2022
, 2022
Circuits for decisions, attention and working memory in the primate visual system
Lecture
Monday, January 10, 2022
Hour: 14:00 - 16:00
Location:
Circuits for decisions, attention and working memory in the primate visual system
Dr. Leor Katz
National Eye Institute, National Institutes of Health at Bethesda, MD
Making decisions, attending to certain items, and manipulating information in working memory are fundamental behaviors that rely on specific neural circuitry. Throughout my research I have contributed to understanding such behaviors in human and in nonhuman primates but found that despite tremendous advances in the field, we still lack a mechanistic understanding of what goes wrong in conditions such as dementia or autism. My long-term research goal is to determine the circuits that support cognitive behavior, in health and disease.
In my talk, I present three key contributions I have made towards uncovering neuronal circuits for cognition in the macaque, an animal model whose neural circuitry affords unique insight into human brain function. First, I demonstrate the utility of rigorous psychophysical frameworks in determining the causal contribution of key brain regions to behavior in a perceptual decision-making task. Next, I describe how causal manipulations of brain areas involved in attentional control can be used to identify hitherto unknown areas and reveal new functional circuits in support of selective attention and object recognition. Finally, I show how computational analyses of population data reveal circuits within circuits with distinct roles in supporting working memory.
I end the talk by presenting my future research directions and approach: to leverage my experience studying how we select from external information (from sensory signals) to investigate how we select from internal information (from information stored in visual working memory). By blending theory-driven experiments with large-scale electrophysiological recording and circuit-specific causal manipulations in behaving macaques, I aim to uncover how we select relevant information from working memory, and equally important, how we fail to do so when struck by disorders of executive or memory function.
Investigating the mechanisms underlying the stable coexistence of multiple maps for the same environment
Lecture
Wednesday, January 5, 2022
Hour: 10:00 - 11:00
Location:
Investigating the mechanisms underlying the stable coexistence of multiple maps for the same environment
Alice Eldar- MSc Thesis Defense
Prof. Yaniv Ziv, Lab
Dept of Brain Sciences, WIS
Hippocampal place cells fire at a high rate whenever an animal is in a specific location in an environment and are thought to support spatial and episodic memory. When an animal visits different environments, place cells typically ‘remap’ (i.e., change their preferred locations), and when revisiting the same environment, the same spatial code reemerges. In a recent study by our lab, place cells were shown to globally remap, forming multiple distinct representations (maps) of the same environment that stably coexist across time. In that study, switching between different maps of the same environment happened only after the mice were disconnected from the environment.
Here I performed a set of experiments to further understand the mechanism underlying switching between multiple maps. My project established a way to manipulate this mechanism, both through external orientation inputs and by acting directly on the hippocampal network state using optogenetics. My results provide support for the proposed role of head-direction or other orientation signals in the switching between maps. They also support the model of maps as stable attractors, where the specific attractor (map) used depends on the initial conditions of the network.
Zoom: https://weizmann.zoom.us/j/92871200575?pwd=WWdZbXVmM1R5RkFZYnpTajloelVTZz09
Meeting ID: 928 7120 0575
Password: 344121
Zoom Seminar - Using deep neural networks as cognitive models for how brains act in the natural world
Lecture
Tuesday, January 4, 2022
Hour: 12:30 - 13:30
Location:
Zoom Seminar - Using deep neural networks as cognitive models for how brains act in the natural world
Prof. Uri Hasson
Psychology Dept & the Neuroscience Institute,
Princeton University
Naturalistic experimental paradigms in neuroimaging arose from a pressure to test the validity of models we derive from highly controlled experiments in real-world contexts. In many cases, however, such efforts led to the realization that models developed under particular experimental manipulations failed to capture much variance outside the context of that manipulation. The critique of non-naturalistic experiments is not a recent development; it echoes a persistent and subversive thread in the history of modern psychology. The brain has evolved to guide behavior in a multidimensional world with many interacting variables. The assumption that artificially decoupling and manipulating these variables will lead to a good understanding of the brain may be untenable.
Recent advances in artificial neural networks provide an alternative computational framework to model cognition in natural contexts. In contrast to the simplified and interpretable hypotheses we test in the lab, these models do not learn simple, human-interpretable rules or representations of the world. Instead, they use local computations to interpolate over task-relevant manifolds in high-dimensional parameter space. Counterintuitively, over-parameterized deep neural models are parsimonious and straightforward, as they provide a versatile, robust solution for learning a diverse set of functions in natural contexts. Naturalistic paradigms should not be deployed as an afterthought if we hope to build models of brain and behavior that extend beyond the laboratory into the real world.
In my talk, I will discuss the relevance of deep neural models to cognition in the context of natural language and deep language models.
Zoom link-
https://weizmann.zoom.us/j/95406893197?pwd=REt5L1g3SmprMUhrK3dpUDJVeHlrZz09
Meeting ID: 954 0689 3197
Password: 750421
Pages
2022
, 2022
Investigating the mechanisms underlying the stable coexistence of multiple maps for the same environment
Lecture
Wednesday, January 5, 2022
Hour: 10:00 - 11:00
Location:
Investigating the mechanisms underlying the stable coexistence of multiple maps for the same environment
Alice Eldar- MSc Thesis Defense
Prof. Yaniv Ziv, Lab
Dept of Brain Sciences, WIS
Hippocampal place cells fire at a high rate whenever an animal is in a specific location in an environment and are thought to support spatial and episodic memory. When an animal visits different environments, place cells typically ‘remap’ (i.e., change their preferred locations), and when revisiting the same environment, the same spatial code reemerges. In a recent study by our lab, place cells were shown to globally remap, forming multiple distinct representations (maps) of the same environment that stably coexist across time. In that study, switching between different maps of the same environment happened only after the mice were disconnected from the environment.
Here I performed a set of experiments to further understand the mechanism underlying switching between multiple maps. My project established a way to manipulate this mechanism, both through external orientation inputs and by acting directly on the hippocampal network state using optogenetics. My results provide support for the proposed role of head-direction or other orientation signals in the switching between maps. They also support the model of maps as stable attractors, where the specific attractor (map) used depends on the initial conditions of the network.
Zoom: https://weizmann.zoom.us/j/92871200575?pwd=WWdZbXVmM1R5RkFZYnpTajloelVTZz09
Meeting ID: 928 7120 0575
Password: 344121
Zoom Seminar - Using deep neural networks as cognitive models for how brains act in the natural world
Lecture
Tuesday, January 4, 2022
Hour: 12:30 - 13:30
Location:
Zoom Seminar - Using deep neural networks as cognitive models for how brains act in the natural world
Prof. Uri Hasson
Psychology Dept & the Neuroscience Institute,
Princeton University
Naturalistic experimental paradigms in neuroimaging arose from a pressure to test the validity of models we derive from highly controlled experiments in real-world contexts. In many cases, however, such efforts led to the realization that models developed under particular experimental manipulations failed to capture much variance outside the context of that manipulation. The critique of non-naturalistic experiments is not a recent development; it echoes a persistent and subversive thread in the history of modern psychology. The brain has evolved to guide behavior in a multidimensional world with many interacting variables. The assumption that artificially decoupling and manipulating these variables will lead to a good understanding of the brain may be untenable.
Recent advances in artificial neural networks provide an alternative computational framework to model cognition in natural contexts. In contrast to the simplified and interpretable hypotheses we test in the lab, these models do not learn simple, human-interpretable rules or representations of the world. Instead, they use local computations to interpolate over task-relevant manifolds in high-dimensional parameter space. Counterintuitively, over-parameterized deep neural models are parsimonious and straightforward, as they provide a versatile, robust solution for learning a diverse set of functions in natural contexts. Naturalistic paradigms should not be deployed as an afterthought if we hope to build models of brain and behavior that extend beyond the laboratory into the real world.
In my talk, I will discuss the relevance of deep neural models to cognition in the context of natural language and deep language models.
Zoom link-
https://weizmann.zoom.us/j/95406893197?pwd=REt5L1g3SmprMUhrK3dpUDJVeHlrZz09
Meeting ID: 954 0689 3197
Password: 750421
Pages
2022
, 2022
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