Neural recordings

Build scalable computational pipelines for biophysical models of neurons and neural populations. Apply machine learning and statistical inference to neural recordings, construct biologically realistic networks and help shape research directions at an early-stage company working at the intersection of neuroscience and computation. On-site engagement is preferred and international candidates may apply. The advertised salary range is USD 132,000–259,000; no fixed closing date is stated.

Chris Rozell discusses combining deep-brain stimulation, neural recordings and machine learning to study treatment-resistant depression. The conversation examines biomarkers of recovery, open- and closed-loop interventions, prediction of treatment responses and explainable models, while addressing the obstacles and interdisciplinary work involved in precision neuroengineering.

Neural dynamics represent the hard-to-interpret substrate of circuit computations. Advances in large-scale recordings have highlighted the sheer spatiotemporal complexity of circuit dynamics within and across circuits, portraying in detail the difficulty of interpreting such dynamics and relating it to computation. Indeed, even in extremely simplified experimental conditions, one observes high-dimensional temporal dynamics in the relevant circuits. This complexity can be potentially addressed by the notion that not all changes in population activity have equal meaning, i.e., a small change in

…to link connectivity features of large neural networks to their emergent dynamics. Nevertheless, how to integrate connectomics data and neural recordings to determine the function of a neural system remains challenging, given the physiological properties of individual neurons that remain unconstrained. To address this, we developed a theory that describes the space of solutions when inferring activit

Understanding the mechanisms of statistical learning, where organisms infer structure from sensory observations without explicit instruction, is crucial in comprehending how the brain processes and responds to complex and dynamic environments. This work explores the innate capabilities of mice to discern environmental regularities, focusing on the role of the hippocampus in this unsupervised learning process. The study leverages a novel comparative behavioural paradigm, termed X-detection, to manipulate the statistics of embedded tone sequences (patterns) in an auditory cover task: subjects s

We present a comprehensive dataset of neural activity, compiled from 11 neuroimaging experiments in the nematode worm C. elegans. These source datasets, while collected under different experimental protocols, all measure neural activity via changes in calcium fluorescence in labeled subsets of the hermaphrodite worm’s 300 neurons. Our contribution is to standardize these disparate datasets into a unified framework including ordering the neural data from labeled neurons consistently, resampling traces to a common timestep, and including a feature mask to indicate which neurons were labeled in e

…variance in sub-cortical and cortical neural activity.Here, we used large-scale neural recordings from measurement sites spanning the inferior colliculus (IC) of normal-hearing gerbils to develop a deep learning model of

Electrophysiological experiments include metadata, behavioral data, and neural recordings, requiring precise methods to synchronize the different sources. In addition, analysis often involves complex mathematical functions that cannot be applied by non-experts. The massive amount of data generated by long recordings and hundreds of electrode sites adds to the complexity and highlights the need for a workflow that enables standardized and large-scale data analysis.To address these challenges, we developed a Python-based workflow with a graphical user interface using PyQt5. It uses a modular fra

Carina Curto explains applications of topology to neural recordings and discusses mathematical models of recurrent networks.

…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

…Previous studies have demonstrated neural correlates of head and eye movements in rodent primary visual cortex (V1), but the sources and computational roles of these signals are unclear. We addressed this by combining measurement of head and eye movements with high density neural recordings in freely moving mice. V1 neurons responded primarily to gaze shifts, where head movements are accompanied by saccadic eye movements, rather than to head movements where compensatory eye movements stabilize gaze. A variety o

…The neural circuits that govern this sophisticated skill include the orbitofrontal cortex, hippocampus, amygdala and other regions. Whether these regions merely store representations of latent states or actively construct them remains to be described. Further, if they are constructed, the mechanism for forming them is unknown. Here, we report on monkeys playing a simplified version of the game battleship, designed to investigate learnin

Neural recordings are often high-dimensional while latent dynamics related to behavior are low-dimensional. Similarly, ethological behaviors are described by large numbers of dynamic features and it is important to know both the dimensionality and composition of the neurally relevant behavioral subspace. However, methods for data-driven identification of behavioral subspaces relevant for neural recordings is nascent. Here, we introduce external Dynamics Components Analysis (eDCA), a linear dimensionality reduction method that finds subspaces of past neural data that have maximal mutual informa

The cost of experimental neural recordings strongly limits the stimuli or conditions one can present. Ideally, we would therefore like to know how neural responses observed in specific experiments generalize beyond them. E.g., how does the brain’s response to isolated sounds presented in the lab compare to its response to those sounds within natural language in complex social settings? We addressed this problem in fly courtship–a complex, acoustically mediated social behavior. Starting with recorded responses from diverse female neurons to isolated elements of male courtship song, we found the

…have been proposed to capture the rich neural and behavioral phenomenology associated with these regions, it remains difficult to compare these theories systematically against each other using the full range of empirical data. To address this gap, we built an open-source standardised software framework, named NeuralPlayground, to facilitate comparisons of hippocampus and entorhinal cortex models. This Python software package offers a repr

…is to decode behavior from recorded neural signals. In order to study the neural correlates of learning a task, we would like to decode behavior across the entire timespan of learning, which can take multiple recording sessions across many days. However, decoding across sessions is hindered due to a high amount of session-to-session variability in neural recordings. Here, we propose utilizing key time points in the trials with high behavioral correlations across sessions, as well as a novel bootstrapping method and region-based conv

…talk, the timeseries data consist of neural recordings taken from the auditory cortex of human subjects who listened to sequences of repeated triplets of tones and reported their perception by pressing a button. Subjects reported spontaneous alternations between two auditory perceptual states (1-stream and 2-streams). We discuss a data-driven method, which leverages time-delayed coordinates, diffusion maps, and dynamic mode decomposition, to identify neural features that correlated with subjec

…from behavioral measurements and neural responses. We applied this method to an evidence accumulation task studied in rats. Our method models trial-to-trial state switches with a hidden Markov model (HMM)

Large scale neural recordings are typically found to embed lower-dimensional structure that reflects behaviour. Empirically, the best models of such low-dimensional structure -- by several statistical measures -- tend to be those that describe neural recordings via a latent dynamical system. Importantly, as recordings are typically made in one or a subset of areas within the brain, the dynamics that best capture the data cannot in general be expected to be fully autonomous, but may instead be driven by unobserved inputs. Learning the parameters of a probabilistic dynamical system whilst simult

…How neural processes enable this capacity to extrapolate to novel stimuli is a fundamental open question. A prominent but underexplored hypothesis suggests that generalization is facilitated by a low-dimensional organization of collective neural activity, yet evidence for the underlying neural mechanisms remains wanting. Combining

…of consciousness to the complexity of neural activity, i.e., using the information contained in a stream of recorded signals they can tell whether the subject might be awake, asleep, or anaesthetised. Usually, the signals stemming from a complex system are correlated in time; the behaviour of the future depends on the pa

…(BCIs), which bypass damaged neural pathways to control paralyzed or prosthetic limbs. Here, we demonstrate the BCI control of a prosthetic hand using intracortical recordings from the posterior parietal cortex (PPC). As part of an ongoing clinical trial, two participants with cervical spinal cord injury were each implanted with a 96-channel array in the left PPC. Across four sessions each, we recorded neural activity while they attempted to pr

…Using neural recordings in combination with behavioral state monitoring, we find that arousal and motor activity have complementary roles in regulating local cortical operations, providing dynamic control of sensory encoding. These changes in encoding are linked to altered performance on perceptual tasks. Neuromodulators, such as acetylcholine, may regulate this state-dependent flexibility of cortical network function. We

…Using neural recordings in combination with behavioral state monitoring, we find that arousal and motor activity have complementary roles in regulating local cortical operations, providing dynamic control of sensory encoding. These changes in encoding are linked to altered performance on perceptual tasks. Neuromodulators, such as acetylcholine, may regulate this state-dependent flexibility of cortical network function. We the