All events, All years

Mean-field models for finite-size populations of spiking neurons

Lecture
Date:
Monday, June 8, 2020
Hour: 10:00
Location:
Dr. Tilo Schwalger
|
Institute for Mathematics Technical University of Berlin

Firing-rate (FR) or neural-mass models are widely used for studying computations performed by neural populations. Despite their success, classical firing-rate models do not capture spike timing effects on the microscopic level such as spike synchronization and are difficult to link to spiking data in experimental recordings. For large neuronal populations, the gap between the spiking neuron dynamics on the microscopic level and coarse-grained FR models on the population level can be bridged by mean-field theory formally valid for infinitely many neurons. It remains however challenging to extend the resulting mean-field models to finite-size populations with biologically realistic neuron numbers per cell type (mesoscopic scale). In this talk, I present a mathematical framework for mesoscopic populations of generalized integrate-and-fire neuron models that accounts for fluctuations caused by the finite number of neurons. To this end, I will introduce the refractory density method for quasi-renewal processes and show how this method can be generalized to finite-size populations. To demonstrate the flexibility of this approach, I will show how synaptic short-term plasticity can be incorporated in the mesoscopic mean-field framework. On the other hand, the framework permits a systematic reduction to low-dimensional FR equations using the eigenfunction method. Our modeling framework enables a re-examination of classical FR models in computational neuroscience under biophysically more realistic conditions.

Individual differences in decision-making under uncertainty: a neuroeconomic approach

Lecture
Date:
Tuesday, May 19, 2020
Hour: 12:30
Location:
Prof. Ifat Levy
|
Decision Neuroscience Lab Yale School of Medicine

Individuals differ substantially in their attitudes to uncertainty: some avoid is at all costs, while others are tolerant of, or even seek, uncertainty. These differences are important, because uncertainty is everywhere – how we cope with uncertainty can have significant implications for our mental health and quality of life. I will describe a series of studies in which we characterize individual differences in decision-making under uncertainty, and use these characterizations to study the neural mechanisms of decision-making under uncertainty and variations in these mechanisms in mental illness.

From sensory perception to decision making in bats

Lecture
Date:
Tuesday, May 12, 2020
Hour: 12:30
Location:
Prof. Yossi Yovel
|
Faculty of Life Sciences Tel Aviv University

From Cognition to Depression: Using Magnetic Resonance Spectroscopy to Study In-vivo Neurochemistry

Lecture
Date:
Tuesday, March 3, 2020
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Dr. Assaf Tal
|
Dept of Chemical & Biological Physics Faculty of Chemistry, WIS

Magnetic Resonance Spectroscopy (MRS) can be used to measure the in-vivo concentrations of several metabolites in the brain non-invasively. I will present our work using MRS to study two aspects of brain metabolism. First, I'll talk about our work on functional MRS, whereby we look at neurochemical changes during or after learning or function. In the second half of the talk, I will focus on new methods we're developing in the lab, and in particular on our ability to measure the thermal relaxation times of metabolites, which probe specific cellular and subcellular microenvironments. I will present some preliminary data showing where and how this could be useful.

Synaptic markers in the reward system for the predisposition to overeat

Lecture
Date:
Tuesday, February 25, 2020
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Dr. Yonatan Kupchik
|
Dept of Medical Neurobiology Faculty of Medicine The Institute for Medical Research Israel-Canada (IMRIC), The Hebrew University of Jerusalem

Obesity is a complex disease with its roots in the physiology of various brain circuits. Although much progress has been made in understanding the disease, the most fundamental question remains unanswered – why do we overeat? As Clifford Saper (Harvard) points out, “if feeding were controlled solely by homeostatic mechanisms, most of us would be at our ideal body weight, and people would consider feeding like breathing or elimination, a necessary but unexciting part of existence”. Clearly this is not the case; hedonic eating has come increasing under the spotlight in recent years as a main driver of obesity. As food becomes more and more rewarding, could overeating be driven by a pathological search for reward? In my talk I will demonstrate that chronic diet of highly-palatable food changes the physiology of the reward system and that mice that gained the most weight differ from those that gained the least weight in the physiology of two regions of the reward system – the nucleus accumbens and the ventral pallidum. Furthermore, I will show that long term plasticity in the ventral pallidum may be an innate marker for the predisposition to overeat palatable food.

A common neuronal mechanism underlying free and creative behavior in the human brain

Lecture
Date:
Tuesday, February 11, 2020
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Prof. Rafael Malach
|
Dept of Neurobiology, WIS

Free behavior is likely the most fundamental and essential aspect of human life. It underlies our unique ability to self-generate actions and come up with creative and original solutions. Yet, the brain mechanism that drives such free and creative behaviors remains unknown. In my talk I will present experimental findings supporting the hypothesis that ultra-slow spontaneous (resting state) activity fluctuations are a central and ubiquitous mechanism underlying all types of free behavior. Traces of slow resting state fluctuations can account for the intriguing observation that free behaviors of all types- from generating names to free recall of visual images- are invariably preceded by a wave of slow (1-4 seconds) activity buildup. This buildup can be observed in BOLD-fMRI, intracranial recording of single neurons and more recently, in the massive hippocampal bursts called Sharp Wave Ripples. Could the similar slow dynamics of the spontaneous fluctuations and the anticipatory buildup preceding free behaviors be a mere coincidence? Crucially, I will present evidence that individual differences in the waveforms of spontaneous fluctuations measured during are significantly correlated to the shape of the buildup wave anticipating free and creative events. The critical role of spontaneous activity fluctuations in generating creative decisions is reminiscent of the use of stochastic noise in optimizing solutions in network models.

Effects of dopamine on response properties of distinct types of retinal ganglion cells

Lecture
Date:
Wednesday, February 5, 2020
Hour: 15:00
Location:
Nella and Leon Benoziyo Building for Brain Research
Lior Pinkus (PhD Thesis Defense)
|
Dr. Michal Rivlin Lab Dept of Neurobiology

Whole-brain fMRI of the Behaving Mouse

Lecture
Date:
Tuesday, February 4, 2020
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Prof. Itamar Kahn
|
Faculty of Medicine, Technion, Haifa

Functional MRI is used pervasively in human brain research, enabling characterization of distributed brain activity underlying complex perceptual and cognitive processes. However, heretofore this technique has been limited in utility in rodents. I will present whole-brain functional imaging of head-fixed mice performing go/no-go odor discrimination in a platform allowing precise odor-delivery system, non-invasive sniff recordings and lick detection, detailing the brain regions subserving this behavior from the naïve state to task proficiency including learning of rule reversal. I will briefly discuss efforts to expand the mouse fMRI platform to additional modalities and conclude by describing the prospects of this approach more broadly.

PhD Thesis Defense - Spatial and temporal integration in perceptual calibration

Lecture
Date:
Thursday, January 30, 2020
Hour: 10:30
Location:
Nella and Leon Benoziyo Building for Brain Research
Ron Dekel (PhD Thesis Defense)
|
Prof. Dov Sagi Lab Dept of Neurobiology

Processing of a visual stimulus depends on previous and surrounding stimulations. For example, how an orientation detail is perceived depends on previous and surrounding orientation content. The influence of such context, temporal and spatial, is postulated to be beneficial, but the involved mechanism(s) as well as the behavioral relevance are not fully understood. Here, using behavioral experiments that measure how context integrates in space and time, we argue that context changes how statistical decisions are made by the visual system. Most importantly, we find that several context-dependent perceptual biases, such as visual illusions and aftereffects, are much reduced with increasing reaction time. To account for this, we consider a simple yet general explanation: prior and noisy decision-related evidence are integrated serially, with evidence and noise accumulating over time (as in the standard drift diffusion model). With time, owing to noise accumulation, the prior effect is predicted to diminish. This theory suggests a single-process alternative to the intuitive notion of dual brain systems (the so-called System 1 and System 2), and quantitatively predicts several known properties of perceptual bias, such as the order-of-magnitude variation in measured bias magnitudes between individuals.

New methods for identifying latent manifold structure from neural data

Lecture
Date:
Tuesday, January 28, 2020
Hour: 14:00
Location:
Gerhard M.J. Schmidt Lecture Hall
Prof. Jonathan Pillow
|
Dept of Psychology, Princeton University

An important problem in neuroscience is to identify low-dimensional structure underlying noisy, high-dimensional spike trains. In this talk, I will discuss recent advances for tackling this problem in single and multi-region neural datasets. First, I will discuss the Gaussian Process Latent Variable Model with Poisson observations (Poisson-GPLVM), which seeks to identify a low-dimensional nonlinear manifold from spike train data. This model can successfully handle datasets that appear high-dimensional with linear dimensionality reduction methods like PCA, and we show that it can identify a 2D spatial map underlying hippocampal place cell responses from their spike trains alone. Second, I will discuss recent extensions to Poisson-spiking Gaussian Process Factor Analysis (Poisson-GPFA), which incorporates separate signal and noise dimensions as well as a multi-region model with coupling between latent variables governing activity in different regions. This model provides a powerful tool for characterizing the flow of signals between brain areas, and we illustrate its applicability using multi-region recordings from mouse visual cortex.

Pages

All events, All years

Mean-field models for finite-size populations of spiking neurons

Lecture
Date:
Monday, June 8, 2020
Hour: 10:00
Location:
Dr. Tilo Schwalger
|
Institute for Mathematics Technical University of Berlin

Firing-rate (FR) or neural-mass models are widely used for studying computations performed by neural populations. Despite their success, classical firing-rate models do not capture spike timing effects on the microscopic level such as spike synchronization and are difficult to link to spiking data in experimental recordings. For large neuronal populations, the gap between the spiking neuron dynamics on the microscopic level and coarse-grained FR models on the population level can be bridged by mean-field theory formally valid for infinitely many neurons. It remains however challenging to extend the resulting mean-field models to finite-size populations with biologically realistic neuron numbers per cell type (mesoscopic scale). In this talk, I present a mathematical framework for mesoscopic populations of generalized integrate-and-fire neuron models that accounts for fluctuations caused by the finite number of neurons. To this end, I will introduce the refractory density method for quasi-renewal processes and show how this method can be generalized to finite-size populations. To demonstrate the flexibility of this approach, I will show how synaptic short-term plasticity can be incorporated in the mesoscopic mean-field framework. On the other hand, the framework permits a systematic reduction to low-dimensional FR equations using the eigenfunction method. Our modeling framework enables a re-examination of classical FR models in computational neuroscience under biophysically more realistic conditions.

Individual differences in decision-making under uncertainty: a neuroeconomic approach

Lecture
Date:
Tuesday, May 19, 2020
Hour: 12:30
Location:
Prof. Ifat Levy
|
Decision Neuroscience Lab Yale School of Medicine

Individuals differ substantially in their attitudes to uncertainty: some avoid is at all costs, while others are tolerant of, or even seek, uncertainty. These differences are important, because uncertainty is everywhere – how we cope with uncertainty can have significant implications for our mental health and quality of life. I will describe a series of studies in which we characterize individual differences in decision-making under uncertainty, and use these characterizations to study the neural mechanisms of decision-making under uncertainty and variations in these mechanisms in mental illness.

From sensory perception to decision making in bats

Lecture
Date:
Tuesday, May 12, 2020
Hour: 12:30
Location:
Prof. Yossi Yovel
|
Faculty of Life Sciences Tel Aviv University

From Cognition to Depression: Using Magnetic Resonance Spectroscopy to Study In-vivo Neurochemistry

Lecture
Date:
Tuesday, March 3, 2020
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Dr. Assaf Tal
|
Dept of Chemical & Biological Physics Faculty of Chemistry, WIS

Magnetic Resonance Spectroscopy (MRS) can be used to measure the in-vivo concentrations of several metabolites in the brain non-invasively. I will present our work using MRS to study two aspects of brain metabolism. First, I'll talk about our work on functional MRS, whereby we look at neurochemical changes during or after learning or function. In the second half of the talk, I will focus on new methods we're developing in the lab, and in particular on our ability to measure the thermal relaxation times of metabolites, which probe specific cellular and subcellular microenvironments. I will present some preliminary data showing where and how this could be useful.

Synaptic markers in the reward system for the predisposition to overeat

Lecture
Date:
Tuesday, February 25, 2020
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Dr. Yonatan Kupchik
|
Dept of Medical Neurobiology Faculty of Medicine The Institute for Medical Research Israel-Canada (IMRIC), The Hebrew University of Jerusalem

Obesity is a complex disease with its roots in the physiology of various brain circuits. Although much progress has been made in understanding the disease, the most fundamental question remains unanswered – why do we overeat? As Clifford Saper (Harvard) points out, “if feeding were controlled solely by homeostatic mechanisms, most of us would be at our ideal body weight, and people would consider feeding like breathing or elimination, a necessary but unexciting part of existence”. Clearly this is not the case; hedonic eating has come increasing under the spotlight in recent years as a main driver of obesity. As food becomes more and more rewarding, could overeating be driven by a pathological search for reward? In my talk I will demonstrate that chronic diet of highly-palatable food changes the physiology of the reward system and that mice that gained the most weight differ from those that gained the least weight in the physiology of two regions of the reward system – the nucleus accumbens and the ventral pallidum. Furthermore, I will show that long term plasticity in the ventral pallidum may be an innate marker for the predisposition to overeat palatable food.

A common neuronal mechanism underlying free and creative behavior in the human brain

Lecture
Date:
Tuesday, February 11, 2020
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Prof. Rafael Malach
|
Dept of Neurobiology, WIS

Free behavior is likely the most fundamental and essential aspect of human life. It underlies our unique ability to self-generate actions and come up with creative and original solutions. Yet, the brain mechanism that drives such free and creative behaviors remains unknown. In my talk I will present experimental findings supporting the hypothesis that ultra-slow spontaneous (resting state) activity fluctuations are a central and ubiquitous mechanism underlying all types of free behavior. Traces of slow resting state fluctuations can account for the intriguing observation that free behaviors of all types- from generating names to free recall of visual images- are invariably preceded by a wave of slow (1-4 seconds) activity buildup. This buildup can be observed in BOLD-fMRI, intracranial recording of single neurons and more recently, in the massive hippocampal bursts called Sharp Wave Ripples. Could the similar slow dynamics of the spontaneous fluctuations and the anticipatory buildup preceding free behaviors be a mere coincidence? Crucially, I will present evidence that individual differences in the waveforms of spontaneous fluctuations measured during are significantly correlated to the shape of the buildup wave anticipating free and creative events. The critical role of spontaneous activity fluctuations in generating creative decisions is reminiscent of the use of stochastic noise in optimizing solutions in network models.

Effects of dopamine on response properties of distinct types of retinal ganglion cells

Lecture
Date:
Wednesday, February 5, 2020
Hour: 15:00
Location:
Nella and Leon Benoziyo Building for Brain Research
Lior Pinkus (PhD Thesis Defense)
|
Dr. Michal Rivlin Lab Dept of Neurobiology

Whole-brain fMRI of the Behaving Mouse

Lecture
Date:
Tuesday, February 4, 2020
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Prof. Itamar Kahn
|
Faculty of Medicine, Technion, Haifa

Functional MRI is used pervasively in human brain research, enabling characterization of distributed brain activity underlying complex perceptual and cognitive processes. However, heretofore this technique has been limited in utility in rodents. I will present whole-brain functional imaging of head-fixed mice performing go/no-go odor discrimination in a platform allowing precise odor-delivery system, non-invasive sniff recordings and lick detection, detailing the brain regions subserving this behavior from the naïve state to task proficiency including learning of rule reversal. I will briefly discuss efforts to expand the mouse fMRI platform to additional modalities and conclude by describing the prospects of this approach more broadly.

PhD Thesis Defense - Spatial and temporal integration in perceptual calibration

Lecture
Date:
Thursday, January 30, 2020
Hour: 10:30
Location:
Nella and Leon Benoziyo Building for Brain Research
Ron Dekel (PhD Thesis Defense)
|
Prof. Dov Sagi Lab Dept of Neurobiology

Processing of a visual stimulus depends on previous and surrounding stimulations. For example, how an orientation detail is perceived depends on previous and surrounding orientation content. The influence of such context, temporal and spatial, is postulated to be beneficial, but the involved mechanism(s) as well as the behavioral relevance are not fully understood. Here, using behavioral experiments that measure how context integrates in space and time, we argue that context changes how statistical decisions are made by the visual system. Most importantly, we find that several context-dependent perceptual biases, such as visual illusions and aftereffects, are much reduced with increasing reaction time. To account for this, we consider a simple yet general explanation: prior and noisy decision-related evidence are integrated serially, with evidence and noise accumulating over time (as in the standard drift diffusion model). With time, owing to noise accumulation, the prior effect is predicted to diminish. This theory suggests a single-process alternative to the intuitive notion of dual brain systems (the so-called System 1 and System 2), and quantitatively predicts several known properties of perceptual bias, such as the order-of-magnitude variation in measured bias magnitudes between individuals.

New methods for identifying latent manifold structure from neural data

Lecture
Date:
Tuesday, January 28, 2020
Hour: 14:00
Location:
Gerhard M.J. Schmidt Lecture Hall
Prof. Jonathan Pillow
|
Dept of Psychology, Princeton University

An important problem in neuroscience is to identify low-dimensional structure underlying noisy, high-dimensional spike trains. In this talk, I will discuss recent advances for tackling this problem in single and multi-region neural datasets. First, I will discuss the Gaussian Process Latent Variable Model with Poisson observations (Poisson-GPLVM), which seeks to identify a low-dimensional nonlinear manifold from spike train data. This model can successfully handle datasets that appear high-dimensional with linear dimensionality reduction methods like PCA, and we show that it can identify a 2D spatial map underlying hippocampal place cell responses from their spike trains alone. Second, I will discuss recent extensions to Poisson-spiking Gaussian Process Factor Analysis (Poisson-GPFA), which incorporates separate signal and noise dimensions as well as a multi-region model with coupling between latent variables governing activity in different regions. This model provides a powerful tool for characterizing the flow of signals between brain areas, and we illustrate its applicability using multi-region recordings from mouse visual cortex.

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Brain functions: from basic research to clinical applications

Conference
Date:
Monday, March 12, 2007
Hour:
Location:

Pages