Sensory inputs

In a first part of the talk, I will present Predictive Coding Light (PCL), a novel unsupervised learning architecture for spiking neural networks. In contrast to conventional predictive coding approaches, which only transmit prediction errors to higher processing stages, PCL learns inhibitory lateral and top-down connectivity to suppress the most predictable spikes and passes a compressed representation of the input to higher processing stages. We show that PCL reproduces a range of biological findings and exhibits a favorable tradeoff between energy consumption and downstream classification p

…reflecting a combination of sensory inputs, existing knowledge, and contextually modulated expectations. We hypothesize that this integration is realized in the temporal dynamics of perceptual population responses. Here, we use latent-space modeling and dynamical systems analysis to characterize activity across populations of simultaneously recorded neurons in a secondary auditory forebrain region of songbirds (European starlings), engaged in an expectation-cued song-syllable categorization task. We

Uncovering how a stream of sensory inputs maps onto a sequence of actions is crucial for understanding the nervous system. However, behavior is shaped not only by recent sensory stimuli but also by longer-term experiences, internal neuronal dynamics, and physiological states. As a result, the same experimental conditions can result in variable behaviors. We study the role of internal states in zebrafish larvae behavior, leveraging a rich dataset of three million movements recorded during spontaneous behavior, photomotor responses, optomotor responses, and visually driven escape in the zebraf

Predictions about sensory inputs (e.g. contextual signals from associative cortical areas to sensory areas) arrive primarily in the superficial layers of the cortical sheet, where they are received by the distal apical dendrites of pyramidal neurons. These distal apical dendrites are long tree-like extensions which are electro- tonically segregated from the cell body under passive propagation of membrane potential. At the same time, incoming sensory inputs primarily target the region closest to the cell body. The relative coupling of activity between these neuronal regions (i.e. simultaneous v

Animals rely on sensory inputs to navigate complex, dynamic environments. Understanding how the brain integrates these inputs to guide behavior remains a central question in neuroscience. Training AI and animals on the same task offers a unique opportunity to uncover both similarities and critical differences in the strategies used by biological and artificial systems. By pinpointing what the brain accomplishes that modern AI lacks, we can drive advancements in machine learning, pushing AI to more robust, generalizable solutions. In this study, we train mice and reinforcement learning agents o

…Behavior selection is influenced by sensory inputs, internal states, and memory, suggesting that neural mechanisms across the brain coordinate these decisions. While investigating the neural basis of behavioral switching requires whole-brain imaging, current methods face challenges in behaving animals. We employed functional ultrasound imaging (fUS) to record large-scale neural activity in head-fixed mice while simultaneously tracking their behavior. Our aim was to identify brain regions that predict trans

…neurons tile the range of continuous sensory inputs and intrinsic variables with overlapping responses. It is characteristic for the underlying recurrent connectivity in the cortex that neurons with similar tuning predominantly excite each other. The reason for such an architecture is not clear. Using an analytically tractable model, we show that it can naturally arise from a cooperative coding scheme. In this scheme neurons with similar responses specifically support each other by sharing their computations to obtain the desired

…brain to interpret a diverse set of sensory inputs and can allow it to operate and respond flexibly across a wide array of contexts. Hence it is important to study factors that can enable network complexity

Memories, thoughts, or higher sensory categories are believed to be characterized by distinct neural patterns within their respective brain neural networks. These patterns, acting as discrete attractor states, can accommodate subtle differences in input information. This ability allows for the completion of a range of similar input patterns into already stored activity states or memory engrams, facilitating their categorization or activation of the appropriate memory trace.Here we present set of experiments that question kinetics of fast changes in hippocampal spatial maps upon manipulating se

…shape significantly their response to sensory inputs. In this talk, I will describe three recent collaborative projects that shed light on these issues. (1) I will show how optogenetic excitation of inhibition neurons is consistent with cortex being inhibition stabilized even in the absence of sensory inputs, and how this data can constrain the coupling strengths of E-I cortical network models. (2) Recent analysis of the effects of optogenetic excitation of pyramidal cells in V1 of mice and mon

As we interact with the world around us, we experience a constant stream of sensory inputs, and must generate a constant stream of behavioral actions. What makes brains more than simple input-output machines is their capacity to integrate sensory inputs with an animal’s own internal motivational state to produce behavior that is flexible and adaptive. Working with neural recordings from subcortical structures involved in regulation of survival behaviors, we show how the dynamical properties of neural populations give rise to motivational states that change animal behavior on a timescale of min

…First, during natural vision, the inputs to cortical neurons are not isolate

The brain learns an internal model of the world by making predictions about upcoming inputs and comparing its predictions with actual incoming sensory information. Prediction errors are essential for learning an accurate internal representation of the world because they indicate where the internal representation needs to be improved to make a better prediction in the future. Promisingly, a neural correlate for prediction errors has been found in the activity of pyramidal neurons in layer 2/3 of diverse cortical areas. To make contextually appropriate predictions in a stochastic environment, th

…information encoded by the peripheral sensory organs whose function is affected by typical, developmental changes across the lifespan. Yet, how the brain adapts to the maturation of the senses, as well as experiential changes in the multisensory environment, is poorly understood. Over the past f

…relevant information is extracted from sensory inputs, how information is stored in memory circuits, and how sensory inputs inform behavior. A series of recent findings provides evidence that inhibition has not only homeostatic functions in neuronal circuits but makes highly specific, instructive contributions to behaviorally relevant computations in different brain regions. These observations imply that the connectivity among excitatory and inhibitory neurons exhibits essential higher-order structure that

…so as to optimize the processing of sensory inputs expected in the current environment. While there is accumulating evidence that the brain indeed operates under this principle, most of the attention has been placed on mechanisms that rely on static coding properties of neurons. The potential contribution of dynamical features, such as those reflected in the evolution of neural population dynamics, has thus far been overlooked. In this talk, I will present evidence for a novel mechanism for predictive proce