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Lecture 10

Lecture 10.docx

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Rutsuko Ito

Lecture 10 Synapses in networks Network oscillations Lecture outline 1. Neural oscillations at all levels of brain organization a. Single cell b. Local communication c. Regional communication d. Network communication 2. Functional significance of network oscillations a. Information representation i. Grandmother cell vs distributed coding theories b. Modulation of information 3. Pathological oscillations 4. Application to disease Neural oscillations 1. Movement back and forth in a regular rhythm, observed across all levels of brain organization a. Single cell i. Single neurons exhibit intrinsic oscillatory firing activity due to fluctuations in membrane potential, as measured with intracellular recordings 1. E.g. rhythmic burst firing and spike activity in thalamic relay neurons a. Rhythmic firing neurons have the potential to serve as pacemakers for network oscillations b. Local cell ensembles i. Local group of cells can generate oscillatory activity through interactions with other local neurons, measured with extracellular local field potential recordings 1. Excitatory-excitatory cell interactions a. E.g. CA3 pyramidal-CA3 pyramidal cells  theta oscillations 2. Excitatory-inhibitory cell interactions a. E.g. cortical pyramidal-GABA interneuron  slow oscillations 3. Inhibitory-inhibitory cell interactions a. E.g. GABA interneuron network  gamma oscillations c. Brain networks i. Region-region communication 1. Brain regions often have reciprocal connections which form feedback loops through which oscillatory activity can be transmitted from one region to another a. Thalamocortical loop i. Thalamic neurons are powerful single-neuron oscillators 1. Can act as pacemakers for the entire cortex through highly divergent axonal projections ii. Rhythmic cortical feedback to the thalamus in turn serves to amplify thalamocortical oscillations 2. The deep sleep state (non-REM sleep) is associated with highly synchronous, oscillatory activity (spindle waves) between the thalamus and the cortex, causing a disconnection between the cortex and the outside world 2. Are oscillations simply an epiphenomenon to the brain's need to maintain a balance between excitatory and inhibitory mechanisms so as to prevent seizures? a. Or do they have a primary, causal role in brain function? How are network oscillations measured? 1. EEG (electroencephalogram) a. EEG recordings identified alpha rhythm (berger rhythm: 8-12 Hz) i. During eye closure in calm, awake subject b. An EEG electrode records the summated activity of many neurons (network activity) in the cerebral cortex 2. Synchrony a. Small EEG signals often imply desynchronized activity b. Large EEG signals often imply strong synchronized activity Normal to abnormal network oscillation rhythms 1. Higher frequency oscillations are confined to a smaller neuronal space (local networks) a. Larger networks can be recruited during slow oscillations b. Frequency ranges are somewhat arbitrary and overlapping c. See slide 12 Functional importance of network oscillations 2. There have been huge advances in elucidating the physiological and behavioral correlates of neural oscillations in the brain a. But there is no consensus as to what functions they subserve i. Theories: 1. Network oscillations represent information and bind percepts 2. Network oscillations regulate the strength of information processing Network oscillations as a binding mechanism Information representation: the neural code 1. Rate coding a. Adrian (1926) i. The firing rate of mechanoreceptor neurons in frog leg is proportional to the stretch applied b. Hubel and wiesel (1959) i. Selective response of visual cells 1. E.g. the firing rate is a function of edge orientation c. Firing rates also code stimulus intensity in sensory systems d. Rate coding is confirmed in sensory system and primary cortical area e. However, it is increasingly considered insufficient for the integration of information  binding problem Binding problem 1. How can we have a coherent, unified perception of an object, given that its various attributes are processed in different parts of the brain? a. How do we distinguish between two different objects that may have overlapping characteristics? (complex feature cells) Questions of representation 2. How does our brain represent an object? a. E.g. Green couch with red book on it i. Feature binding in cell assembles? 1. Problem of superimposition a. Slide 18 ii. Grandmother cells 1. Introduce overarching complex detector (grandmother) cells Grandmother cell theory 1. Simple cells connect to complex cells a. Complex cells connect to hyper complex cells and so on i. Until finally there is one unique cell that fires when you see your grandmother 1. If you lose that cell, you can no longer recognize your grandmother, but have no problems recognizing grandfather 2. Evidence a. Prosopagnosia (face blindness) i. Although able to detect a face among objects (face detection) and recognize objects, prosopagnosic patients lose the ability to identify familiar faces, including famous persons, friends, relatives, and their own face ii. Lesions to FFA (fusiform face area) causes prosopagnosia b. Single cell recordings in human brain i. A single cell in the left posterior hippocampus was activated exclusively by different views of Jennifer Aniston 1. *note absense of response when she's in a picture with brad pitt ii. Also found a cell in the left anterior hippocampus that responded to halle berry, even when it was a drawing of her, or a letter string iii. Problems with this? 1. Male vs Female brain? 2. ... 3. Fallbacks a. Could there really be a cell or group of cells that represents every object / concept that we can think of? Distributed processing theory 1. Information on a particular function is spread out over the entire cortex a. Each unit in a network is involved in coding more than one familiar thing i. Thus the identity of a stimulus cannot be determined by considering the activation of a single unit (neuron) 1. E.g. a given unit will contribute to the coding of the words blue and blur Relational information processing 1. Representation of relational information can help associate complex stimuli Solving the binding problem with temporal coding 2. Slide 26 a. Temporal vs rate coding mechanism b. Code features with temporal aspects i. Bind two precepts together ii. Discriminate between two precepts 1. Red + Circle firing temporally 2. Green + triangle firing temporally Binding by synchrony 3. Slide 27 a. Which cells are firing in synchrony (temporal) in order to associate relevant representations of precepts b. Distinguishes and discriminates between two objects 4. Evidence for distributed processing: binding by synchrony a. Network oscillations have been proposed to provide a general mechanism by which activity patterns in spatially separate regions of the brain are temporally coordinated i. Gamma oscillations  attention 1. As attention is directed inside the receptive field, the recorded neurons in V4 fire more in phase with gamma oscillations (50 Hz) and less to higher frequency oscillations (10 Hz) a. Graphs i. Stimulus presented outside receptive field of recorded neuron ii. Stimulus presented within receptive field of recorded
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