Population activity

…ripple patterns and neuron types to population activity, asking how particular cells help shape the lower-dimensional dynamics used to describe neural computation.

Juan Gallego discusses whether neural manifolds are real explanatory structures and how population activity relates to behaviour. Topics include causal interpretations, evolutionary claims, motor control and the limits of low-dimensional descriptions, along with work on restoring movement after spinal-cord injury.

…by the notion that not all changes in population activity have equal meaning, i.e., a small change in

During development, neural circuits are shaped continuously as we learn to control our bodies, with the goal of producing neural dynamics that enable the rich repertoire of behaviors we perform with our limbs. However, the nature of the teaching signal underlying this normative learning process remains elusive. Here, we test two well-established and biologically plausible theories---supervised learning (SL) and reinforcement learning (RL)---that could explain how neural circuits develop the capacity for skilled movements. We trained recurrent neural networks to control a biomechanical model of

Learning enables animals to construct flexible knowledge structures, allowing them to adapt to new tasks by reconfiguring prior experiences into novel strategies. This compositional nature of learning suggests that fundamental building blocks from past experiences are essential for future problem-solving. To investigate how experience shapes learning, we trained Recurrent Neural Networks (RNNs) to model a complex, context-dependent odor timing task, using constraints derived from prior reports detailing mouse behavior and shaping procedures. The task, a temporal variant of the Delay Non-match

Studying and understanding the code of large neural populations hinges on learning accurate models of population activity. While statistical models based on pairwise interactions are highly accurate in describing the code of tens of neurons, accurate models of >100 neurons require higher order dependencies. Statistical models based on sparse nonlinear Random Projections (RP) of the population proved to be highly accurate and efficient for 100-200 neurons. The scaling of these models to larger populations is challenging yet critical for mapping the semantic organization of population codes, the

…confidence relates to the structure of population activity in the sensory cortex. And second, that this relation differs from the one between sensory activity and perceptual decisions. To this end, we studied neural population activity in the primary visual cortex of macaque monkeys performing a task that reveals the animal's subjective sense of perceptual confidence. Specifical

Biological agents continually reconcile the internal states of their brain circuits with incoming sensory and environmental evidence to evaluate when and how to act. The brains of biological agents, including animals and humans, exploit many evolutionary innovations, chiefly modularity—observable at the level of anatomically-defined brain regions, cortical layers, and cell types among others—that can be repurposed in a compositional manner to endow the animal with a highly flexible behavioral repertoire. Accordingly, their behaviors show their own modularity, yet such behavioral modules seldom

…shapes collective properties of neural populations and how such changes may impact encoding of sensory signals remain unknown. Here we tackle these questions directly by studying the population coding of na

…trained, and their behavior and firing activity were examined in comparison to those obser

…in anesthesia may be due to neurons population activity coding of stimuli in the cortex collapsing into the spontaneous activity subspace, therefore reflecting internally generated dynamics rather than evo

When animals move, they induce changes in the relative position of objects in their environment. These changes are perceived as visual motion signals, known as optic flow. Fast and efficient interpretation of the global structure of optic flow is thought to be critical for maintaining stability and guiding the animal's course during navigation. In flies, the lobula plate tangential cells (LPTCs), a highly interconnected network of ~60 neurons, are known to be specialised optic flow detectors. Bridging the sensory periphery with motor output structures, LPTCs are ideally positioned for course c

…we recently demonstrated that neuronal populations in the dorsomedial prefrontal cortex (dmPFC) encode both a general danger state and spec

…importance of patterns of collective activity in cognition, the influence of attention on population dynamics is not clear. Interestingly, neurons in association cortices have been reported to exhibit mixed selectivity, yet its contribution to the population code during attention is poorly understood.To investigate the influence of spatial attention on patterns of population activity, we performed extracellular recordings in the

Social interactions in primates are possible through the ability to extract relevant information from voices, for example their identity. The anterior Temporal Voice Area (aTVA) is a region in the anterior temporal lobe of humans, macaques and marmosets specialized in the processing of voices, but the exact voice information represented by individual neurons in the aTVA remains obscure.Here we asked how aTVA neurons encode voice identity information, separating different identities and grouping together calls from a same identity. We implanted two rhesus macaques with high-density multi-electr

Large-scale neuronal recordings have revealed that movement-related activity is distributed across the brain. Within motor cortices, such activity is constrained to latent dynamics on the population level. Whether other regions exhibit these motor cortical dynamics, and how this distributed activity relate to specific motor behavior remains unclear. We used multiple Neuropixels probes to simultaneously record population activity across cortical and subcortical regions during a goal-directed reaching task in mice. Task-related population activity followed dominant latent dynamics across all rec

…To investigate this, we recorded the activity of hippocampal CA1 neurons of rats using in vivo electrophysiology, as they learned a space-context association task over the course of 5 days. To assess the representation of different task-related variables such as position, reward, movement direction or context in the population activ

…Shifting from single-cell to population-level analysis, we see this phenomenon in neuroscience, with recent evidence for “neural manifolds”: low-dimensional, functionally-relevant descriptions of population activity. The most general way to discover manifolds is to find the simplest transform of activity that maximises the mutual information (MI) between the transformation and the relevant task variables, since MI is the most general meas

…by dynamically adjusting its firing activity. Here, we studied the specific contributions of two cell subclasses, putative excitatory pyramidal (68%) and putative inhibitory interneurons (24%), within a sample of 147 single units recorded from the dorsolateral PFC of two macaque monkeys while they performed a Transitive Inference (TI) task. The TI task required learning the relationship between the adjacent items of an arbitrarily rank-ordered sequence in every session, i.e., A>B>C>D&g

…Here, we propose a population perspective on memory consolidation that reproduces many features of this “representational drift”. We develop an analytically tractable model for the dynamics of memory engrams that is structurally similar to a recurren

…We investigate the population activity responsible for this surprise signal and identify neuronal subgroups

…oscillations between source and target populations of neurons are required for information propagation. 2) The information transmission via communication subspace (CS) hypothesis, recently introduced by Semedo et al., adv

Studying and understanding the code of large neural populations hinges on learning accurate models of population activity. Statistical models that are based on sparse nonlinear Random Projections (RP) of the population proved to be highly accurate, efficient, and scalable. Interestingly, this class of models has a straightforward biologically-plausible implementation as a shallow neural circuit. Here we extend the RP models, and learn them by optimizing or ``reshaping" the random projections themselves. The reshaping of projections is akin to changing synaptic connections in the neural circuit

Low dimensional neural manifolds provide a compact characterization of population activity and shared co-variability across brain areas. Nonetheless technical limitations restrict the applicability of these tools to experimental designs without explicit trial structure, and thus cannot be used to understand neural responses during naturalistic behavior. Here we propose a novel probabilistic framework that allows for interpretable partitioning of population variability within and across areas in the context of naturalistic behavior. Our approach for task aligned manifold estimation (TAME-GP) ex