Blindfold learning of an accurate neural metric

The brain has no direct access to physical stimuli, but only to the spiking activity evoked in sensory organs. It is unclear how the brain can structure its representation of the world based on differences between those noisy, correlated responses alone. Here we show how to build a distance map of responses from the structure of the population activity of retinal ganglion cells, allowing for the accurate discrimination of distinct visual stimuli from the retinal response. We introduce the Temporal Restricted Boltzmann Machine to learn the spatiotemporal structure of the population activity, and use this model to define a distance between spike trains. We show that this metric outperforms existing neural distances at discriminating pairs of stimuli that are barely distinguishable. The proposed method provides a generic and biologically plausible way to learn to associate similar stimuli based on their spiking responses, without any other knowledge of these stimuli

A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data

Deducing the structure of neural circuits is one of the central problems of modern neuroscience. Recently-introduced calcium fluorescent imaging methods permit experimentalists to observe network activity in large populations of neurons, but these techniques provide only indirect observations of neural spike trains, with limited time resolution and signal quality. In this work we present a Bayesian approach for inferring neural circuitry given this type of imaging data. We model the network activity in terms of a collection of coupled hidden Markov chains, with each chain corresponding to a single neuron in the network and the coupling between the chains reflecting the network's connectivity matrix. We derive a Monte Carlo Expectation--Maximization algorithm for fitting the model parameters; to obtain the sufficient statistics in a computationally-efficient manner, we introduce a specialized blockwise-Gibbs algorithm for sampling from the joint activity of all observed neurons given the observed fluorescence data. We perform large-scale simulations of randomly connected neuronal networks with biophysically realistic parameters and find that the proposed methods can accurately infer the connectivity in these networks given reasonable experimental and computational constraints. In addition, the estimation accuracy may be improved significantly by incorporating prior knowledge about the sparseness of connectivity in the network, via standard L$_1$ penalization methods.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS303 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

High accuracy decoding of dynamical motion from a large retinal population

Motion tracking is a challenge the visual system has to solve by reading out the retinal population. Here we recorded a large population of ganglion cells in a dense patch of salamander and guinea pig retinas while displaying a bar moving diffusively. We show that the bar position can be reconstructed from retinal activity with a precision in the hyperacuity regime using a linear decoder acting on 100+ cells. The classical view would have suggested that the firing rates of the cells form a moving hill of activity tracking the bar's position. Instead, we found that ganglion cells fired sparsely over an area much larger than predicted by their receptive fields, so that the neural image did not track the bar. This highly redundant organization allows for diverse collections of ganglion cells to represent high-accuracy motion information in a form easily read out by downstream neural circuits.Comment: 23 pages, 7 figure

A Novel Framework of Online, Task-Independent Cognitive State Transition Detection and Its Applications

Complex reach, grasp, and object manipulation tasks require sequential, temporal coordination of a movement plan by neurons in the brain. Detecting cognitive state transitions associated with motor tasks from sequential neural data is pivotal in rehabilitation engineering. The cognitive state detectors proposed thus far rely on task-dependent (TD) models, i.e., the detection strategy exploits a priori knowledge of the movement goals to determine the actual states, regardless of whether these cognitive states actually depend on the movement tasks or not. This approach, however, is not viable when the tasks are not known a priori (e.g., the subject performs many different tasks) or when there is a paucity of neural data for each task. Moreover, some cognitive states (e.g., holding) are invariant to the tasks performs. I first develop an offline, task-dependent cognitive state transition detector and a kinematics decoder to show the feasibility of distinguishing between cognitive states based on their inherent features extracted via a hidden Markov model (HMM) based detection framework. The proposed framework is designed to decode both cognitive states and kinematics from ensemble neural activity. The proposed decoding framework is able to a) automatically differentiate between baseline, plan, and movement, and b) determine novel holding epochs of neural activity and also estimate the epoch-dependent kinematics. Specifically, the framework is mainly composed of a hidden Markov model (HMM) state decoder and a switching linear system (S-LDS) kinematics decoder. I take a supervised approach and use a generative framework of neural activity and kinematics. I demonstrate the decoding framework using neural recordings from ventral premotor (PMv) and dorsal premotor (PMd) neurons of a non-human primate executing four complex reach-to-grasp tasks along with the corresponding kinematics recording. Using the HMM state decoder, I demonstrate that the transitions between neighboring epochs of neural activity, regardless of the existence of any external kinematics changes, can be detected with high accuracy (>85%) and short latencies (<150 ms). I further show that the joint angle kinematics can be estimated reliably with high accuracy (mean = 88%) using a S-LDS kinematics decoder. In addition, I demonstrate that the use of multiple latent state variables to model the within-epoch neural activity variability can improve the decoder performance. This unified decoding framework combining a HMM state decoder and a S-LDS may be useful in neural decoding of cognitive states and complex movements of prosthetic limbs in practical brain-computer interface implementations. I then develop a real-time (online) task-independent (TI) framework to detect cognitive state transitions from spike trains and kinematic measurements. I applied this framework to 226 single-unit recordings collected via multi-electrode arrays in the premotor dorsal and ventral (PMd and PMv) regions of the cortex of two non-human primates performing 3D multi-object reach-to-grasp tasks, and I used the detection latency and accuracy of state transitions to measure the performance. I found that, in both online and offline detection modes, (i) TI models have significantly better performance than TD models when using neuronal data alone, however (ii) during movements, the addition of the kinematics history to the TI models further improves detection performance. These findings suggest that TI models may be able to more accurately detect cognitive state transitions than TD under certain circumstances. The proposed framework could pave the way for a TI control of prosthesis from cortical neurons, a beneficial outcome when the choice of tasks is vast, but despite that the basic movement cognitive states need to be decoded. Based on the online cognitive state transition detector, I further construct an online task-independent kinematics decoder. I constructed our framework using single-unit recordings from 452 neurons and synchronized kinematics recordings from two non-human primates performing 3D multi-object reach-to-grasp tasks. I find that (i) the proposed TI framework performs significantly better than current frameworks that rely on TD models (p = 0.03); and (ii) modeling cognitive state information further improves decoding performance. These findings suggest that TI models with cognitive-state-dependent parameters may more accurately decode kinematics and could pave the way for more clinically viable neural prosthetics

Timescales of Multineuronal Activity Patterns Reflect Temporal Structure of Visual Stimuli

The investigation of distributed coding across multiple neurons in the cortex remains to this date a challenge. Our current understanding of collective encoding of information and the relevant timescales is still limited. Most results are restricted to disparate timescales, focused on either very fast, e.g., spike-synchrony, or slow timescales, e.g., firing rate. Here, we investigated systematically multineuronal activity patterns evolving on different timescales, spanning the whole range from spike-synchrony to mean firing rate. Using multi-electrode recordings from cat visual cortex, we show that cortical responses can be described as trajectories in a high-dimensional pattern space. Patterns evolve on a continuum of coexisting timescales that strongly relate to the temporal properties of stimuli. Timescales consistent with the time constants of neuronal membranes and fast synaptic transmission (5–20 ms) play a particularly salient role in encoding a large amount of stimulus-related information. Thus, to faithfully encode the properties of visual stimuli the brain engages multiple neurons into activity patterns evolving on multiple timescales

Modeling and Analysis of Neural Spike Trains

open

Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States

UP and DOWN states, the periodic fluctuations between increased and decreased spiking activity of a neuronal population, are a fundamental feature of cortical circuits. Understanding UP-DOWN state dynamics is important for understanding how these circuits represent and transmit information in the brain. To date, limited work has been done on characterizing the stochastic properties of UP-DOWN state dynamics. We present a set of Markov and semi-Markov discrete- and continuous-time probability models for estimating UP and DOWN states from multiunit neural spiking activity. We model multiunit neural spiking activity as a stochastic point process, modulated by the hidden (UP and DOWN) states and the ensemble spiking history. We estimate jointly the hidden states and the model parameters by maximum likelihood using an expectation-maximization (EM) algorithm and a Monte Carlo EM algorithm that uses reversible-jump Markov chain Monte Carlo sampling in the E-step. We apply our models and algorithms in the analysis of both simulated multiunit spiking activity and actual multi- unit spiking activity recorded from primary somatosensory cortex in a behaving rat during slow-wave sleep. Our approach provides a statistical characterization of UP-DOWN state dynamics that can serve as a basis for verifying and refining mechanistic descriptions of this process.National Institutes of Health (U.S.) (Grant R01-DA015644)National Institutes of Health (U.S.) (Director Pioneer Award DP1- OD003646)National Institutes of Health (U.S.) (NIH/NHLBI grant R01-HL084502)National Institutes of Health (U.S.) (NIH institutional NRSA grant T32 HL07901

Platonic model of mind as an approximation to neurodynamics

Hierarchy of approximations involved in simplification of microscopic theories, from sub-cellural to the whole brain level, is presented. A new approximation to neural dynamics is described, leading to a Platonic-like model of mind based on psychological spaces. Objects and events in these spaces correspond to quasi-stable states of brain dynamics and may be interpreted from psychological point of view. Platonic model bridges the gap between neurosciences and psychological sciences. Static and dynamic versions of this model are outlined and Feature Space Mapping, a neurofuzzy realization of the static version of Platonic model, described. Categorization experiments with human subjects are analyzed from the neurodynamical and Platonic model points of view