Why we sleep Hsin-Hua Wei Hsin-Hua Wei Stefanie Lutz Stefanie Lutz
Dec 20, 2015
Overview
Estimates: brain consists of 100 billions of neurons that are connected with about 1014 of synapses
Function of the brain is based on interaction between highly networked neurons by means of electrical impulses
Typically neurons connect to at least a thousand other neurons
Neurons are typically composed of a soma, a dendritic tree and an axon
The axon extends away from the cell body and is the main conducting unit for carrying signals to other neurons.
Signals flow in only one direction
About 1000 times a night, billions of neurons undergo a synchronous one-second burst of non-REM electrical activity.
Throughout the night, the bursts become smaller.
The bursts disappear completely just before waking
The longer a person has been sleep-deprivated, the bigger the initial burst
The waves of brain activity during deep sleep
reactivate neurons strengthen neuronal connection The bursts let the brain slowly reinforce
synaptic connections that already exist.
We sleep to remember
“Going up and down, up and down, basically all the neurons fire and then all are silent – it’s a wonderful way for the brain to tell the synapses to get weaker”
The progressive weakening allows only the strong connections to survive.
Tononi’s theory
Without paring unneeded information, our brains would face “space crunch”
By proportionally weakening synapses, the brain ensures that they retain the same strength relative to each other.
Development of the model
discrete model for strength of the synapses during our sleep
simulate two interpretations:1. brain bursts cause strengthening2. brain bursts cause weakening of synapses
include influence of neighbouring synapses
General equations
syn(i,j),t+1 = syn(i,j),t + α * (g(t) + f(t));
g(t) = β * (syn(i-1,j),t + syn(i+1,j),t + syn(i,j-1),t + syn(i,j+1),t)
f(t) = -c(n) * (t - n) * (t - n - 1); Parabola with negative coefficient in front of t2
]0, 1[ , ]3, 4[ , ]6, 7[ , ... Maximum at t = n + 0.5 c(n) = μ * (n + 0.5)(-0.1)
strictly decreasing
Choice of signs and parameters leads to different interpretations
classical interpretation:
syn(i,j),t+1 = syn(i,j),t + α * (g(t) + f(t)) , α = 0.01 new interpretation:
syn(i,j),t+1 = syn(i,j),t - α * (g(t) + f(t)) , α = 0.01 our interpretation:
syn(i,j),t+1 = syn(i,j),t + α * (g(t) + f(t))
for syn(i,j),t ≥ threshold
syn(i,j),t+1 = syn(i,j),t + α * (g(t) - f(t)) , α = 0.01
for syn(i,j),t < threshold
Realisation of function g(t) of neighbours
Assumptions: cell represents neuron with synapses strength of synapses is proportional to
strength of neuron focus on synapses simplification: neuron sends signals only to
one neighbour, but can be reached by 0 to 4 neighbours (von Neumann neighbourhood)
Classical
syn(i,j),t+1 = syn(i,j),t + 0.01 * (g(t) + f(t));
g(t) = 0.001* (syn(i-1,j),t + syn(i+1,j),t + syn(i,j-1),t + syn(i,j+1),t)
f(t) = -c(n) * (t - n) * (t - n - 1) , c(n) = (n + 0.5)(-0.1)
New
syn(i,j),t+1 = syn(i,j),t - 0.01 * (g(t) + f(t));
g(t) = 0.001* (syn(i-1,j),t + syn(i+1,j),t + syn(i,j-1),t + syn(i,j+1),t)
f(t) = -c(n) * (t - n) * (t - n - 1) , c(n) = (n + 0.5)(-0.1)
Threshold
syn(i,j),t+1 = syn(i,j),t + 0.01 * (g(t) + f(t))
for syn(i,j),t ≥ threshold
syn(i,j),t+1 = syn(i,j),t + 0.01 * (g(t) - f(t))
for syn(i,j),t < threshold
synthres = 0.5
Classical
syn(i,j),t+1 = syn(i,j),t + 0.01 * (g(t) + f(t));
g(t) = 0.001* (syn(i-1,j),t + syn(i+1,j),t + syn(i,j-1),t + syn(i,j+1),t)
f(t) = -c(n) * (t - n) * (t - n - 1) , c(n) = 1.5 * (n + 0.5)(-0.1)
New
syn(i,j),t+1 = syn(i,j),t - 0.01 * (g(t) + f(t));
g(t) = 0.001* (syn(i-1,j),t + syn(i+1,j),t + syn(i,j-1),t + syn(i,j+1),t)
f(t) = -c(n) * (t - n) * (t - n - 1) , c(n) = 1.5 * (n + 0.5)(-0.1)
Threshold
syn(i,j),t+1 = syn(i,j),t + 0.01 * (g(t) + f(t))
for syn(i,j),t ≥ threshold
syn(i,j),t+1 = syn(i,j),t + 0.01 * (g(t) – f(t))
for syn(i,j),t < threshold
synthres = 0.5
Why do we sleep?
Conservation of metabolic energy, higher mental function, heat retention, learning and memory?
highly simplifying assumptions ideas which you could base further models on Classical interpretation: 170% (200%) of original
strength after a few iterations every memory reinforces
New interpretation: significant weakening of synapses, only the initially strongest survive principally we forget
Bigger bursts cause stronger synapses at the end (classical), more vanishing (new), both for our model after sleep loss our brain has to process more data, more extreme results
Our model: growing and diminishing synapses, depending on the initial conditions “strong” memories persist and reinforce, unimportant ones disappear
nobody can retain every cognition sleep as the brain’s selection of the most
important things to retain (new interpretation or our model?)