All events, 2017

Parametric control of actions and its feed-forward nature

Lecture
Date:
Thursday, March 9, 2017
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Prof. Anatol G. Feldman
|
Dept of Neuroscience, University of Montreal and The Centre for Interdisciplinary Research in Rehabilitation, Montreal

The activity of different descending systems can be de-correlated from kinematic and kinetic variables describing the motor outcome to reveal that these systems are responsible for parametric shifts in balance in the interaction between the organism and environment. Such shifts also pre-determine the origin (referent) points of spatial frames reference in which actions are produced. Parametric (referent) control can be identified at any level of action production, from the level of a single motorneuron to the level involving motoneurons of multiple muscles of the body.

MIF as a therapeutic candidate for amyotrophic lateral sclerosis

Lecture
Date:
Tuesday, March 7, 2017
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Prof. Adrian Israelson
|
Dept of Physiology and Cell Biology Ben-Gurion University of the Negev, Be'er-Sheva

Cortical spike multiplexing using gamma frequency latencies

Lecture
Date:
Thursday, March 2, 2017
Hour: 12:45
Location:
Gerhard M.J. Schmidt Lecture Hall
Prof. Dana H. Ballard
|
Dept of Computer Sciences, University of Texas at Austin

Neuronal ensembles: emergent motifs of cortical function?

Lecture
Date:
Thursday, March 2, 2017
Hour: 11:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Prof. Rafael Yuste
|
Dept of Biological Sciences, Columbia University, NY

Oxytocin for autism? Insights from genetic mouse models

Lecture
Date:
Thursday, February 23, 2017
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Prof. Olga Penagarikano
|
Dept of Pharmacology, School of Medicine University of the Basque Country, Leioa, Spain

Autism Spectrum Disorder is a heterogeneous condition characterized by deficits in social interactions and repetitive behaviors/restricted interests. Mouse models based on human disease-causing mutations provide the potential for understanding associated neuropathology and developing targeted treatments. Genetic, neurobiological and imaging data provide convergent evidence for the CNTNAP2 gene as a risk factor for autism and other developmental disorders. First, I will present data from my postdoctoral work demonstrating construct, face and predictive validity of a mouse knockout for the Cntnap2 gene, providing a tool for mechanistic and therapeutic research. In fact, through an in vivo drug screen in this model we found that administration of the neuropeptide oxytocin dramatically improves social deficits. Strikingly, reduced neuropeptide levels in this model seemed to account for the behavioral response. Last, I will present ongoing work in my lab evaluating the oxytocin system and related neurotransmitters in this model. Alterations in the oxytocin system and/or dysfunction in its related biological processes could potentially be more common in autism than previously anticipated.

A Circuits First Approach to Mental Illness

Lecture
Date:
Tuesday, February 21, 2017
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Prof. Amit Etkin
|
Dept of Psychiatry and Behavioral Sciences Stanford Neurosciences Institute, Stanford University and Investigator, Sierra-Pacific MIRECC, Palo Alto VA

The interplay between learning systems and their impact on long-term declarative memory

Lecture
Date:
Tuesday, February 14, 2017
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Dr. Avi Mendelsohn
|
Dept of Neurobiology, Faculty of Life Sciences, University of Haifa

Nonlinear coherences among multiple time-series:Use of MRI data to identify brain temporal organization and directionality of information flow

Lecture
Date:
Thursday, February 9, 2017
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Prof. Gadi Goelman
|
Human Biology Research Center, Dept of Nuclear Medicine, Hadassah Medical Center, Jerusalem

Coherences and time-lags are commonly used to infer directionality of information flow in electrophysiology EEG, MEG and MRI. Current approaches, however, enable to calculate only pairwise (linear) coherences. I will describe a novel high-order statistical framework to calculate coherences among multiple coupled time-series. The full mathematical expressions for 4 time-series will be described and its validity will be demonstrated by computer simulations of the Kuramoto model. Quartets of time-series (i.e. brain regions) will be defined as linear, nonlinear or of higher (>4) order. By this, whole systems (e.g. motor, visual) will be categorized as linear or nonlinear. Based on the assumption that MRI phase delays are associated with time of information flow, the temporal hierarchy and directionality of several brain systems will be described. To fully categorize the information flow within 4th order networks, I will introduce the concept of Motifs that describes the pathway trajectories within networks. The advantages of motifs in brain research will be demonstrated by comparing motifs of the ventral versus the dorsal streams systems and in males versus females.

Why Sensory Deprivation and High Plasticity may lead to Hallucinations and Synaesthesia:A Computational Perspective

Lecture
Date:
Tuesday, February 7, 2017
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Dr. Oren Shriki
|
Dept of Cognitive and Brain Sciences Ben-Gurion University

Recurrent connections are abundant in cortical circuitry but their functional role has been the subject of intense debates. The talk will present a computational approach to investigate the role of recurrent connections in the context of sensory processing. Specifically, I will describe a neural network model in which the recurrent connections evolve according to concrete learning rules that optimize the information representation of the network. Interestingly, these networks tend to operate near a "critical" point in their dynamics, namely close to a phase of "hallucinations", in which non-trivial spontaneous patterns of activity evolve even without structured input. Various scenarios, such as attenuation of the external inputs or increased plasticity, can lead the network to cross the border into the hallucinatory phase. The theory will be illustrated through applications to a model of a visual hypercolumn, a model of tinnitus and a model of synaesthesia. References: Shriki O. and Yellin D., Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neural Network. PLoS Computational Biology 12(2): e1004698. doi:10.1371/journal.pcbi.1004698, 2016 Shriki O., Sadeh Y. and Ward J., The Emergence of Synaesthesia in a Neuronal Network Model via Changes in Perceptual Sensitivity and Plasticity. PLoS Computational Biology 12(7): e1004959. doi:10.1371/journal.pcbi.1004959, 2016.

Cellular substrates for network information processing in hippocampal CA1

Lecture
Date:
Thursday, February 2, 2017
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Dr. Alessio Attardo
|
Dept of Stress Neurobiology and Neurogenetics Max Planck Institute of Psychiatry, Munich

Pages

All events, 2017

MIF as a therapeutic candidate for amyotrophic lateral sclerosis

Lecture
Date:
Tuesday, March 7, 2017
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Prof. Adrian Israelson
|
Dept of Physiology and Cell Biology Ben-Gurion University of the Negev, Be'er-Sheva

Cortical spike multiplexing using gamma frequency latencies

Lecture
Date:
Thursday, March 2, 2017
Hour: 12:45
Location:
Gerhard M.J. Schmidt Lecture Hall
Prof. Dana H. Ballard
|
Dept of Computer Sciences, University of Texas at Austin

Neuronal ensembles: emergent motifs of cortical function?

Lecture
Date:
Thursday, March 2, 2017
Hour: 11:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Prof. Rafael Yuste
|
Dept of Biological Sciences, Columbia University, NY

Oxytocin for autism? Insights from genetic mouse models

Lecture
Date:
Thursday, February 23, 2017
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Prof. Olga Penagarikano
|
Dept of Pharmacology, School of Medicine University of the Basque Country, Leioa, Spain

Autism Spectrum Disorder is a heterogeneous condition characterized by deficits in social interactions and repetitive behaviors/restricted interests. Mouse models based on human disease-causing mutations provide the potential for understanding associated neuropathology and developing targeted treatments. Genetic, neurobiological and imaging data provide convergent evidence for the CNTNAP2 gene as a risk factor for autism and other developmental disorders. First, I will present data from my postdoctoral work demonstrating construct, face and predictive validity of a mouse knockout for the Cntnap2 gene, providing a tool for mechanistic and therapeutic research. In fact, through an in vivo drug screen in this model we found that administration of the neuropeptide oxytocin dramatically improves social deficits. Strikingly, reduced neuropeptide levels in this model seemed to account for the behavioral response. Last, I will present ongoing work in my lab evaluating the oxytocin system and related neurotransmitters in this model. Alterations in the oxytocin system and/or dysfunction in its related biological processes could potentially be more common in autism than previously anticipated.

A Circuits First Approach to Mental Illness

Lecture
Date:
Tuesday, February 21, 2017
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Prof. Amit Etkin
|
Dept of Psychiatry and Behavioral Sciences Stanford Neurosciences Institute, Stanford University and Investigator, Sierra-Pacific MIRECC, Palo Alto VA

The interplay between learning systems and their impact on long-term declarative memory

Lecture
Date:
Tuesday, February 14, 2017
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Dr. Avi Mendelsohn
|
Dept of Neurobiology, Faculty of Life Sciences, University of Haifa

Nonlinear coherences among multiple time-series:Use of MRI data to identify brain temporal organization and directionality of information flow

Lecture
Date:
Thursday, February 9, 2017
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Prof. Gadi Goelman
|
Human Biology Research Center, Dept of Nuclear Medicine, Hadassah Medical Center, Jerusalem

Coherences and time-lags are commonly used to infer directionality of information flow in electrophysiology EEG, MEG and MRI. Current approaches, however, enable to calculate only pairwise (linear) coherences. I will describe a novel high-order statistical framework to calculate coherences among multiple coupled time-series. The full mathematical expressions for 4 time-series will be described and its validity will be demonstrated by computer simulations of the Kuramoto model. Quartets of time-series (i.e. brain regions) will be defined as linear, nonlinear or of higher (>4) order. By this, whole systems (e.g. motor, visual) will be categorized as linear or nonlinear. Based on the assumption that MRI phase delays are associated with time of information flow, the temporal hierarchy and directionality of several brain systems will be described. To fully categorize the information flow within 4th order networks, I will introduce the concept of Motifs that describes the pathway trajectories within networks. The advantages of motifs in brain research will be demonstrated by comparing motifs of the ventral versus the dorsal streams systems and in males versus females.

Why Sensory Deprivation and High Plasticity may lead to Hallucinations and Synaesthesia:A Computational Perspective

Lecture
Date:
Tuesday, February 7, 2017
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Dr. Oren Shriki
|
Dept of Cognitive and Brain Sciences Ben-Gurion University

Recurrent connections are abundant in cortical circuitry but their functional role has been the subject of intense debates. The talk will present a computational approach to investigate the role of recurrent connections in the context of sensory processing. Specifically, I will describe a neural network model in which the recurrent connections evolve according to concrete learning rules that optimize the information representation of the network. Interestingly, these networks tend to operate near a "critical" point in their dynamics, namely close to a phase of "hallucinations", in which non-trivial spontaneous patterns of activity evolve even without structured input. Various scenarios, such as attenuation of the external inputs or increased plasticity, can lead the network to cross the border into the hallucinatory phase. The theory will be illustrated through applications to a model of a visual hypercolumn, a model of tinnitus and a model of synaesthesia. References: Shriki O. and Yellin D., Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neural Network. PLoS Computational Biology 12(2): e1004698. doi:10.1371/journal.pcbi.1004698, 2016 Shriki O., Sadeh Y. and Ward J., The Emergence of Synaesthesia in a Neuronal Network Model via Changes in Perceptual Sensitivity and Plasticity. PLoS Computational Biology 12(7): e1004959. doi:10.1371/journal.pcbi.1004959, 2016.

Cellular substrates for network information processing in hippocampal CA1

Lecture
Date:
Thursday, February 2, 2017
Hour: 12:30
Location:
Gerhard M.J. Schmidt Lecture Hall
Dr. Alessio Attardo
|
Dept of Stress Neurobiology and Neurogenetics Max Planck Institute of Psychiatry, Munich

From Single Nuclei RNA-Sequencing to Dynamics of Neuronal Regeneration

Lecture
Date:
Sunday, January 29, 2017
Hour: 11:00
Location:
Gerhard M.J. Schmidt Lecture Hall
Dr. Naomi Habib
|
Postdoctoral Fellow, Feng Zhang and Aviv Regev Labs Broad Institute of MIT and Harvard and McGovern Institute for Brain Research at MIT

Throughout adult life, adult neuronal stem cells (NSCs) continuously generate neurons in discrete brain regions. I am interested in harnessing this natural regenerative process for repairing the diseased and aging brain. To effectively use this regenerative capacity in a clinical setting requires first an advanced understanding of NSCs, adult neurogenesis and neuronal regeneration during neurodegenerative diseases and aging. Study of these areas, however, is challenging, as it requires profiling rare continuous processes in the adult brain. To this end, I developed sNuc-Seq, a method for profiling RNA in complex tissues with single nuclei resolution by RNA-sequencing, and Div-Seq, for profiling RNA in individual dividing cells. I applied sNuc-Seq to study the adult hippocampus brain region, revealing new cell-type specific and spatial expression patterns. I then applied Div-Seq to track transcriptional dynamics of newborn neurons within the adult hippocampal neurogenic region and to identify and profile rare newborn GABAergic neurons in the adult spinal cord. I am currently developing follow-up technologies to sNuc-Seq and applying them to study the cross-talk between neurons, NSCs, glia and immune cells during neurodegenerative diseases and its role in inhibiting or promoting regeneration. I will continue to work towards advancing our ability to mitigate and even reverse neurodegenerative disease and age-related pathologies. Incorporating in my work techniques from molecular neuroscience, single cell genomics, genome engineering and computational biology.

Pages

All events, 2017

There are no events to display

All events, 2017

There are no events to display