Embodied models of delayed neural responses: spatiotemporal categorization and predictive motor control in brain based devices

Machine Learning Capabilities of a Simulated Cerebellum

This paper describes the learning and control capabilities of a biologically constrained bottom-up model of the mammalian cerebellum. Results are presented from six tasks: 1) eyelid conditioning; 2) pendulum balancing; 3) proportional-integral-derivative control; 4) robot balancing; 5) pattern recognition; and 6) MNIST handwritten digit recognition. These tasks span several paradigms of machine learning, including supervised learning, reinforcement learning, control, and pattern recognition. Results over these six domains indicate that the cerebellar simulation is capable of robustly identifying static input patterns even when randomized across the sensory apparatus. This capability allows the simulated cerebellum to perform several different supervised learning and control tasks. On the other hand, both reinforcement learning and temporal pattern recognition prove problematic due to the delayed nature of error signals and the simulator's inability to solve the credit assignment problem. These results are consistent with previous findings which hypothesize that in the human brain, the basal ganglia is responsible for reinforcement learning, while the cerebellum handles supervised learning.

Temporal sequence learning in winner-take-all networks of spiking neurons demonstrated in a brain-based device

Animal behavior often involves a temporally ordered sequence of actions learned from experience. Here we describe simulations of interconnected networks of spiking neurons that learn to generate patterns of activity in correct temporal order. The simulation consists of large-scale networks of thousands of excitatory and inhibitory neurons that exhibit short-term synaptic plasticity and spike-timing dependent synaptic plasticity. The neural architecture within each area is arranged to evoke winner-take-all (WTA) patterns of neural activity that persist for tens of milliseconds. In order to generate and switch between consecutive firing patterns in correct temporal order, a reentrant exchange of signals between these areas was necessary. To demonstrate the capacity of this arrangement, we used the simulation to train a brain-based device responding to visual input by autonomously generating temporal sequences of motor actions.

The corollary discharge in humans is related to synchronous neural oscillations

How do animals distinguish between sensations coming from external sources and those resulting from their own actions? A corollary discharge system has evolved that involves the transmission of a copy of motor commands to sensory cortex, where the expected sensation is generated. Through this mechanism, sensations are tagged as coming from self, and responsiveness to them is minimized. The present study investigated whether neural phase synchrony between motor command and auditory cortical areas is related to the suppression of the auditory cortical response. We recorded electrocorticograms from the human brain during a vocalizing/listening task. Neural phase synchrony between Broca's area and auditory cortex in the gamma band (35 to ∼50 Hz) in the 50-msec time window preceding speech onset was greater during vocalizing than during listening to a playback of the same spoken sounds. Because prespeech neural synchrony was correlated (r = -.83, p = .006), with the subsequent suppression of the auditory cortical response to the spoken sound, we hypothesize that phase synchrony in the gamma band between Broca's area and auditory cortex is the neural instantiation of the transmission of a copy of motor commands. We suggest that neural phase synchrony of gamma frequencies may contribute to transmission of corollary discharges in humans.