Bernal S D_2010.pdf - University of Plymouth

3.2. EVIDENCE FROM tm BRAIN
feedback, which can also be accommodated within Bayesian inference theory (Chikkerur et al,
2009, Spratling 2008b, Friston 201(1). These results are explained as an increase in the belief
or prediction populations, as a consequence of an enhancement of features consistent with the
global percept.
Moreover, the Bayesian framework is also compatible with basic synaptic physiology such as
Hebbian plasticity, which results from the optimization of the generative model parameters in
order to reduce prediction error (Friston et al. 2006). A recent study (Nessler et al. 2009)
further showed how a winner-lake-all network of spiking neurons implementing a spike-timing-
dependent plasticity rule could be understood in terms of a hierarchical generative model which
discovered the causes of its input.
Research has also made progress in accommodating the probabilistic framework at a neuronal
processing level, describing how simple spiking neuron responses and population codes can
represent probability di.stributions and implement inference (Pouget ei al. 2003, Zemel et al.
2004, Deneve 2008a,b, Ma et al. 2006, Wu and Amari 2001). A recent outstanding publication
(Soltani and Wang 2010) demonstrated how neuronal synaptic computations could underlie
probabilistic inference by integrating information from individual cues. The model, validated on
data from an experiment on a monkey performing a categorization task, showed how synapses,
based on reward-dependent plasticity, naturally encode the posterior probability over different
causes given the presentation of specific cues.
Our understanding of the psychophysics of action and perception has also strongly benefited
from Bayesian inference approaches. These have provided a unifying framework to model the
psychophysics of object perception (Kerslen et al, 2004, Knill and Richards 1996. Yuille and
Kersten 2006), resolving its complexities and ambiguities by probabilistic integration of prior
object knowledge with image features. Interestingly, visual illusions, which are typically inter­
preted as errors of some imprecise neural mechanism, can in fact be seen as the optima! adap­
tation of a perceptual system obeying rules of Bayesian inference (Geisler and Kcrsten 2002).
Similarly, Weiss and Adelson (1998) presented a Bayesian model of motion perception which
predicted a wide range of psychophysical results, including a set of complex visual illusions, by
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