Consciousness, Evolution, and Why Nature Outdid All of Us

Consciousness, Evolution, and

Why Nature Outdid All of Us

J. Leo van Hemmen

Physik Department der TU München

References

Why are neuronal algorithms of information processing interesting to us?

What is a neuron doing?

output

input

synapse

axon

Pyramidal neuron(after Ramon y Cajal 1898)

1 ms

What is a synapse doing?presynaptic action potential = spike

postsynaptic membrane potential

synaptic transmission

Consciousness is a matter of definition.

a) Neuronal

b) Synaptic

c) Potency of an individuum to realize itself in a usually hostile surroundings.

The really interesting problem is that of attention.

Sound localization, i.e., direction, by means of determining interaural time difference (1 µs).

Barn owl is a night hunter

• One mouse every 10 minutes.

• Localization accuracy < 2°

• Temporal resolution < 4 µs

Jeffress model (1948)

Place code through maximum of firing rate:

``map´´ as neuronal representation of the outside world

Jeffress-like map in the barn owl‘s laminar nucleus.

Anatomy: delay lines & how to tune them

Neuron

output

input

synapse

axon

Pyramidal neuron(after Ramon y Cajal 1898)

postsynaptic

pre-

Axon-mediated synaptic learning (AMSL)

STDPW. Gerstner, R. Kempter,J.L. van Hemmen, H. WagnerNature 383(1996) 76-78

W. Gerstner, R. Kempter,J.L. van Hemmen, H. WagnerNature 383(1996) 76-78

postsynaptic

pre-

Relating synaptic input time to the postsynaptic firing time (t=0) generates the final distribution of synaptic strengths c), a collective process since all the inputs determine when precisely the postsynaptic neuron (bottom left) fires. Once it has fired, the synaptic efficacy of each individual synapse changes (center A, with change in EPSC, vertical axis in B as its experimental analog, far right) according to the arrival time of the (presynaptic) spike (input = horizontal axis in B; bottom right). The initial synaptic configuration is a), top left, plotted in dependence upon the corresponding delay of the signal it carries; b) is at a later time and c) is final.

Through millions of years of evolution Nature has taken lots of time to devise, and test, highly efficient algorithms of neuronal information processing.

For example, the sand scorpion did not change during the last 165 million years.

To locate ist prey in the desert, a sand scorpion exploits Raleigh waves in sand.

Neuronal algorithm (vector code) to determine the prey‘s direction.

Experimental tests verify theory.

ConclusionsConclusions

• Nature has devised, and developed, highly efficient algorithms to solve specific information processing problems.

• Spiking neurons and spike-timing-dependent synaptic plasticity (STDP) are essential ingredients of spatio-temporal processing.

• Neuronal hardware and software cooperate so as to reach an optimal solution.

• Hence there is a wealth of neuronal algorithms to solve ``real-life´´ neuro-IT problems

• ...so that our goal is to obtain a Tools from Neuroscience Project.

• Nature has devised, and developed, highly efficient algorithms to solve specific information processing problems.

• Spiking neurons and spike-timing-dependent synaptic plasticity (STDP) are essential ingredients of spatio-temporal processing.

• Neuronal hardware and software cooperate so as to reach an optimal solution.

• Hence there is a wealth of neuronal algorithms to solve ``real-life´´ neuro-IT problems

• ...so that our goal is to obtain a Tools from Neuroscience Project.