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Buschman, T. J.

Publications and source records attributed to Buschman, T. J..

22 records · Page 2Linked to original sources

Rotational Dynamics Reduce Interference Between Sensory and Memory Representations

Sensory stimuli arrive in a continuous stream. By learning statistical regularities in the sequence of stimuli, the brain can predict future stimuli (Xu et al. 2012; Gavornik and Bear 2014; Maniscalco et al. 2018; J. Fiser and Aslin 2002). Such learning requires associating immediate sensory information with the memory of recently encountered stimuli (Ostojic and Fusi 2013; Kiyonaga et al. 2017). However, new sensory information can also interfere with short-term memories (Parthasarathy et al. 2017). How the brain prevents such interference is unknown. Here, we show that sensory representations rotate in neural space over time, to form an independent memory representation, thus reducing interference with future sensory inputs. We used an implicit learning paradigm in mice to study how statistical regularities in a sequence of stimuli are learned and represented in primary auditory cortex. Mice experienced both common sequences of stimuli (e.g. ABCD) and uncommon sequences (e.g. XYCD). Over four days of learning, the neural population representation of commonly associated stimuli (e.g. A and C) converged. This facilitated the prediction of upcoming stimuli, but also led unexpected sensory inputs to overwrite the sensory representation of previous stimuli (postdiction). Surprisingly, we found the memory of previous stimuli persisted in a second, orthogonal dimension. Unsupervised clustering of functional cell types revealed that the emergence of this second memory dimension is supported by two separate types of neurons; a stable population that maintained its selectivity throughout the sequence and a switching population that dynamically inverted its selectivity. This combination of sustained and dynamic representations produces a rotation of the encoding dimension in the neural population. This rotational dynamic may be a general principle, by which the cortex protects memories of prior events from interference by incoming stimuli.

neuroscience↗

A Flexible Model of Working Memory

Working memory is fundamental to cognition, allowing one to hold information in mind and use it to guide behavior. A defining characteristic of working memory is its flexibility: we can hold anything in mind. However, typical models of working memory rely on finely tuned, content-specific, attractors to persistently maintain neural activity and therefore do not allow for the flexibility observed in behavior. Here we present a flexible model of working memory that maintains representations through random recurrent connections between two layers of neurons: a structured sensory layer and a randomly connected, unstructured, layer. As the interactions are untuned with respect to the content being stored, the network is able to maintain any arbitrary input. However, this flexibility comes at a cost: the random connections overlap, leading to interference between representations and limiting the memory capacity of the network. Additionally, our model captures several other key behavioral and neurophysiological characteristics of working memory.

neuroscience↗

Intrinsic neuronal dynamics predict distinct functional roles during working memory

Working memory (WM) is characterized by the ability to maintain stable representations over time; however, neural activity associated with WM maintenance can be highly dynamic. We explore whether complex population coding dynamics during WM relate to the intrinsic temporal properties of single neurons in lateral prefrontal cortex (lPFC), the frontal eye fields (FEF) and lateral intraparietal cortex (LIP) of two monkeys (Macaca mulatta). We found that cells with short timescales carried memory information relatively early during memory encoding in lPFC; whereas long timescale cells played a greater role later during processing, dominating coding in the delay period. We also observed a link between functional connectivity at rest and intrinsic timescale in FEF and LIP. Our results indicate that individual differences in the temporal processing capacity predicts complex neuronal dynamics during WM; ranging from rapid dynamic encoding of stimuli to slower, but stable, maintenance of mnemonic information.

neuroscience↗

Working Memory Load Modulates Neuronal Coupling

There is a severe limitation in the number of items that can be held in working memory. However, the neurophysiological limits remain unknown. We asked whether the capacity limit might be explained by differences in neuronal coupling. We developed a theoretical model based on Predictive Coding and used it to analyze Cross Spectral Density data from the prefrontal cortex (PFC), frontal eye fields (FEF) and lateral intraparietal area (LIP). Monkeys performed a change detection task (Buschman et al., 2011). The number of objects that had to be remembered (memory load) was varied (1-3 objects in the same visual hemifield). Changes in memory load changed the connectivity in the PFC-FEF-LIP network. Feedback (top-down) coupling broke down when the number of objects exceeded cognitive capacity. This provides new insights into the neuronal underpinnings of cognitive capacity and how coupling in a distributed working memory network is affected by memory load.

neuroscience↗