Optimizing with synapses Sebastian Seung Howard Hughes Medical Institute and Brain & Cog. Sci....
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Transcript of Optimizing with synapses Sebastian Seung Howard Hughes Medical Institute and Brain & Cog. Sci....
Optimizing with synapses
Sebastian Seung
Howard Hughes Medical Institute
and Brain & Cog. Sci. Dept., MIT
Practice makes perfect
• Birdsong learned from male tutor
• Stored template• Zebra finch: up to
100,000 iterations• Known anatomy and
physiology
Hahnloser, Kozhevnikov, Fee (2002)
Supervisory signals in the brain
• Global broadcast of reward signal• E.g. dopaminergic system
Neural basis of learning
Global signal(Reward, motor error, etc.)
Local signals (Voltage, calcium, etc.)
Synaptic plasticity
The interaction between global and local signals is largely uncharacterized.
Noise injection hypothesis
• HVC vs. LMAN– lesion– neural activity
RA
HVC
LMAN
motor neurons
Doya and Sejnowski
Trial and error learning
• Generation of variability• Reinforcement of favorable variations
Synaptic learning rule
1 2reward
3
regular synapses
noise synapses
regular
noise
reward
0W 0W Fiete and Seung
Optimization in biology
• Evolution– Search in genotype space– Random genetic variation
• Learning– Search in synapse space?– Unreliable synapses– Noise injection
Stochastic gradient learning
• Systems-level models of learning– Birdsong– Oculomotor system
• Synaptic plasticity in vitro– Microisland cultures
• Training neural circuits in vitro– Silicon stimulation– Pattern culture– Intrinsic imaging by interferometry
Reward-driven plasticity in vitro
Jen WangNaveen Agnihotri
Patterned culture by inkjet printing
Sawyer Fuller and Neville Sanjana
In vitro models of learning
• Goal: study how synaptic plasticity affects the dynamics of neural circuits
• Technical challenge: electrical and chemical control of neurons
• Conceptual challenge: training neural circuits