State-dependent computations:
spatiotemporal processing in
cortical networks
(i.e. taking time into account)
by Dean V. Buonomano and Wolfgang Maass
Kristjan, Comp neuro seminar, 04.04.2014, Tartu
The Crux Computational Problem: Core
Recognition Requires Invariance
The Invariance of Core Object Recognition: A
Graphical Intuition into the Problem
What Do We Know about the Brain’s ‘‘Object’’
Representation? The Ventral Visual Stream Houses
Critical Circuitry for Core Object Recognition
Canonical Cortical Algorithms: Possible Mechanisms
of Subspace Untangling
Desperately want to understand the brain!
Spatial information is important
but what about poor temporal information…
• Visual
• Direction and velocity
• Duration and interval
• Somatosensory
• Motion
• Object and texture discrimination
• Auditory system
• Spectral and temporal sounds contain information
• Morse code!
Some hacks
• Spatialization of time
• Implicitly representing time
• No continues representations
• Not realistic
• How to solve it?
Stat-dependent computations
• Inputs interact with internal states
• Active and hidden internal states
Stat-dependent computations
• Inputs interact with internal states
• Active and hidden internal states
Stat-dependent computations
• Inputs interact with internal states
• Active and hidden internal states
• Hidden biological factors:
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slow inhibitory postsynaptic potentials (IPSPs)
metabotropic glutamate currents
ion channel kinetics
Ca2+ dynamics in synaptic and cellular compartments
NMDA (N-methyl-d-aspartate) channel kinetics
Decoding neural trajectories
• Lots of new machine learning algorithms
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liquid-state machines
echo-state networks
state-dependent networks
reservoir computing
• Common results
• trajectories of active network states can be used for
noise-robust computations on time-varying external
inputs.
Decoding neural trajectories
• „Read-out neurons“
• weighted sum
Result
Noise, chaos and network dynamics
Computing with trajectories
• Change in perspective
• No attractors any more
• Evidence
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Olfactory system
Timing in the cerebellum
State-dependent cortical responses
State-dependent temporal processing
Visual cortex
Summary of the framework
1. Networks with interaction between external stimuli
and the internal state of the network
2. Cortical microcircuits contribute to computations by
projecting network responses into high-dimensional
representations, which amplifies the separation of
network trajectories
3. Convergence to ‘read-out’ neurons, allows for
decoding by appropriately adjusting the synaptic
weights between these groups of neurons
4. Different read-out neurons can extract different
features of the information (for example, spatial or
temporal features) present in the trajectory of active
network states.
Prediction
• The population response of cortical networks
should not be interpreted as simply encoding the
current stimulus, but rather as generating a
representation of each incoming stimulus in the
context of the previous stimuli