DS responses are absent in the central visual field, and that their preferred directions are topographically aligned to match translational optic flow patterns in the remaining visual field. OS responses were uniformly distributed throughout the visual field. By eliminating retinal DS in transgenic mice, we found that DS- but not OS-responses in the LGN were dependent on retinal DS. Thus, LGN DS is inherited from the retina, but retinogeniculate transfer may be topography-dependent, optimizing representations that support visually-guided behaviors.
Designing Language Models to Think Like Humans
Lecture
Thursday, July 11, 2024
Hour: 11:00 - 12:00
Location:
Gerhard M.J. Schmidt Lecture Hall
Designing Language Models to Think Like Humans
Dr. Chen Shani
Post-doctoral researcher NLP group
Stanford University
While language models (LMs) show impressive text manipulation capabilities, they also lack commonsense and reasoning abilities and are known to be brittle. In this talk, I will suggest a different LMs design paradigm, inspired by how humans understand it. I will present two papers, both shedding light on human-inspired NLP architectures aimed at delving deeper into the meaning beyond words.
The first paper [1] accounts for the lack of commonsense and reasoning abilities by proposing a paradigm shift in language understanding, drawing inspiration from embodied cognitive linguistics (ECL). In this position paper we propose a new architecture that treats language as inherently executable, grounded in embodied interaction, and driven by metaphoric reasoning.
The second paper [2] shows that LMs are brittle and far from human performance in their concept-understanding and abstraction capabilities. We argue this is due to their token-based objectives, and implement a concept-aware post-processing manipulation, showing it matches human intuition better. We then pave the way for more concept-aware training paradigms.
[1] Language (Re)modelling: Towards Embodied Language Understanding
Ronen Tamari, Chen Shani, Tom Hope, Miriam R L Petruck, Omri Abend, and Dafna Shahaf. 2020.
In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pages 6268–6281, Online. Association for Computational Linguistics.
[2] Towards Concept-Aware Large Language Models
Shani, Chen, Jilles Vreeken, and Dafna Shahaf.
In Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 13158-13170. 2023.
Bio: Chen Shani is a post-doctoral researcher at Stanford's NLP group, collaborating with Prof. Dan Jurafsky. Previously, she pursued her Ph.D. at the Hebrew University under the guidance of Prof. Dafna Shahaf and worked at Amazon Research. Her focus lies at the intersection of humans and NLP, where she implements insights from human cognition to improve NLP systems.
This decision, not just the average decision: Factors contributing to one single perceptual judgment
Lecture
Tuesday, July 9, 2024
Hour: 12:30 - 13:30
Location:
Gerhard M.J. Schmidt Lecture Hall
This decision, not just the average decision: Factors contributing to one single perceptual judgment
Prof. Mathew E. Diamond
Cognitive Neuroscience, SISSA Trieste, Italy
While cognitive neuroscientists have uncovered principles of perceptual decision-making by analyzing choices and neuronal firing across thousands of trials, we do not yet know the behavioral or neuronal dynamics underlying one SINGLE choice. For instance, why might a subject judge a given stimulus in category A 70% of the time but in category B 30%? Until we can work out precisely what determines single-decisions – this choice, right now – the mechanisms of real-world decision-making will remain unknown. In tactile psychophysical tasks with rats and humans, we are trying to sort out factors that explain the variability in judgments (across trials) to the identical stimulus input. We identify four factors: (i) trial-to-trial fluctuations in sensory coding, (ii) temporal context, namely, the history of preceding stimuli and choices, (iii) attention, and (iv) bias (predictions originating in beliefs about the environment’s probabilistic structure). The strategy is to bring these factors under experimental control, rather than leaving them to vary according to uninterrogated states within the subject. Psychophysics from rats and humans show that large chunks of variability are accounted for by these factors; evidence from cortical neuronal populations in rats provides some mechanistic grounding.
Reading Minds & Machines-AND-The Wisdom of a Crowd of Brains
Lecture
Tuesday, June 25, 2024
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Reading Minds & Machines-AND-The Wisdom of a Crowd of Brains
Prof. Michal Irani
Dept of Computer Science & Applied Mathematics, WIS
1. Can we reconstruct images that a person saw, directly from his/her fMRI brain recordings?
2. Can we reconstruct the training data that a deep-network trained on, directly from the parameters of the network?
The answer to both of these intriguing questions is “Yes!”
In this talk I will show how these can be done. I will then show how exploring the two domains in tandem can potentially lead to significant breakthroughs in both fields. More specifically:
(i) I will show how combining the power of Brains & Machines can potentially be used to bridge the gap between those two domains.
(ii) Combining the power of Multiple Brains (scanned on different fMRI scanners with NO shared stimuli) can lead to new breakthroughs and discoveries in Brain-Science. We refer to this as “the Wisdom of a Crowd of Brains”. In particular, we show that a Universal Encoder can be trained on multiple brains with no shared data, and that information can be functionally mapped between different brains.
Pages
2024
, 2024
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