Topic: population codes

The current dogma in neuroscience is that neurons primarily convey stimulus information through their firing rate. However, recent studies suggest a remarkable speed of sensory processing which may be incompatible with traditional rate-coding schemes, and it has been hypothesized that sensory encoding may rely on the spike timing relationships among neurons (Thorpe et al., 2001 and Resulaj et al., 2018). To study multi-neuron spiking patterns, we developed SpikeShip, an unsupervised, linear, geometry-based dissimilarity measure that aligns spikes across pairs of epochs based on optimal transpo

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

Primate amygdala neuron process the value of visual stimuli to guide decision-making and social interactions (Grabenhorst et al., 2019; Grabenhorst et al., 2023). However, it remains unclear whether different amygdala neurons are sensitive to distinct value components, such as reward probability and magnitude, and whether they integrate these components to decision variables, including risk and expected value. To address these questions, we recorded activity of amygdala neurons while monkeys viewed sequentially presented stimuli that provided separate information about the probability, magnitu