An algorithm for the analysis of temporally structured multidimensional measurements.

Analysis of multichannel recordings acquired with contemporary imaging or electrophysiological methods in neuroscience is often difficult due to the high dimensionality of the data and the low signal-to-noise ratio. We developed a method that addresses both problems by utilizing prior information about the temporal structure of the signal and the noise. This information is expressed mathematically in terms of sets of correlation matrices, a versatile approach that allows the treatment of a large class of signal and noise sources, including non-stationary sources or correlated signal and noise sources. We present a mathematical analysis of the algorithm, as well as application to an artificial dataset, and show that the algorithm is tolerant to inaccurate assumptions about the temporal structure of the data. We suggest that the algorithm, which we name temporally structured component analysis, can be highly beneficial to various multichannel measurement techniques, such as fMRI or optical imaging.

Mapping dynamic memories of gradually changing objects.

Our brain is able to maintain a continuously updated memory representation of objects despite changes in their appearance over time (aging faces or objects, growing trees, etc.). Although this ability is crucial for cognition and behavior, it was barely explored. Here, we investigate this memory characteristic using a protocol emulating face transformation. Observers were presented with a sequence of faces that gradually transformed over many days, from a known face (source) to a new face (target), in presentations separated by other stimuli. This practice resulted in a drastic change in the memory and recognition of the faces. Although identification of the source and older face instances was reduced, recent face instances were increasingly identified as the source and rated as highly similar to the memory of the source. Using an object perturbation method, we estimated the corresponding memory shift, showing that memory patterns shifted from the source neighborhood toward the target. Our findings suggest that memory is updated to account for object changes over time while still keeping associations with past appearances. These experimental results are broadly compatible with a recently developed model of associative memory that assumes attractor dynamics with a learning rule facilitated by novelty, shown to hold when objects change gradually over short timescales.

Dynamics of memory representations in networks with novelty-facilitated synaptic plasticity.

The ability to associate some stimuli while differentiating between others is an essential characteristic of biological memory. Theoretical models identify memories as attractors of neural network activity, with learning based on Hebb-like synaptic modifications. Our analysis shows that when network inputs are correlated, this mechanism results in overassociations, even up to several memories "merging" into one. To counteract this tendency, we introduce a learning mechanism that involves novelty-facilitated modifications, accentuating synaptic changes proportionally to the difference between network input and stored memories. This mechanism introduces a dependency of synaptic modifications on previously acquired memories, enabling a wide spectrum of memory associations, ranging from absolute discrimination to complete merging. The model predicts that memory representations should be sensitive to learning order, consistent with recent psychophysical studies of face recognition and electrophysiological experiments on hippocampal place cells. The proposed mechanism is compatible with a recent biological model of novelty-facilitated learning in hippocampal circuitry.

Neural network model of the primary visual cortex: from functional architecture to lateral connectivity and back.

The role of intrinsic cortical dynamics is a debatable issue. A recent optical imaging study (Kenet et al., 2003) found that activity patterns similar to orientation maps (OMs), emerge in the primary visual cortex (V1) even in the absence of sensory input, suggesting an intrinsic mechanism of OM activation. To better understand these results and shed light on the intrinsic V1 processing, we suggest a neural network model in which OMs are encoded by the intrinsic lateral connections. The proposed connectivity pattern depends on the preferred orientation and, unlike previous models, on the degree of orientation selectivity of the interconnected neurons. We prove that the network has a ring attractor composed of an approximated version of the OMs. Consequently, OMs emerge spontaneously when the network is presented with an unstructured noisy input. Simulations show that the model can be applied to experimental data and generate realistic OMs. We study a variation of the model with spatially restricted connections, and show that it gives rise to states composed of several OMs. We hypothesize that these states can represent local properties of the visual scene.

Responses of trigeminal ganglion neurons to the radial distance of contact during active vibrissal touch.

Rats explore their environment by actively moving their whiskers. Recently, we described how object location in the horizontal (front-back) axis is encoded by first-order neurons in the trigeminal ganglion (TG) by spike timing. Here we show how TG neurons encode object location along the radial coordinate, i.e., from the snout outward. Using extracellular recordings from urethane-anesthetized rats and electrically induced whisking, we found that TG neurons encode radial distance primarily by the number of spikes fired. When an object was positioned closer to the whisker root, all touch-selective neurons recorded fired more spikes. Some of these cells responded exclusively to objects located near the base of whiskers, signaling proximal touch by an identity (labeled-line) code. A number of tonic touch-selective neurons also decreased delays from touch to the first spike and decreased interspike intervals for closer object positions. Information theory analysis revealed that near-certainty discrimination between two objects separated by 30% of the length of whiskers was possible for some single cells. However, encoding reliability was usually lower as a result of large trial-by-trial response variability. Our current findings, together with the identity coding suggested by anatomy for the vertical dimension and the temporal coding of the horizontal dimension, suggest that object location is encoded by separate neuronal variables along the three spatial dimensions: temporal for the horizontal, spatial for the vertical, and spike rate for the radial dimension.

Correlation maps allow neuronal electrical properties to be predicted from single-cell gene expression profiles in rat neocortex.

The computational power of the neocortex arises from interactions of multiple neurons, which display a wide range of electrical properties. The gene expression profiles underlying this phenotypic diversity are unknown. To explore this relationship, we combined whole-cell electrical recordings with single-cell multiplex RT-PCR of rat (p13-16) neocortical neurons to obtain cDNA libraries of 26 ion channels (including voltage activated potassium channels, Kv1.1/2/4/6, Kvbeta1/2, Kv2.1/2, Kv3.1/2/3/4, Kv4.2/3; sodium/potassium permeable hyperpolarization activated channels, HCN1/2/3/4; the calcium activated potassium channel, SK2; voltage activated calcium channels, Caalpha1A/B/G/I, Cabeta1/3/4), three calcium binding proteins (calbindin, parvalbumin and calretinin) and GAPDH. We found a previously unreported clustering of ion channel genes around the three calcium-binding proteins. We further determined that cells similar in their expression patterns were also similar in their electrical properties. Subsequent regression modeling with statistical resampling yielded a set of coefficients that reliably predicted electrical properties from the expression profile of individual neurons. This is the first report of a consistent relationship between the co-expression of a large profile of ion channel and calcium binding protein genes and the electrical phenotype of individual neocortical neurons.

Glucocorticoid involvement in suppression of NK activity following surgery in rats.

We studied plasma factors mediating suppression of NK activity (NKA) following surgery. Plasma from operated rats suppressed NKA of splenocytes, leukocytes, and purified natural killer (NK) cells, and charcoal stripping nullified suppression. The glucocorticoid antagonist mifepristone prevented suppression, whereas blockers of reactive oxygen metabolites, opioids, catecholamines, prostaglandin-E2, and histamine did not. NKA dropped as corticosterone levels peaked postoperatively, and administration of relevant doses of corticosterone suppressed NKA. Inhibition of glucocorticoid synthesis prevented plasma from suppressing NKA but merely attenuated NKA suppression in operated rats. Thus, postoperative concentrations of corticosterone can directly suppress NKA but additional factors probably act in vivo.