Cycle 6: Oscillations and Synchrony

Slides:



Advertisements
Similar presentations
Introduction to Neural Networks
Advertisements

Dendritic computation. Passive contributions to computation Active contributions to computation Dendrites as computational elements: Examples Dendritic.
Mean = 75.1 sd = 12.4 range =
LECTURE 12 Graded Potentials Action Potential Generation
Neural Modeling Suparat Chuechote. Introduction Nervous system - the main means by which humans and animals coordinate short-term responses to stimuli.
Neural Network of the Cerebellum: Temporal Discrimination and the Timing of Responses Michael D. Mauk Dean V. Buonomano.
Transient Cortical Excitation at the onset of visual fixation Visual recognition is brain state dependent.
Synchrony in Neural Systems: a very brief, biased, basic view Tim Lewis UC Davis NIMBIOS Workshop on Synchrony April 11, 2011.
Marseille, Jan 2010 Alfonso Renart (Rutgers) Jaime de la Rocha (NYU, Rutgers) Peter Bartho (Rutgers) Liad Hollender (Rutgers) Néstor Parga (UA Madrid)
Reading population codes: a neural implementation of ideal observers Sophie Deneve, Peter Latham, and Alexandre Pouget.
Effects of Excitatory and Inhibitory Potentials on Action Potentials Amelia Lindgren.
Neural Condition: Synaptic Transmission
Laurent Itti: CS599 – Computational Architectures in Biological Vision, USC Lecture 7: Coding and Representation 1 Computational Architectures in.
Excitable Membranes. What is an excitable membrane? Any plasma membrane that can hold a charge and propagate electrical signals.
College and Engineering Physics Quiz 9: Simple Harmonic Motion 1 Simple Harmonic Motion.
Chapter 13 Oscillatory Motion.
Chapter 5 Lecture 10 Spring Nonlinear Elements 1. A nonlinear resistance 2. A nonlinear reactance 3. A time varying element in you circuit or system.
Electronic Circuits OSCILLATORS.
Worcester Polytechnic Institute
THE ROLE OF NEURONS IN PERCEPTION Basic Question How can the messages sent by neurons represent objects in the environment?
Action Potentials and Conduction. Neuron F8-2 Axons carry information from the cell body to the axon terminals. Axon terminals communicate with their.
Oscillators. An oscillator is any measurable quantity capable of repetition. Examples: Volume of a loudspeaker Brightness of a bulb Amount of money in.
Biological Modeling of Neural Networks Week 8 – Noisy input models: Barrage of spike arrivals Wulfram Gerstner EPFL, Lausanne, Switzerland 8.1 Variation.
Top Score = 101!!!! Ms. Grundvig 2nd top score = 99 Mr. Chapman 3rd top score = Ms. Rodzon Skewness = -.57.
Sparsely Synchronized Brain Rhythms in A Small-World Neural Network W. Lim (DNUE) and S.-Y. KIM (LABASIS)
ECEN 5341/4341 Lecture 15 Feb 17, Noise Sources 2. Minimal levels of signal detection. 3. Some characteristic s of Neurons. An Important Reference.
John Wordsworth, Peter Ashwin, Gabor Orosz, Stuart Townley Mathematics Research Institute University of Exeter.
ELECTRONIC INSTRUMENTATION & PLC DKT Signal Conditioning Circuits.
Multiple attractors and transient synchrony in a model for an insect's antennal lobe Joint work with B. Smith, W. Just and S. Ahn.
Neural codes and spiking models. Neuronal codes Spiking models: Hodgkin Huxley Model (small regeneration) Reduction of the HH-Model to two dimensions.
CHAPTER 48  NEURONS, SYNAPSES, & SIGNALING 48.1  Neuron organization & Structure I. Intro to information processing A. Processing 1. Sensory input a.
Study on synchronization of coupled oscillators using the Fokker-Planck equation H.Sakaguchi Kyushu University, Japan Fokker-Planck equation: important.
The Function of Synchrony Marieke Rohde Reading Group DyStURB (Dynamical Structures to Understand Real Brains)
Oscillatory motion (chapter twelve)
Biological Neural Network & Nonlinear Dynamics Biological Neural Network Similar Neural Network to Real Neural Networks Membrane Potential Potential of.
Rhythms and Cognition: Creation and Coordination of Cell Assemblies Nancy Kopell Center for BioDynamics Boston University.
Effect of Small-World Connectivity on Sparsely Synchronized Cortical Rhythms W. Lim (DNUE) and S.-Y. KIM (LABASIS)  Fast Sparsely Synchronized Brain Rhythms.
Part 1.
Review – Objectives Transitioning 4-5 Spikes can be detected from many neurons near the electrode tip. What are some ways to determine which spikes belong.
Ch 9. Rhythms and Synchrony 9.7 Adaptive Cooperative Systems, Martin Beckerman, Summarized by M.-O. Heo Biointelligence Laboratory, Seoul National.
From LIF to HH Equivalent circuit for passive membrane The Hodgkin-Huxley model for active membrane Analysis of excitability and refractoriness using the.
Alternating and Synchronous Rhythms in Reciprocally Inhibitory Model Neurons Xiao-Jing Wang, John Rinzel Neural computation (1992). 4: Ubong Ime.
Announcements Midterm –Saturday, October 23, 4:30pm Office Hours cancelled today.
CHS AP Psychology Unit 3: Biological Psychology Essential Task 3-2: Describe the electric process of neural firing (ions, resting potential, action potential,
Measurement and Instrumentation
Biological Modeling of Neural Networks Week 11 – Variability and Noise: Autocorrelation Wulfram Gerstner EPFL, Lausanne, Switzerland 11.1 Variation of.
Applications of SHM and Energy
Neural Oscillations Continued
Mechatronics Engineering
Action Potentials and Conduction
Wien-Bridge Oscillator Circuits
Margaret Lin Veruki, Espen Hartveit  Neuron 
Action Potential Lesson 11
Effects of Excitatory and Inhibitory Potentials on Action Potentials
Neural Condition: Synaptic Transmission
Presentation of Article:
Neuronal Signals.
LECTURE 1 – FUNDAMENTAL OF VIBRATION
Synchrony & Perception
Review – Objectives Transitioning 4-5
Woochang Lim1 and Sang-Yoon Kim2
Thomas Akam, Dimitri M. Kullmann  Neuron 
Unit 3: Biological Psychology
Chapter 15 Oscillations.
Chapter 15: Oscillatory motion
Neural Communication: Action Potential
Shunting Inhibition Modulates Neuronal Gain during Synaptic Excitation
Resting Membrane Potential
Neural Condition: Synaptic Transmission
Presentation transcript:

Cycle 6: Oscillations and Synchrony What is an oscillator? Name two types of oscillators harmonic (e.g. pendulum, spring, car on track) relaxation (e.g. water drops) Components of oscillations: frequency, amplitude, phase, period next slide

Cycle 6: Oscillations and Synchrony Components of oscillators: discharge/charge phase duty cycle (relaxation phases) 1 excitable “ready” 2 active (duty cycle) 3 refractory

Cycle 6: Oscillations and Synchrony Difference between types of oscillators: frequency estimation good for harmonic not relaxation response to perturbation: relaxation phase reset Concept: Oscillators can be considered at the neuron or neural population level

Cycle 6: Oscillations and Synchrony Resonance (movie) p. 143 “neurons were believed to be silent unless excited by some outside sensory input.” BUT Sir Adrian noted ‘spontaneous activity’ in toad optic nerve. How could resonance at varying frequencies be accomplished? p. 144

Cycle 6: Oscillations and Synchrony How could resonance at varying frequencies be accomplished? p. 144 Voltage – and Ion-gated channels with different opening (gating) kinetics: Ia , Ih Filtering: LPF: passive leak and capacitance p.145 HPF: voltage-gating p.146 K+ channels especially effective in ‘shifting bridge’ think “Kapo”

Cycle 6: Oscillations and Synchrony The low-information problem and what is a neuron’s default state p. 149: default state issue: oscillations are a quirky mode seen in isolated neurons, not relevant for information processing (e.g. anesthesia). The non-oscillatory mode is 2 examples provided, p. 149 low info issue:if a cell only fires at a given phase of oscillation, it’s information is reduced.

Cycle 6: Oscillations and Synchrony Define Synchrony: - coupling in time (what window?) - Window depends on the ‘observer’, e.g. for a neuron, the time it takes for its post-synaptic potential to decay to baseline, making next input independent rather than summate. The 1/e decay (down to 37%) is called the ‘time constant’, and it’s the metric used to define temporal decays. for an oscillating population, the duration of the readiness state determines the window: ½ cycle for harmonic oscillators, and the relevant fraction +/- for relaxation oscillations

Cycle 6: Oscillations and Synchrony Stochastic resonance a weak signal is transmitted better in the presence of noise…like getting ‘jumped’ on a trampoline, to see over a fence that was too high for you when jumping alone. Even if that energy input (your ‘jumper’) may fall randomly in your jump cycle (sometimes reducing your height), when it eventually it falls in the right window, you achieve what you couldn’t without the energy input (seeing over the fence).

Cycle 6: Oscillations and Synchrony Stochastic resonance weak signal can be oscillation, if it’s subthreshold ‘noise’ input can also be oscillation, again, if it’s subthreshold p. 158:

Cycle 6: Oscillations and Synchrony Features of cell assemblies: groups of neurons whose coincident activity exceeds what would be expected from sensory inputs. Reverberation, or continued activity within the population that continues in the absence of inputs Flexible membership: a neuron can be a part of many assemblies.

Cycle 6: Oscillations and Synchrony Features of cell assemblies: groups of neurons whose coincident activity exceeds what would be expected from sensory inputs. Reverberation, or continued activity within the population that continues in the absence of inputs Flexible membership: a neuron can be a part of many assemblies. Time windows can define, and segregate assemblies, including oscillatory ‘windows’ p.164 “The uniquely changing assemblies in each oscillatory cycle can target anatomically unique sets of neurons. Through assembly organization, time is translated to neuronal network space.”

Cycle 6: Oscillations and Synchrony Synchrony is cheap the integration time window of neurons means that multiple synchronous inputs effect greater change than the same inputs presented asynchronously. Or, you can get the same level of output with fewer inputs, when the inputs are provided in synchrony. Even Huygen’s clocks on the wall synchronized, if they were in the same wall.