Predictive Coding Theories of Cortical Function

Predictive coding is a unifying framework for understanding perception, action and neocortical organization. In predictive coding, different areas of the neocortex implement a hierarchical generative model of the world that is learned from sensory inputs. Cortical circuits are hypothesized to perform Bayesian inference based on this generative model. Specifically, the Rao-Ballard hierarchical predictive coding model assumes that the top-down feedback connections from higher to lower order cortical areas convey predictions of lower-level activities. The bottom-up, feedforward connections in turn convey the errors between top-down predictions and actual activities. These errors are used to correct current estimates of the state of the world and generate new predictions. Through the objective of minimizing prediction errors, predictive coding provides a functional explanation for a wide range of neural responses and many aspects of brain organization

Organization and development of cholinergic input to the mouse visual thalamus.

Cholinergic signaling plays a vital role in modulating the flow of sensory information through thalamic circuits in a state-dependent manner. In the dorsal lateral geniculate nucleus (dLGN), the thalamic visual relay, release of acetylcholine (ACh) contributes to enhanced thalamocortical transfer of retinal signal during behavioral states of arousal, wakefulness, and sleep/wake transitions. Moreover, ACh modulates activity of the thalamic reticular nucleus (TRN), a structure which provides inhibitory input to dLGN. While several cholinergic nuclei have been shown to innervate dLGN and TRN, it is unclear how projections from each area are organized. Furthermore, little is known of how or when cholinergic fibers arrive and form functional synapses during development. To address these questions, we used a genetically modified mouse (ChAT-Cre) mouse to selectively visualize cholinergic projections to dLGN and TRN. We conducted anterograde viral tracing, demonstrating a mainly contralateral cholinergic projection from the parabigeminal nucleus to dLGN. In addition, we saw a sparse ipsilateral projection from the rostral pedunculopontine tegmentum to dLGN and TRN. Next, we used a fluorescent reporter line (Ai9) to visualize cholinergic innervation in dLGN and TRN during early postnatal life. In dLGN, innervation began by the end of the first week, increased steadily with age, and reached an adult-like state by the end of the first month. Furthermore, using a model of visual deafferentation (math5-/-), we showed that the absence of retinal input resulted in disruptions in the trajectory, rate, and pattern of cholinergic innervation in dLGN. In TRN, innervation began during week 1 in the ventral non-visual sectors, proceeded into the dorsal visual sector during week 2, and reached adult-like levels by week 3. To assess the functional maturation of cholinergic synapses within TRN, we used a channelrhodopsin-2 reporter and selectively stimulated cholinergic afferents while conducting recordings from TRN neurons. Postsynaptic responses appeared in non-visual sectors of TRN during the first postnatal week, and in the visual sector by week 2. By the end of the first month, all sectors of TRN exhibited adult-like biphasic responses. Together, these studies shed light on the organizational pattern and developmental progression of cholinergic input to the visual thalamus

The role of the anterior cingulate cortex in prediction error and signaling surprise

In the past two decades, reinforcement learning (RL) has become a popular framework for understanding brain function. A key component of RL models, prediction error, has been associated with neural signals throughout the brain, including subcortical nuclei, primary sensory cortices, and prefrontal cortex. Depending on the location in which activity is observed, the functional interpretation of prediction error may change: Prediction errors may reflect a discrepancy in the anticipated and actual value of reward, a signal indicating the salience or novelty of a stimulus, and many other interpretations. Anterior cingulate cortex (ACC) has long been recognized as a region involved in processing behavioral error, and recent computational models of the region have expanded this interpretation to include a more general role for the region in predicting likely events, broadly construed, and signaling deviations between expected and observed events. Ongoing modeling work investigating the interaction between ACC and additional regions involved in cognitive control suggests an even broader role for cingulate in computing a hierarchically structured surprise signal critical for learning models of the environment. The result is a predictive coding model of the frontal lobes, suggesting that predictive coding may be a unifying computational principle across the neocortex. This paper reviews the brain mechanisms responsible for surprise; focusing on the Anterior Cingulate Cortex (ACC), long-known to play a role in behavioral-error, with a recently-expanded role in predicting likely' events and signaling deviations between expected and observed events. It argues for ACC's role in in surprise and learning, based on recent modelling work. As such, the paper provides the neuroscience complement to the psychological and computational proposals of other papers in the volume

Decoding face categories in diagnostic subregions of primary visual cortex

Higher visual areas in the occipitotemporal cortex contain discrete regions for face processing, but it remains unclear if V1 is modulated by top-down influences during face discrimination, and if this is widespread throughout V1 or localized to retinotopic regions processing task-relevant facial features. Employing functional magnetic resonance imaging (fMRI), we mapped the cortical representation of two feature locations that modulate higher visual areas during categorical judgements – the eyes and mouth. Subjects were presented with happy and fearful faces, and we measured the fMRI signal of V1 regions processing the eyes and mouth whilst subjects engaged in gender and expression categorization tasks. In a univariate analysis, we used a region-of-interest-based general linear model approach to reveal changes in activation within these regions as a function of task. We then trained a linear pattern classifier to classify facial expression or gender on the basis of V1 data from ‘eye’ and ‘mouth’ regions, and from the remaining non-diagnostic V1 region. Using multivariate techniques, we show that V1 activity discriminates face categories both in local ‘diagnostic’ and widespread ‘non-diagnostic’ cortical subregions. This indicates that V1 might receive the processed outcome of complex facial feature analysis from other cortical (i.e. fusiform face area, occipital face area) or subcortical areas (amygdala)