Learning to make external sensory stimulus predictions using internal correlations in populations of neurons

Abstract

To compensate for sensory processing delays, the visual system must make predictions to ensure timely and appropriate behaviors. Recent work has found predictive information about the stimulus in neural populations early in vision processing, starting in the retina. However, to utilize this information, cells downstream must be able to read out the predictive information from the spiking activity of retinal ganglion cells. Here we investigate whether a downstream cell could learn efficient encoding of predictive information in its inputs from the correlations in the inputs themselves, in the absence of other instructive signals. We simulate learning driven by spiking activity recorded in salamander retina. We model a downstream cell as a binary neuron receiving a small group of weighted inputs and quantify the predictive information between activity in the binary neuron and future input. Input weights change according to spike timing-dependent learning rules during a training period. We characterize the readouts learned under spike timing-dependent synaptic update rules, finding that although the fixed points of learning dynamics are not associated with absolute optimal readouts they convey nearly all of the information conveyed by the optimal readout. Moreover, we find that learned perceptrons transmit position and velocity information of a moving-bar stimulus nearly as efficiently as optimal perceptrons. We conclude that predictive information is, in principle, readable from the perspective of downstream neurons in the absence of other inputs. This suggests an important role for feedforward prediction in sensory encoding.

Information Closure Theory of Consciousness

“…Very few experiments and data collections examining NTIC properties in neural systems have yet appeared. To our knowledge, only two studies (Palmer et al, 2015 ; Sederberg et al, 2018 ) coincidentally examined relevant properties in salamander retina; these found that a large group of neural populations of retinal ganglion cells encoded predictive information about external stimuli and also had high self-predictive information about their own future states. This result is consistent with the characteristic of NTIC.…”

Predictive encoding of motion begins in the primate retina

“…The correlations inherent to visual motion mean that the motion trajectory leading up to a particular time (t ≤ 0) provides information about the likely trajectory of motion at later time points (t > 0). This information can then be used to estimate motion trajectories and to guide behavior (Bialek et al, 2006;Palmer et al, 2015;Salisbury and Palmer, 2016;Chalk et al, 2018;Sederberg et al, 2018). The correlations in the motion stimuli used in our study contained this type of predictive information within a time window of approximately ±50 ms ( Figure 3D).…”

Sources of predictive information in dynamical neural networks

“…The efficient coding principle focuses on how the nervous system extracts predictive information from environmental stimuli. Both theories have been supported by experimental evidence, primarily in the visual and auditory systems [9][10][11][12] .…”