Neural state space

…is unclear how outcome feedback alters neural representations of these variables and impacts

Recent studies have proposed that neural circuits generate a task manifold: i.e., a subset of the neural state-space to which neural activity is confined as an animal performs a task. Thus, discovering and characterising these manifolds from experimental data can shed light on the neural computations unfolding within the brain during behavioural tasks. In parallel, neuroscience has seen a recent surge of interest into fitting dynamical systems to experimental data with the goal of reverse-engineering neural circuits. Yet, common neural manifolds frameworks often do not take into account that n

Classical views suggest that memories are stored in assemblies of excitatory neurons that become strongly interconnected during learning. However, recent experimental and theoretical results have challenged this view, leading to the hypothesis that memories are encoded in assemblies containing both excitatory (E) and inhibitory (I) neurons. Understanding the effects of these E-I assemblies on memory function is therefore essential. Using a biologically constrained model of an olfactory memory network, I will first describe how introducing E-I assemblies reorganizes odor-evoked activity pattern

Recent studies have proposed that neural circuits have a $\textit{task manifold}$: i.e., a subset of the neural state-space to which neural activity is confined as an animal performs a task [1]. Thus, discovering and characterising these manifolds and their associated dynamics from experimental data can shed light on the neural computations unfolding within the brain during various cognitive tasks. Yet, common manifold discovery methods often do not take into account that neural data is generated by an underlying dynamical system. To address this, we first derive a general class of manifolds t

…shed light on this effect, recurrent neural networks (RNNs) of spiking neurons were trained, and their behavior and firing activity were examined in comparison to those obser

Recent studies on representational drift in the visual cortex have produced an intriguing puzzle, demonstrating that neuronal responses to the same external stimuli change over time while population-level activity remains stable (Deitch et al., 2021; Marks & Gourd, 2021). The purpose of drift, and how the brain either compensates for or takes advantage of this phenomenon to maintain robust perception, remains largely unknown. A recent study found that the geometric properties of drift in the primary visual cortex resemble that of continual learning in the presence of a large amount of drop

How the brain stores a sequence in memory remains largely unknown. We investigated the neural code underlying sequence working memory using two-photon calcium imaging to record thousands of neurons in the prefrontal cortex of macaque monkeys memorizing and then reproducing a sequence of locations after a delay. We discovered a regular geometrical organization: The high-dimensional neural state space during the delay could be decomposed into a sum of low-dimensional subspaces, each storing the spatial location at a given ordinal rank, which could be generalized to novel sequences and explain mo