=<font color="blue">'''This Week -
04 June 2021 (9:30 a.m., via Zoom)'''</font>= |+|
=<font color="blue">'''This Week - 2021 (9:30 a.m., via Zoom)'''</font>=
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Megha Sehgal''' |+|
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<u>Title: </u> ''' “
Cortical response selectivity derives from strength in numbers of synapses ” ''' |+|
<u>Title: </u> ''' “ selectivity in ” '''
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Single neocortical neurons are driven by populations of excitatory inputs, which form the basis of neuronal selectivity to features of sensory input. Excitatory connections are thought to mature during development through activity-dependent Hebbian plasticity, whereby similarity between presynaptic and postsynaptic activity selectively strengthens some synapses and weakens others. Evidence in support of this process includes measurements of synaptic ultrastructure and in vitro and in vivo physiology and imaging studies. These corroborating lines of evidence lead to the prediction that a small number of strong synaptic inputs drive neuronal selectivity , whereas weak synaptic inputs are less correlated with the somatic output and modulate activity overall. Supporting evidence from cortical circuits, however, has been limited to measurements of neighbouring, connected cell pairs, raising the question of whether this prediction holds for a broad range of synapses converging onto cortical neurons. Here we measure the strengths of functionally characterized excitatory inputs contacting single pyramidal neurons in ferret primary visual cortex ( V1) by combining in vivo two-photon synaptic imaging and post hoc electron microscopy. Using electron microscopy reconstruction of individual synapses as a metric of strength, we find no evidence that strong synapses have a predominant role in the selectivity of cortical neuron responses to visual stimuli. Instead, selectivity appears to arise from the total number of synapses activated by different stimuli. Moreover, spatial clustering of co-active inputs appears to be reserved for weaker synapses, enhancing the contribution of weak synapses to somatic responses. Our results challenge the role of Hebbian mechanisms in shaping neuronal selectivity in cortical circuits, and suggest that selectivity reflects the co- activation of large populations of presynaptic neurons with similar properties and a mixture of strengths. |+|
<u>Abstract:</u> neurons of , the of to are thought to to selectivity and . , however, to of , of . the of neurons in (). of a a the of responses . to the of . , of , of synapses the of in , that the -and .
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<u> Blurb: </u>
Neurons receive diverse synaptic inputs but it is unclear which synaptic properties (strength, number or spatial organization) ultimately decides neuronal selectivity? Using 2 photon calcium imaging and electron microscopy, Scholl et al. demonstrate that neuronal selectivity is driven by strength in numbers and weaker synapses cluster together to drive somatic output. |+|
<u> Blurb: </u> is , and , . that is in and .
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https: //www.nature.com/articles/s41586-020-03044-3#article-info |+|
<u>Relevant Papers:</u> : ://www...//.
|−|https://www. sciencedirect.com/science/article/pii/S0896627317309856?via%3Dihub |+|
|−|https://www. nature. com/ articles/ nn. 4323 |+|
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Revision as of 19:11, 5 October 2021
This Week - 08 October 2021 (9:30 a.m., via Zoom)
Speaker: Chinmay Purandare
Title: “ Moving bar of light generates angle, distance and direction selectivity in place cells. ”
Abstract: Primary visual cortical neurons selectively respond to the position and motion direction of specific stimuli retrospectively, without any locomotion or task demand. At the other end of the visual circuit is the hippocampus, where in addition to visual cues, self-motion cues and task demand are thought to be crucial to generate selectivity to allocentric space in rodents that is abstract and prospective. In primates, however, hippocampal neurons encode object-place association without any locomotion requirement. To bridge these disparities, we measured rodent hippocampal responses to a vertical bar of light in a body-fixed rat, independent of behavior and rewards. When the bar revolved around the rat at a fixed distance, more than 70% of dorsal CA1 neurons showed stable modulation of activity as a function of the bar’s angular position, while nearly 40% showed canonical angular tuning, in a body-centric coordinate frame, termed Stimulus Angle Cells or Coding (SAC). The angular position of the oriented bar could be decoded from only a few hundred neurons’ activity. Nearly a third of SAC were also tuned to the direction of revolution of the bar and most of these responses were retrospective. SAC were invariant with respect to the pattern, color, speed and predictability of movement of the bar. When the bar moved towards and away from the rat at a fixed angle, neurons encoded its distance and direction of movement, with more neurons preferring approaching motion. Thus, a majority of neurons in the hippocampus, a multisensory region several synapses away from the primary visual cortex, encode non-abstract information about stimulus-angle, distance and direction of movement, in a manner similar to the visual cortex, without any locomotion, reward or memory demand. We posit that these responses would influence the cortico-hippocampal circuit and form the basis for generating abstract and prospective representations.
Blurb: A novel, simple way to activate the hippocampus and probe its function. Hippocampus is crucial for learning and memory and implicated in major disorders including Alzheimer's, epilepsy and schizophrenia. But, hippocampal responses in rodents are measured when they are navigating a spatial arena, and called place cells. While humans and nonhuman primate hippocampal function is typically measured while the subjects are seated and solving a memory task, leading to very different types of activity that is often unrelated to space. To overcome these challenges, and generate a reliable translational model of hippocampal function we need an experimental design that can be concocted in rodents and humans under identical conditions. Here we report such a novel and simple design that generates reliable responses in the rodent hippocampus.
Relevant Papers: For background material see: http://www.physics.ucla.edu/~mayank/publications.html
The Integrative Center for Learning and Memory (ICLM) is a multidisciplinary center of UCLA labs devoted to understanding the neural basis of learning and memory and its disorders. This will require a unified approach across different levels of analysis, including;
1. Elucidating the molecular cellular and systems mechanisms that allow neurons and synapses to undergo the long-term changes that ultimately correspond to 'neural memories'.
2. Understanding how functional dynamics and computations emerge from complex circuits of neurons, and how plasticity governs these processes.
3. Describing the neural systems in which different forms of learning and memory take place, and how these systems interact to ultimately generate behavior and cognition.
History of ICLM
The Integrative Center for Learning and Memory formally LMP started in its current form in 1998, and has served as a platform for many interactions and collaborations within UCLA. A key event organized by the group is the weekly ICLM Journal Club. For more than 10 years, graduate students, postdocs, principal investigators, and invited speakers have presented on topics ranging from the molecular mechanisms of synaptic plasticity, through computational models of learning, to behavior and cognition. Dean Buonomano oversees the ICLM journal club with help of student/post doctoral organizers. For other events organized by ICLM go to http://www.iclm.ucla.edu/Events.html.
Megha Sehgal (Silva Lab) & Giselle Fernandes (Silva Lab).
Please email us at firstname.lastname@example.org if you would like to get regular updates regarding our journal club and weekly reminders.
Current Faculty Advisor:
i) Anna Matynia(Aug 2004 - Jun 2008) (Silva Lab)
ii) Robert Brown (Aug 2008 - Jun 2009) (Balleine Lab)
iii) Balaji Jayaprakash (Aug 2008 - Nov 2011) (Silva Lab)
iv) Justin Shobe & Thomas Rogerson (Dec 2011 - June 2013) (Silva Lab)
v) Walt Babiec (O'Dell Lab) (2013-2014)
vi) Walt Babiec (O'Dell Lab) & Helen Motanis (Buonomano Lab) (2014-2017)
vii) Helen Motanis (Buonomano Lab) & Shonali Dhingra (Mehta Lab) (2017-2018)
viii) Shonali Dhingra (Mehta Lab) (2018-2020)
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