Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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 , I h • 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 postsynaptic 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.