Learning enhances the relative impact of top-down processing in the visual cortex

Abstract

Theories have proposed that in sensory cortices learning can enhance top-down modulation by higher brain areas while reducing bottom-up sensory inputs. To address circuit mechanisms underlying this process, we examined the activity of layer 2/3 (L2/3) excitatory neurons in the mouse primary visual cortex (V1) as well as L4 neurons, the main bottom-up source, and long-range top-down projections from the retrosplenial cortex (RSC) during associative learning over days using chronic two-photon calcium imaging. During learning, L4 responses gradually weakened, while RSC inputs became stronger. Furthermore, L2/3 acquired a ramp-up response temporal profile with learning, coinciding with a similar change in RSC inputs. Learning also reduced the activity of somatostatin-expressing inhibitory neurons (SOM-INs) in V1 that could potentially gate top-down inputs. Finally, RSC inactivation or SOM-IN activation was sufficient to partially reverse the learning-induced changes in L2/3. Together, these results reveal a learning-dependent dynamic shift in the balance between bottom-up and top-down information streams and uncover a role of SOM-INs in controlling this process.

Attentional Correlation Filter Network for Adaptive Visual Tracking

“…Furthermore, the robustness of the tracker increases due to the exploitation of previous experience, which allows concentrating on the expected appearance and dynamic changes and ignoring irrelevant properties of a scene. The importance of attentional mechanisms of the human visual system is highlighted by recent trends in neuroscience research [13,14,28], as well as by theoretical work on the computational aspects of visual attention [12,34]. Similarly, we find that our framework using the attentional mechanism outperforms a network which utilises all module trackers while being significantly faster.…”

“…Humans rely on various cues when observing and tracking objects, and the selection of attentional cues highly depends on knowledge-based expectation according to the dynamics of the current scene [13,14,28]. Similarly, in order to infer the accurate location of the target object, a tracker needs to take changes of several appearance (illumination change, blurriness, occlusion) and dynamic (expanding, shrinking, aspect ratio change) properties into account.…”

Natural Firing Patterns Imply Low Sensitivity of Synaptic Plasticity to Spike Timing Compared with Firing Rate

“…Relating network-wide synaptic changes with network activity under natural conditions is currently challenging, both from an experimental and theoretical point of view. Recent experiments that track network activity during learning using calcium imaging (Kato et al, 2015;Makino and Komiyama, 2015;Poort et al, 2015) may provide an important foray in this direction. Theoretically, the framework developed here to link natural firing patterns and plasticity should facilitate the study of plasticity at the network level.…”

The form and function of channelrhodopsin

“…Even psychiatry has yielded some of its deepest mysteries to ChR pore structural insights (Fig. 5), as exemplified by studies of circuit dynamics underlying the core depression symptom of anhedonia (59) in which natural reward responses central to the behavior of all animals are lost. The presence of anhedonia allows a diagnosis of major depressive disorder even without depressed mood, but causal circuit dynamics–level understanding had remained elusive.…”