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

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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:• 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• 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• 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