Long- and short-term history effects in a spiking network model of statistical learning

Abstract

The statistical structure of the environment is often important when making decisions. There are multiple theories of how the brain represents statistical structure. One such theory states that neural activity spontaneously samples from probability distributions. In other words, the network spends more time in states which encode high-probability stimuli. Existing spiking network models implementing sampling lack the ability to learn the statistical structure from observed stimuli and instead often hard-code a dynamics. Here, we focus on how arbitrary prior knowledge about the external world can both be learned and spontaneously recollected. We present a model based upon learning the inverse of the cumulative distribution function. Learning is entirely unsupervised using biophysical neurons and biologically plausible learning rules. We show how this prior knowledge can then be accessed to compute expectations and signal surprise in downstream networks. Sensory history effects emerge from the model as a consequence of ongoing learning.

From recency to central tendency biases in working memory: a unifying network model

“…We note that in our model, the stimulus distribution is not explicitly learned (but see [41]). Instead, since the PPC dynamics slowly follows the input, its marginal distribution of activity bump will be similar to the marginal distribution of the external input.…”

“…More studies are needed to fully verify the extent to which the statistical structure of the stimuli affect the performance. Finally, we note that in our model, the stimulus distribution is not explicitly learned (but see [58]): instead, the PPC dynamics follows the input, and its marginal distribution of activity is similar to that of the external input. Support for this idea comes from Ref.…”

Simplest Model of Nervous System. I. Formalism

“…Reference to this specific hypothetical scenario offers a fresh perspective and allows us to go beyond the existing models. For example, current literature often presents the function of a biological neural network as something like an unsupervised learning of probability distributions of the external world to minimize sensory uncertainty or surprisal [13][14][15][16][17][18][19][20] or, even simpler, as a minimization of mean square errors. [21][22][23][24][25] But if we bear in mind the hypothetical scenario of "Dickinsonia", then it is easier to notice that the connection between the sensory uncertainty or mean square error and evolutionary fitness is not straightforward and requires an explicit consideration.…”