Biomedical subjects

Shy Shoham

Publications and source records attributed to Shy Shoham.

8 recordsLinked to original sources

PubMed2006

How silent is the brain: is there a "dark matter" problem in neuroscience?

Evidence from a variety of recording methods suggests that many areas of the brain are far more sparsely active than commonly thought. Here, we review experimental findings pointing to the existence of neurons which fire action potentials rarely or only to very specific stimuli. Because such neurons would be difficult to detect with the most common method of monitoring neural activity in vivo-extracellular electrode recording-they could be referred to as "dark neurons," in analogy to the astrophysical observation that much of the matter in the universe is undetectable, or dark. In addition to discussing the evidence for largely silent neurons, we review technical advances that will ultimately answer the question: how silent is the brain?

Action Potentials↗

PubMed2006

Discrete stimulus estimation from neural responses in the turtle retina.

In this paper, we investigate the decoding of flashed, full-field visual stimuli while recording from a population of retinal ganglion cells. We present a direct statistical method for determining the likelihood that a response was evoked by a particular stimulus, and use this method to estimate stimuli based on microelectrode array recordings in the turtle retina. This method uses the well-known time-varying Poisson model of neural firing, along with extensions to accommodate neural refractory periods. Unlike other approaches commonly used for Poisson processes, the specific formulation presented here is bin free and requires few user-specified parameters. Statistical dependency issues and the effects of stationarity on the estimation method are also discussed.

Action Potentials↗

PubMed2005

Identification of network-level coding units for real-time representation of episodic experiences in the hippocampus.

To examine the network-level organizing principles by which the brain achieves its real-time encoding of episodic information, we have developed a 96-channel array to simultaneously record the activity patterns of as many as 260 individual neurons in the mouse hippocampus during various startling episodes. We find that the mnemonic startling episodes triggered firing changes in a set of CA1 neurons in both startle-type and environment-dependent manners. Pattern classification methods reveal that these firing changes form distinct ensemble representations in a low-dimensional encoding subspace. Application of a sliding window technique further enabled us to reliably capture not only the temporal dynamics of real-time network encoding but also postevent processing of newly formed ensemble traces. Our analyses revealed that the network-encoding power is derived from a set of functional coding units, termed neural cliques, in the CA1 network. The individual neurons within neural cliques exhibit "collective cospiking" dynamics that allow the neural clique to overcome the response variability of its members and to achieve real-time encoding robustness. Conversion of activation patterns of these coding unit assemblies into a set of real-time digital codes permits concise and universal representation and categorization of discrete behavioral episodes across different individual brains.

Animals↗

PubMed2005

Rapid neurotransmitter uncaging in spatially defined patterns.

Light-sensitive 'caged' molecules provide a means of rapidly and noninvasively manipulating biochemical signals with submicron spatial resolution. Here we describe a new optical system for rapid uncaging in arbitrary patterns to emulate complex neural activity. This system uses TeO(2) acousto-optical deflectors to steer an ultraviolet beam rapidly and can uncage at over 20,000 locations per second. The uncaging beam is projected into the focal plane of a two-photon microscope, allowing us to combine patterned uncaging with imaging and electrophysiology. By photolyzing caged neurotransmitter in brain slices we can generate precise, complex activity patterns for dendritic integration. The method can also be used to activate many presynaptic neurons at once. Patterned uncaging opens new vistas in the study of signal integration and plasticity in neuronal circuits and other biological systems.

Animals↗

PubMed2005

Statistical encoding model for a primary motor cortical brain-machine interface.

A number of studies of the motor system suggest that the majority of primary motor cortical neurons represent simple movement-related kinematic and dynamic quantities in their time-varying activity patterns. An example of such an encoding relationship is the cosine tuning of firing rate with respect to the direction of hand motion. We present a systematic development of statistical encoding models for movement-related motor neurons using multielectrode array recordings during a two-dimensional (2-D) continuous pursuit-tracking task. Our approach avoids massive averaging of responses by utilizing 2-D normalized occupancy plots, cascaded linear-nonlinear (LN) system models and a method for describing variability in discrete random systems. We found that the expected firing rate of most movement-related motor neurons is related to the kinematic values by a linear transformation, with a significant nonlinear distortion in about 1/3 of the neurons. The measured variability of the neural responses is markedly non-Poisson in many neurons and is well captured by a "normalized-Gaussian" statistical model that is defined and introduced here. The statistical model is seamlessly integrated into a nearly-optimal recursive method for decoding movement from neural responses based on a Sequential Monte Carlo filter.

Algorithms↗

PubMed2004

Superlinear population encoding of dynamic hand trajectory in primary motor cortex.

Neural activity in primary motor cortex (MI) is known to correlate with hand position and velocity. Previous descriptions of this tuning have (1) been linear in position or velocity, (2) depended only instantaneously on these signals, and/or (3) not incorporated the effects of interneuronal dependencies on firing rate. We show here that many MI cells encode a superlinear function of the full time-varying hand trajectory. Approximately 20% of MI cells carry information in the hand trajectory beyond just the position, velocity, and acceleration at a single time lag. Moreover, approximately one-third of MI cells encode the trajectory in a significantly superlinear manner; as one consequence, even small position changes can dramatically modulate the gain of the velocity tuning of MI cells, in agreement with recent psychophysical evidence. We introduce a compact nonlinear "preferred trajectory" model that predicts the complex structure of the spatiotemporal tuning functions described in previous work. Finally, observing the activity of neighboring cells in the MI network significantly increases the predictability of the firing rate of a single MI cell; however, we find interneuronal dependencies in MI to be much more locked to external kinematic parameters than those described recently in the hippocampus. Nevertheless, this neighbor activity is approximately as informative as the hand velocity, supporting the view that neural encoding in MI is best understood at a population level.

Action Potentials↗

PubMed2004

Neural substrates of tactile object recognition: an fMRI study.

A functional magnetic resonance imaging (fMRI) study was conducted during which seven subjects carried out naturalistic tactile object recognition (TOR) of real objects. Activation maps, conjunctions across subjects, were compared between tasks involving TOR of common real objects, palpation of "nonsense" objects, and rest. The tactile tasks involved similar motor and sensory stimulation, allowing higher tactile recognition processes to be isolated. Compared to nonsense object palpation, the most prominent activation evoked by TOR was in secondary somatosensory areas in the parietal operculum (SII) and insula, confirming a modality-specific path for TOR. Prominent activation was also present in medial and lateral secondary motor cortices, but not in primary motor areas, supporting the high level of sensory and motor integration characteristic of object recognition in the tactile modality. Activation in a lateral occipitotemporal area associated previously with visual object recognition may support cross-modal collateral activation. Finally, activation in medial temporal and prefrontal areas may reflect a common final pathway of modality-independent object recognition. This study suggests that TOR involves a complex network including parietal and insular somatosensory association cortices, as well as occipitotemporal visual areas, prefrontal, and medial temporal supramodal areas, and medial and lateral secondary motor cortices. It confirms the involvement of somatosensory association areas in the recognition component of TOR, and the existence of a ventrolateral somatosensory pathway for TOR in intact subjects. It challenges the results of previous studies that emphasize the role of visual cortex rather than somatosensory association cortices in higher-level somatosensory cognition.

Adult↗

PubMed2003

Robust, automatic spike sorting using mixtures of multivariate t-distributions.

A number of recent methods developed for automatic classification of multiunit neural activity rely on a Gaussian model of the variability of individual waveforms and the statistical methods of Gaussian mixture decomposition. Recent evidence has shown that the Gaussian model does not accurately capture the multivariate statistics of the waveform samples' distribution. We present further data demonstrating non-Gaussian statistics, and show that the multivariate t-distribution, a wide-tailed family of distributions, provides a significantly better fit to the true statistics. We introduce an adaptation of a new expectation-maximization based competitive mixture decomposition algorithm and show that it efficiently and reliably performs mixture decomposition of t-distributions. Our algorithm determines the number of units in multiunit neural recordings, even in the presence of significant noise contamination resulting from random threshold crossings and overlapping spikes.

Action Potentials↗