Past events | The department of Brain Sciences

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Synaptic mechanisms of sensory perception

Lecture

Date:

Wednesday, June 18, 2014

Hour: 12:30

Location:

Gerhard M.J. Schmidt Lecture Hall

Prof. Carl Petersen

|

Brain Mind Institute, Ecole Polytechnique Federale de Lausanne (EPFL),Lausanne, Switzerland

A key goal of modern neuroscience is to understand the neural circuits and synaptic mechanisms underlying sensory perception. Here, I will discuss our efforts to characterise sensory processing in the mouse barrel cortex, a brain region known to process tactile information relating to the whiskers on the snout. Each whisker is individually represented in the primary somatosensory neocortex by an anatomical unit termed a ‘barrel’. The barrels are arranged in a stereotypical map, which allows recordings and manipulations to be targeted with remarkable precision. In this cortical region it may therefore be feasible to gain a quantitative understanding of neocortical function. We have begun experiments towards this goal using whole-cell recordings, voltage-sensitive dye imaging, viral manipulations, optogenetics and two-photon microscopy. Through combining these techniques with behavioral training, our experiments provide new insight into sensory perception at the level of individual neurons and their synaptic connections.

How do rewards affect visual cortex?

Lecture

Date:

Tuesday, June 17, 2014

Hour: 12:30

Location:

Gerhard M.J. Schmidt Lecture Hall

Prof. Wim Vanduffel

|

Dept of Neurosciences, KU Leuven, Belgium Harvard Medical School, MA

I will discuss data from a series of monkey fMRI experiments showing evidence for a reward induced spatially-selective modulation of visual cortical activity. These effects reflect a dopamine-dependent reward-prediction error signal that may be caused by ventral mid-brain nuclei. I will also discuss data from our first microstimulation experiments in the monkey targeting the ventral midbrain which revealed profound functional network changes in the reward circuitry and changes in behavior during a free choice task in monkeys.

Magnetic Resonance Spectroscopy (MRS) as a Tool for Probing Brain Metabolism in Vivo

Lecture

Date:

Tuesday, June 10, 2014

Hour: 12:30

Location:

Gerhard M.J. Schmidt Lecture Hall

Dr. Assaf Tal

|

Department of Chemical Physics, WIS

Magnetic resonance is used mostly to image the intense signal arising from the water molecules in vivo, yielding high resolution anatomical maps. However, by suppressing the water signal, it is possible to detect the much weaker signals of less abundant metabolites, including creatine, choline, GABA, glutamine/glutamate and several others: this is termed Magnetic Resonance Spectroscopy (MRS). I will attempt to provide a broad overview of how this metabolic information can be leveraged to study the human brain by presenting in-vivo data from our multiple sclerosis cohort, as well as discuss the main difficulties associated with MRS and how the research we conduct aims to rectify them.

High spatial and temporal dynamics of sequential binding amongst cortical areas:an MEG study

Lecture

Date:

Tuesday, May 20, 2014

Hour: 12:30

Location:

Gerhard M.J. Schmidt Lecture Hall

Prof. Moshe Abeles

|

Bar-Ilan University The Hebrew University of Jerusalem

We assume that while preforming any higher brain function, multiple cortical regions interact with some fairly fixed temporal order. This type of process needs to be studied with a resolution of a few ms. Such sequences of coordinated activities amongst multiple cortical locations was revealed in ongoing activity with milliseconds accuracy. That was achieved without the need for averaging over time or frequencies. The analysis was based on recording MEG and reconstructing the cortical current-dipole-amplitudes at multiple points. In these current-dipole traces instances of brief activity undulations were automatically detected and used to reveal where and when cortical points interact.

Homeostatic regulation of intrinsic excitability and circuit function

Lecture

Date:

Wednesday, May 14, 2014

Hour: 12:30

Location:

Arthur and Rochelle Belfer Building for Biomedical Research

Prof. Eve Marder

|

Faculty of Biology and Volen National Center for Complex Systems, Brandeis University

Neurons and networks must constantly rebuild themselves in response to the continual and ongoing turnover of all of the ion channels and receptors that are necessary for neuronal signaling. A good deal of work argues that stable neuronal and network function arises from homeostatic negative feedback mechanisms. Nonetheless, while these mechanisms can produce a target activity or performance, they are also consistent with a good deal of recent theoretical and experimental work that shows that similar circuit outputs can be produced with highly variable circuit parameters. This work argues that the nervous system of each healthy individual has found a set of different solutions that give “good enough circuit performance. I will describe new computational models (O’Leary et al., PNAS 2013; Neuron in press, 2014) for cellular homeostasis that give insight into a variety of experimental observations, including correlations in the expression of ion channel genes. In response to perturbation these homeostatic models usually compensate for perturbations, but some perturbations elude compensation. Moreover, situations can arise in which the homeostatic mechanisms result in aberrant behavior, such as may occur in disease.

Janus-faced gating-modifiers targeting the voltage sensor of voltage-gated cation channels:A new approach to cure hyperexcitability disorders

Lecture

Date:

Tuesday, May 13, 2014

Hour: 12:30

Location:

Gerhard M.J. Schmidt Lecture Hall

Prof. Bernard Attali

|

Sagol School of Neuroscience and Dept of Physiology and Pharmacology Sackler Medical School, Tel Aviv University

Some of the fascinating features of voltage-sensing domains (VSD) in voltage-gated cation channels (VGCC) are their modular nature and adaptability. Here we examined the VSD promiscuity of VGCC, using non-toxin gating-modifiers, NH17 and NH29, which share closely related structures and stabilize Kv7.2 potassium channels, in the closed and open state, respectively. NH17 and NH29 exert opposite gating-modifier effects on TRPV1 channels, operating respectively, as an activator and a blocker of TRPV1 currents. Combined mutagenesis and electrophysiology, structural homology modeling, molecular docking and molecular dynamics simulation indicate that both compounds target the VSD of TRPV1 channels, which like vanilloids are involved in π-π stacking, H-bonding and hydrophobic interactions. Reflecting the VSD promiscuity, they also affect the lone VSD proton channel mVSOP. Remarkably, NH29 alleviates neuropathic pain in rats, suggesting that sometimes, promiscuous VSD ligands may be therapeutically beneficial. Thus, structurally related, yet different molecules can interact with the VSD of the same VGCC, while the same gating-modifier can promiscuously interact with different VGCC. Subtle differences at the VSD-ligand interface will dictate whether the gating-modifier stabilizes channels in either the closed or the open state.

Epigenetic dysfunction in neuropsychiatric diseases: insights from cell-reprogramming based models

Lecture

Date:

Monday, May 12, 2014

Hour: 12:30

Location:

Gerhard M.J. Schmidt Lecture Hall

Prof. Giuseppe Testa

|

Director, Laboratory of Stem Cell Epigenetics European Institute of Oncology and European School of Molecular Medicine Milan, Italy

Popular Debate: Macroeconomics Symposium

Lecture

Date:

Wednesday, May 7, 2014

Hour: 16:00 - 18:00

Location:

Dolfi and Lola Ebner Auditorium

Bursting reverberation in small and large neuronal networks

Lecture

Date:

Thursday, May 1, 2014

Hour: 14:30

Location:

Gerhard M.J. Schmidt Lecture Hall

Dr. David Holcman

|

Group of Applied Mathematics and Computational Biology, IBENS Ecole Normale Superieure, Paris

Neuronal networks can generate complex patterns of activity that depend on membrane properties of individual neurons as well as on functional synapses. To decipher the impact of synaptic properties and connectivity on neuronal network behavior, we studied using a combination of electrophysiological recordings and the synaptic depression-facilitation model, the responses of neuronal ensembles from small (between 5-30 cells in a restricted sphere) and large (acute hippocampal slice) networks to single electrical stimulation. Interestingly, in both cases, a single stimulus generated a synchronous long-lasting bursting activity. We characterized this activity in neuronal populations using electrophysiological recordings and we also extract the network time constant parameters using the mean-field model based on synaptic facilitation/depression. While the initial spikes triggered a reverberating network activity that lasted 2-5 seconds for small networks, it lasted only up to 300 milliseconds in slices, a phenomena that was also present in our simulations. We found here that the reverberation time has a bell shaped relation with the synaptic density. In addition, before reaching its maximum, this reverberation time increased sub-linearly with the network connectivity parameter. We conclude that synaptic properties and the network connectivity shape the mean burst duration, which persists across various network scales. This synchronization is an inherent property of sufficiently connected neural networks based on synaptic depression and facilitation.

Modelling hippocampal circuit dynamics:space, time, and context

Lecture

Date:

Tuesday, April 29, 2014

Hour: 12:30

Location:

Gerhard M.J. Schmidt Lecture Hall

Dr. Sandro Romani

|

Columbia University, NY

Since the discovery of place cells in the hippocampus, a variety of experimental observations have pointed to the complexity of hippocampal circuit dynamics and their importance in memory related tasks. During spatial navigation, place cell activity predicts the upcoming animal location within the short time scale of individual cycles of theta oscillations. Sudden changes of the spatial context are followed by a bistability between population coding of past and current context, paced by the theta rhythm. During immobility, brief sequences of place cell activation encode spatial trajectories, which have been linked to learning in spatial memory tasks and goal-directed navigation. Finally, when the animal is engaged in a delayed memory task, hippocampal cells fire at specific time intervals within the delay period and the activity of a population of cells is predictive of the behavioral outcome. I will present a unified attractor network model that accounts for this wide range of experimental observations. A critical component of the model is the use of realistic synapses that exhibit short-term plasticity driven by presynaptic activity. Complexity in the network dynamics emerges due to the effect of history dependent synaptic states on the network activity. Model predictions, possible extensions of the model and its relationship to dynamics observed in other cortical areas will be discussed.

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All events, All years

Popular Debate: Macroeconomics Symposium

Lecture

Date:

Wednesday, May 7, 2014

Hour: 16:00 - 18:00

Location:

Dolfi and Lola Ebner Auditorium

Bursting reverberation in small and large neuronal networks

Lecture

Date:

Thursday, May 1, 2014

Hour: 14:30

Location:

Gerhard M.J. Schmidt Lecture Hall

Dr. David Holcman

|

Group of Applied Mathematics and Computational Biology, IBENS Ecole Normale Superieure, Paris

Neuronal networks can generate complex patterns of activity that depend on membrane properties of individual neurons as well as on functional synapses. To decipher the impact of synaptic properties and connectivity on neuronal network behavior, we studied using a combination of electrophysiological recordings and the synaptic depression-facilitation model, the responses of neuronal ensembles from small (between 5-30 cells in a restricted sphere) and large (acute hippocampal slice) networks to single electrical stimulation. Interestingly, in both cases, a single stimulus generated a synchronous long-lasting bursting activity. We characterized this activity in neuronal populations using electrophysiological recordings and we also extract the network time constant parameters using the mean-field model based on synaptic facilitation/depression. While the initial spikes triggered a reverberating network activity that lasted 2-5 seconds for small networks, it lasted only up to 300 milliseconds in slices, a phenomena that was also present in our simulations. We found here that the reverberation time has a bell shaped relation with the synaptic density. In addition, before reaching its maximum, this reverberation time increased sub-linearly with the network connectivity parameter. We conclude that synaptic properties and the network connectivity shape the mean burst duration, which persists across various network scales. This synchronization is an inherent property of sufficiently connected neural networks based on synaptic depression and facilitation.

Modelling hippocampal circuit dynamics:space, time, and context

Lecture

Date:

Tuesday, April 29, 2014

Hour: 12:30

Location:

Gerhard M.J. Schmidt Lecture Hall

Dr. Sandro Romani

|

Columbia University, NY

Since the discovery of place cells in the hippocampus, a variety of experimental observations have pointed to the complexity of hippocampal circuit dynamics and their importance in memory related tasks. During spatial navigation, place cell activity predicts the upcoming animal location within the short time scale of individual cycles of theta oscillations. Sudden changes of the spatial context are followed by a bistability between population coding of past and current context, paced by the theta rhythm. During immobility, brief sequences of place cell activation encode spatial trajectories, which have been linked to learning in spatial memory tasks and goal-directed navigation. Finally, when the animal is engaged in a delayed memory task, hippocampal cells fire at specific time intervals within the delay period and the activity of a population of cells is predictive of the behavioral outcome. I will present a unified attractor network model that accounts for this wide range of experimental observations. A critical component of the model is the use of realistic synapses that exhibit short-term plasticity driven by presynaptic activity. Complexity in the network dynamics emerges due to the effect of history dependent synaptic states on the network activity. Model predictions, possible extensions of the model and its relationship to dynamics observed in other cortical areas will be discussed.

Novel homeostatic mechanisms in a neurodevelopmental mice model of Angelman syndrome

Lecture

Date:

Tuesday, April 8, 2014

Hour: 12:30

Location:

Gerhard M.J. Schmidt Lecture Hall

Dr. Hanoch Kaphzan

|

Laboratory for Neurobiology of Psychiatric Disorders Dept of Neurobiology, University of Haifa

Angelman syndrome (AS) is a human neuropsychiatric disorder associated with autism, mental retardation, motor dysfunction, and epilepsy. In most cases, AS is caused by the deletion of small portions of chromosome 15, which includes the UBE3A gene. The UBE3A gene encodes an enzyme termed ubiquitin ligase E3A. A mouse model of AS has been generated and these mice exhibit abnormalities that correlate with neurological alterations observed in humans with AS. One of the prominent affected brain regions in AS is the hippocampus. We characterized the CA1 pyramidal neurons in the AS mice, and observed alterations in the intrinsic membrane properties of these cells between AS mice and their wild-type littermates. These alterations were correlated with increased expression of specific proteins, mainly related to the axon initial segment (AIS). Furthermore, the AIS morphology of these neurons in the AS mice was also found to be altered. By determining the temporal sequence for the increased expression of these proteins we have discovered the precipitating event for the observed AIS alterations which coincides with the homeostatic model of the neuron. Finally, we rescued the hippocampal pathology via a genetic manipulation based on this understanding. Taken together, our findings are the first to suggest that AIS plasticity alterations exist in mammalian brain in-vivo and could be involved in neuropsychiatric disorders such as AS. They also offer a novel conceptualization of neuropsychiatric disorders and propose the option for an innovative therapeutic strategy.

Decision confidence: from statistical principles to the neurobiological mechanisms behind

Lecture

Date:

Monday, April 7, 2014

Hour: 14:00

Location:

Gerhard M.J. Schmidt Lecture Hall

Prof. Adam Kepecs

|

Cold Spring Harbor Laboratory

: Decision confidence is a forecast about the correctness of one’s decision. It is often regarded as a higher-order function of the brain requiring a capacity for metacognition that may be unique to humans. If confidence manifests itself to us as a subjective feeling, how can then one identify it amongst the brain’s electrical signals in an animal in order to uncover its neural basis? We tackled this issue by using mathematical models to gain traction on the problem of confidence, allowing us to identify neural correlates and mechanisms. I will begin with a normative statistical theory that enables us to establish that human self-reports of confidence are based on a computation of statistical decision confidence. Next, I will present computational algorithms that can be used to estimate confidence and decision tasks that we developed to behaviorally read out this estimate in humans and rats. Finally, I will discuss the neural basis of decision confidence, focusing on the role of the orbitofrontal cortex.

Uncertainty in human brain and behavior

Lecture

Date:

Sunday, April 6, 2014

Hour: 12:30

Location:

Gerhard M.J. Schmidt Lecture Hall

Dr. Ifat Levy

|

Decision Neuroscience Lab Yale School of Medicine

Uncertainty is inherent to any situation we encounter. Our individual attitudes towards uncertainty strongly affect our evaluation of different available options and our behavior based on these evaluations. In the talk I will describe a series of studies in which we combine experimental economics and other behavioral methods with functional MRI to study the behavioral and neural characteristics of attitudes towards uncertainty and learning under uncertainty.

The memory function of sleep

Lecture

Date:

Thursday, April 3, 2014

Hour: 11:30

Location:

Arthur and Rochelle Belfer Building for Biomedical Research

Prof. Dr. Jan Born

|

The Institute for Medical Psychology and Behavioural Neurobiology University of Tübingen

Compensatory boosting of cortical inputs to striatal cholinergic interneurons in mouse models of Huntington's disease

Lecture

Date:

Tuesday, March 25, 2014

Hour: 12:30

Location:

Gerhard M.J. Schmidt Lecture Hall

Dr. Joshua A. Goldberg

|

Dept of Medical Neurobiology, Institute of Medical Research Israel–Canada The Faculty of Medicine, The Hebrew University of Jerusalem

In Huntington’s disease (HD) – a devastating autosomal-dominant neurodegenerative disease – the striatum displays reduced cholinergic markers, despite the resiliency of cholinergic interneurons (ChIs) – the source of striatal acetylcholine – to the neurodegeneration that decimates striatal projection neurons. Autonomous spiking of ChIs is unchanged in transgenic HD mice, suggesting a functional deficit in extrinsically driven activity. Using two transgenic mouse models of HD, we show that ChI responses to cortical input are boosted by a post-synaptic up-regulation of the persistent sodium current. This boosting is replicated by in wild-type mice by diminished activation of group I metabotropic glutamate receptors (mGluRs). Activation of group I mGluRs in HD mice counters the boosting. We propose that the recently described loss of thalamic synapses in striatum, reduces group I mGluR activation in ChIs which promotes boosting of cortical inputs. The augmentation of cortical inputs may function to compensate for the lost thalamic glutamatergic drive.

Efficient Coding in Active Perception

Lecture

Date:

Tuesday, March 11, 2014

Hour: 12:30

Location:

Gerhard M.J. Schmidt Lecture Hall

Prof. Jochen Triesch

|

FIAS-Frankfurt Institute of Advanced Studies

The goal of perceptual systems is to provide useful knowledge about the environment and to encode this information efficiently. As such, perception is an active process that often involves the movement of sense organs such as the eyes. This active nature of perception has typically been neglected in current theories describing how nervous systems learn sensory representations. Here we present an approach for intrinsically motivated learning during active perception that treats the learning of sensory representations and the learning of movements of the sense organs in an integrated manner. In this approach, a generative model learns to encode the sensory data while a reinforcement learner directs the sense organs so as to make the generative model work as efficiently as possible. To this end, the reinforcement learner receives an intrinsic reward signal that measures the encoding quality currently obtained by the generative model. In the context of binocular vision, the approach is shown to lead to a self-calibrating stereo vision system that learns a representation for binocular disparity while at the same time learning proper vergence eye movements to fixate objects. The approach is quite general and can be applied to other types of eye movements such as smooth pursuit movements during motion perception. It may also be extended to different sensory modalities. Somewhat surprisingly, the approach also offers a new perspective on the development of imitation abilities.

From Sensory Neural Codes to Behavior

Lecture

Date:

Wednesday, March 5, 2014

Hour: 14:30

Location:

Gerhard M.J. Schmidt Lecture Hall

Dr. Moshe Parnas

|

Centre for Neural Circuits and Behaviour University of Oxford

Most neurons involved in perceptual judgments are at least two synapses removed from sensory receptors. Psychophysical models that link perception to the physical qualities of external stimuli are thus black boxes. Opening these black boxes is challenging and requires comprehensive estimates of activity in many neurons carrying perceptually relevant signals. Because sensory representations are distributed over large numbers of neurons, such estimates have generally remained elusive. Here, we take advantage of the well-characterized olfactory system of fruit flies to relate knowledge of the neuronal population representations of odors to behavioral measures of odor discrimination. Flies detect odors using ~50 types of olfactory receptor neurons (ORNs). ORN axons segregate anatomically by receptor type and transmit signals via separate synaptic relays, to discrete classes of excitatory projection neurons (ePNs). Previously, ORN responses to odors and a transformation estimating PN spike rates from measured ORN spike rates were presented. ePNs project to the mushroom body, and the lateral horn (LH). The LH, thought to be responsible of naïve behavior, also receives input from a functionally uncharacterized group of GABAergic inhibitory PNs (iPNs). The fact that iPNs target exclusively the LH hints at a possible function of these inhibitory neurons in naïve behavior. We formulate and test a simple model of innate odor discrimination that takes as its input the estimated PN signals projected onto the LH and generates as its output a prediction of whether two odors can be distinguished. We show that the main determinant of discriminability is the distance between the PN activity patterns evoked by two odors. Experimental manipulations of this distance have graded predictable perceptual consequences. We further show that, inhibition by iPNs makes closely related odors easier to distinguish, in all likelihood by imposing a high-pass filter on ePN output that stretches the distances between partially overlapping odor representations.