Spike trains

The goal of the Neural Dynamics accelerator is to understand how the brain generates flexible behavior. We aim to uncover the algorithms—implemented by dynamics in brain-wide neural circuits—that allow animals to build internal models, process information, and choose actions. We are searching for a Scientist to develop data-driven theories of how circuit structure shapes neural computation. The Scientist will use the Institute's large-scale connectivity atlases and brain-wide recordings to understand how the brain's wiring organizes neural dynamics and drives flexible behavior. The position o

…as regards the structure of their spike trains, which might provide clues to cortical circuit function. To address this question, we used Neuropixels electrodes to record layer 5 excitatory populations in mouse V1, then transcriptomically identified the recorded cell types. To do so, we performed a subsequent recording of the same cells using 2-photon (2p) calcium imaging, identifying neurons b

…characterization technique called spike-triggered descent (STD) which optimally recovers n-order kernels for the very general cumulative spike response model (CSRM). This works by simulating a model neuron that learns to reproduce the observed spike train. Learning is achieved via parameter optimization that relies on a metric induced on the space of spike trains modeled as a novel inner product space. This technique can precisely learn kernels on extremely

The task of sorting spikes from extracellular recordings is pivotal for analyzing meaningful spike trains associated with behavioral responses, stimulation effects, and various other phenomena. This process, however, is significantly challenged by the presence of overlapping spikes, which result from neurons firing either simultaneously or within very short intervals. Such overlapping spikes are frequently disregarded in numerous studies due to the complexities in discerning individual neuronal activities and the lack of a definitive ground truth. In this research, we introduce a novel approac

…Towards this end, we collected spike-trains by simulating network mod

…significant ms-precise spatio-temporal spike patterns (STPs). For this purpose, the parallel spike trains were analyzed for STPs by the SPADE method (Stella et al, 2019, Biosystems), which detects, counts and evaluates spike patterns for their significance by the use of surrogates (Stella et al, 2022 eNeuro). As a r

…the power spectral density (PSD) of spike trains. Unfortunately, spike train spectra exhibit a global distortion generated by the neuronal recovery period (“RP”, the post-spike drop in spike probability, which can extend beyond the refractory period). This distortion can increase the rate of false negatives and false positives from s

Intelligent behavior depends on the brain’s ability to anticipate future events. Experimental evidence shows reliable temporal sequences in the neural activity, suggesting a functional role of sequences for the association and subsequent anticipation of events in time. However, how neurons can predict temporal patterns and fire ahead of sensory inputs remains largely unknown. We propose a plasticity rule based on predictive processing, where neurons learn a low-rank model of the synaptic input dynamics in their membrane potential. Neurons thereby amplify those synapses that maximally predict o

Spike trains recorded from the cortex of behaving animals can be complex, highly variable from trial to trial, and therefore challenging to interpret. A fraction of cells exhibit obvious trial-averaged, task-related responses such as pure tone frequency tuning in auditory cortex. However, a substantial number of cells do not appear to fire in a task-related manner and are often neglected from analysis. Previous work used a novel single-trial, spike-timing-based analysis to show that both classically and non-classically responsive cortical neurons contain significant information about sensory s

All sensory information – like what we see, hear and smell – gets encoded in spike trains by sensory neurons and gets sent to the brain. Due to the complexity of neural circuits and the difficulty of quantifying complex animal behavior, it has been exceedingly hard to resolve how the brain decodes these spike trains to drive behavior. We now measure quantal signals originating from sparse photons through the most sensitive neural circuits of the mammalian retina and correlate the retinal output spike trains with precisely quantified behavioral decisions. We utilize a combination of electrophys