In the top-down connection (OFC to amygdala), we found negative functional connectivity in HC (P < 0.01) and positive connectivity in SAD patients (P < 0.05) with a significant group difference (P < 0.05 corr).
Follow up two-sample t-tests showed that TD group showed a significantly larger modulatory effect than the RD group in the bottom-up connection from FG to MTG (t(22) = 2.304, p = .031), but not in the top-down connection (t(22) = −0.462, p = .649) (See Fig. 1).
Interestingly, the only negative or functional inhibitory effect was expressed by the top-down connection from IT/F to mMO.
Based on these results, there is strong evidence for models with a top-down connection between middle occipital areas (first level) and IT/F (third level), but there is no evidence for lateral connections between lMO and mMO (first level).
Still other models are based on different mechanisms, such as the modification by perceptual learning of neural connections in a single visual area or of cortical top-down connections that feed into early-stage processing areas from high-level areas.
This adaptive and contextual specialisation is mediated by functional integration or interactions among brain systems with a special emphasis on backwards or top-down connections.
We also show that various new functions can be realized by, for example, introducing top-down connections to the neocognitron: mechanism of selective attention, recognition and completion of partly occluded patterns, restoring occluded contours, and so on.
Specifically they show that in a model where top-down connections gate flow of bottom-up inputs to decision units, learning acts upon the weights of the top-down connections rather than tuning properties of the bottom-up (sensory) inputs.
At the next step of BMS analysis, the 16 models included in family 3 were partitioned into 4 separate families (family A, B, C, D) with different modulatory effects on top-down connections.
In other words, in auditory circuitry with specific neuronal subpopulations, immediate N1 suppression might reflect feed-forward responses that evolve and contribute over time to top-down connections that consolidate and contribute to the observed P2 enhancement.
Herzog & Fahle [49] put forward a recurrent neural network model of perceptual learning that empahsizes the role of plasticity in the top-down connections as an enabling process for perceptual learning.
