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1. Context-dependent modulation of natural approach behaviour in mice

   https://doi.org/10.1098/rspb.2020.1189  

   Procacci, Nicole M; Allen, Kelsey M; Robb, Gael E; Ijekah, Rebecca; Lynam, Hudson; Hoy, Jennifer L (September 2020, Proceedings of the Royal Society B: Biological Sciences)

   Specific features of visual objects innately draw approach responses in animals, and provide natural signals of potential reward. However, visual sampling behaviours and the detection of salient, rewarding stimuli are context and behavioural state-dependent and it remains unclear how visual perception and orienting responses change with specific expectations. To start to address this question, we employed a virtual stimulus orienting paradigm based on prey capture to quantify the conditional expression of visual stimulus-evoked innate approaches in freely moving mice. We found that specific combinations of stimulus features selectively evoked innate approach or freezing responses when stimuli were unexpected. We discovered that prey capture experience, and therefore the expectation of prey in the environment, selectively modified approach frequency, as well as altered those visual features that evoked approach. Thus, we found that mice exhibit robust and selective orienting responses to parameterized visual stimuli that can be robustly and specifically modified via natural experience. This work provides critical insight into how natural appetitive behaviours are driven by both specific features of visual motion and internal states that alter stimulus salience. 

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2. Distinct attentional characteristics of neurons with visual feature coding in the primate brain

   https://doi.org/10.1126/sciadv.adq0332  

   Zhang, Jie; Cao, Runnan; Zhu, Xiaocang; Zhou, Huihui; Wang, Shuo (March 2025, Science Advances)

   Visual attention and object recognition are two critical cognitive functions that shape our perception of the world. While these neural processes converge in the temporal cortex, the nature of their interactions remains largely unclear. Here, we systematically investigated the interplay between visual attention and stimulus feature coding by training macaques to perform a free-gaze visual search task with natural stimuli. Recording from a large number of units across multiple brain areas, we found that units exhibiting visual feature coding showed stronger attentional modulation of responses and spike–local field potential coherence than units without feature coding. Across brain areas, attention directed toward search targets enhanced the neuronal pattern separation of stimuli, with this enhancement more pronounced for units encoding visual features. Together, our results suggest a complex interplay between visual feature and attention coding in the primate brain, likely driven by interactions between brain areas engaged in these processes. 

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3. Spatial working memory alters the efficacy of input to visual cortex

   Merrikhi, Yaser; Clark, Kelsey; Albarran, Eddy; Parsa, Mohammadbagher; Zirnsak, Marc; Moore, Tirin; Noudoost, Behrad (April 2017, Nature communications)

   Prefrontal cortex modulates sensory signals in extrastriate visual cortex, in part via its direct projections from the frontal eye field (FEF), an area involved in selective attention. We find that working memory-related activity is a dominant signal within FEF input to visual cortex. Although this signal alone does not evoke spiking responses in areas V4 and MT during memory, the gain of visual responses in these areas increases, and neuronal receptive fields expand and shift towards the remembered location, improving the stimulus representation by neuronal populations. These results provide a basis for enhancing the representation of working memory targets and implicate persistent FEF activity as a basis for the interdependence of working memory and selective attention. 

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4. Category Learning Selectively Enhances Representations of Boundary-Adjacent Exemplars in Early Visual Cortex

   https://doi.org/10.1523/JNEUROSCI.1039-23.2023  

   O’Bryan, Sean R; Jung, Shinyoung; Mohan, Anto J; Scolari, Miranda (January 2024, The Journal of Neuroscience)

   Category learning and visual perception are fundamentally interactive processes, such that successful categorization often depends on the ability to make fine visual discriminations between stimuli that vary on continuously valued dimensions. Research suggests that category learning can improve perceptual discrimination along the stimulus dimensions that predict category membership and that these perceptual enhancements are a byproduct of functional plasticity in the visual system. However, the precise mechanisms underlying learning-dependent sensory modulation in categorization are not well understood. We hypothesized that category learning leads to a representational sharpening of underlying sensory populations tuned to values at or near the category boundary. Furthermore, such sharpening should occur largely during active learning of new categories. These hypotheses were tested using fMRI and a theoretically constrained model of vision to quantify changes in the shape of orientation representations while human adult subjects learned to categorize physically identical stimuli based on either an orientation rule (N = 12) or an orthogonal spatial frequency rule (N = 13). Consistent with our predictions, modeling results revealed relatively enhanced reconstructed representations of stimulus orientation in visual cortex (V1–V3) only for orientation rule learners. Moreover, these reconstructed representations varied as a function of distance from the category boundary, such that representations for challenging stimuli near the boundary were significantly sharper than those for stimuli at the category centers. These results support an efficient model of plasticity wherein only the sensory populations tuned to the most behaviorally relevant regions of feature space are enhanced during category learning. 

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5. Dynamic predictive coding: A model of hierarchical sequence learning and prediction in the neocortex

   https://doi.org/10.1371/journal.pcbi.1011801  

   Jiang, Linxing Preston; Rao, Rajesh_P N (February 2024, PLOS Computational Biology)

   Rubin, Jonathan (Ed.)

   We introduce dynamic predictive coding, a hierarchical model of spatiotemporal prediction and sequence learning in the neocortex. The model assumes that higher cortical levels modulate the temporal dynamics of lower levels, correcting their predictions of dynamics using prediction errors. As a result, lower levels form representations that encode sequences at shorter timescales (e.g., a single step) while higher levels form representations that encode sequences at longer timescales (e.g., an entire sequence). We tested this model using a two-level neural network, where the top-down modulation creates low-dimensional combinations of a set of learned temporal dynamics to explain input sequences. When trained on natural videos, the lower-level model neurons developed space-time receptive fields similar to those of simple cells in the primary visual cortex while the higher-level responses spanned longer timescales, mimicking temporal response hierarchies in the cortex. Additionally, the network’s hierarchical sequence representation exhibited both predictive and postdictive effects resembling those observed in visual motion processing in humans (e.g., in the flash-lag illusion). When coupled with an associative memory emulating the role of the hippocampus, the model allowed episodic memories to be stored and retrieved, supporting cue-triggered recall of an input sequence similar to activity recall in the visual cortex. When extended to three hierarchical levels, the model learned progressively more abstract temporal representations along the hierarchy. Taken together, our results suggest that cortical processing and learning of sequences can be interpreted as dynamic predictive coding based on a hierarchical spatiotemporal generative model of the visual world. 

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