Scaling Laws in Cognitive Science Christopher Kello Cognitive and Information Sciences Thanks to NSF, DARPA, and the Keck Foundation.

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Presentation transcript:

Scaling Laws in Cognitive Science Christopher Kello Cognitive and Information Sciences Thanks to NSF, DARPA, and the Keck Foundation

Background and Disclaimer Cognitive Mechanics… Fractional Order Mechanics?

Reasons for FC in Cogsci Intrinsic Fluctuations Critical Branching Lévy-like Foraging Continuous-Time Random Walks

Intrinsic Fluctuations Neural activity is intrinsic and ever-present – Sleep, “wakeful rest” Behavioral activity also has intrinsic expressions – Postural sway, gait, any repetition

Lowen & Teich (1996), JASA Allan Factor Analyses Show Scaling Law Clustering Intrinsic Fluctuations In Spike Trains

Intrinsic Fluctuations in LFPs Beggs & Plenz (2003), J Neuroscience Bursts of LFP Activity in Rat Somatosensory Slice Preparations

Mazzoni et al. (2007), PLoS One Burst Sizes Follow a 3/2 Inverse Scaling Law Intrinsic Fluctuations in LFPs Intact Leech Ganglia Dissociated Rat Hippocampus

Intrinsic Fluctuations in Speech

Log f Log S(f) S(f) ~ 1/f α

Scaling Laws in Brain and Behavior How can we model and simulate the pervasiveness of these scaling laws? – Clustering in spike trains – Burst distributions in local field potentials – Fluctuations in repeated measures of behavior

Critical Branching Critical branching is a critical point between damped and runaway spike propagation Damped Runaway pre post

Spiking Network Model Leaky Integrate & Fire Neuron Source Sink Reservoir

Critical Branching Algorithm

Critical Branching Tuning Tuning ONTuning OFF

Spike Trains

Allan Factor Results

Neuronal Bursts

Neuronal Avalanche Results

Simple Response Series

1/f Noise in Simple Responses

Memory Capacity of Spike Dynamics

Critical Branching and FC The critical branching algorithm produces pervasive scaling laws in its activity. FC might serve to: – Analyze and better understand the algorithm – Formalize the capacity for spike computation – Refine and optimize the algorithm

Lévy-like Foraging Animal Foraging Memory Foraging

Lévy-like Visual Search

Lévy-like Foraging Games

“Optimizing” Search with Levy Walks Lévy walks with μ ~ 2 are maximally efficient under certain assumptions How can these results be generalized and applied to more challenging search problems?

Continuous-Time Random Walks In general, the CTRW probability density obeys Mean waiting time: Jump length variance:

Human-Robot Search Teams Wait times correspond to times for vertical movements Tradeoff between sensor accuracy and scope Human-controlled and algorithm-controlled search agents in virtual environments

Conclusions Neural and behavioral activities generally exhibit scaling laws Fractional calculus is a mathematics suited to scaling law phenomena Therefore, cognitive mechanics may be usefully formalized as fractional order mechanics

Collaborators Gregory Anderson Brandon Beltz Bryan Kerster Jeff Rodny Janelle Szary Marty Mayberry Theo Rhodes John Beggs Stefano Carpin YangQuan Chen Jay Holden Guy Van Orden