Biomedical subjects

M J Korenberg

Publications and source records attributed to M J Korenberg.

At least 19 recordsLinked to original sources

PubMed2001

Parallel cascade identification and its application to protein family prediction.

Parallel cascade identification is a method for modeling dynamic systems with possibly high order nonlinearities and lengthy memory, given only input/output data for the system gathered in an experiment. While the method was originally proposed for nonlinear system identification, two recent papers have illustrated its utility for protein family prediction. One strength of this approach is the capability of training effective parallel cascade classifiers from very little training data. Indeed, when the amount of training exemplars is limited, and when distinctions between a small number of categories suffice, parallel cascade identification can outperform some state-of-the-art techniques. Moreover, the unusual approach taken by this method enables it to be effectively combined with other techniques to significantly improve accuracy. In this paper, parallel cascade identification is first reviewed, and its use in a variety of different fields is surveyed. Then protein family prediction via this method is considered in detail, and some particularly useful applications are pointed out.

Computational Biology↗

PubMed2000

Iterative fast orthogonal search algorithm for MDL-based training of generalized single-layer networks.

The generalized single-layer network (GSLN) architecture, which implements a sum of arbitrary basis functions defined on its inputs, is potentially a flexible and efficient structure for approximating arbitrary nonlinear functions. A drawback of GSLNs is that a large number of weights and basis functions may be required to provide satisfactory approximations. In this paper, we present a new approach in which an algorithm known as iterative fast orthogonal search (IFOS) is coupled with the minimum description length (MDL) criterion to provide automatic structure selection and parameter estimation for GSLNs. The resulting algorithm, dubbed IFOS-MDL, performs both network growth and pruning to construct sparse GSLNs from potentially large spaces of candidate basis functions.

Algorithms↗

PubMed2000

Automatic classification of protein sequences into structure/function groups via parallel cascade identification: a feasibility study.

A recent paper introduced the approach of using nonlinear system identification as a means for automatically classifying protein sequences into their structure/function families. The particular technique utilized, known as parallel cascade identification (PCI), could train classifiers on a very limited set of exemplars from the protein families to be distinguished and still achieve impressively good two-way classifications. For the nonlinear system classifiers to have numerical inputs, each amino acid in the protein was mapped into a corresponding hydrophobicity value, and the resulting hydrophobicity profile was used in place of the primary amino acid sequence. While the ensuing classification accuracy was gratifying, the use of (Rose scale) hydrophobicity values had some disadvantages. These included representing multiple amino acids by the same value, weighting some amino acids more heavily than others, and covering a narrow numerical range, resulting in a poor input for system identification. This paper introduces binary and multilevel sequence codes to represent amino acids, for use in protein classification. The new binary and multilevel sequences, which are still able to encode information such as hydrophobicity, polarity, and charge, avoid the above disadvantages and increase classification accuracy. Indeed, over a much larger test set than in the original study, parallel cascade models using numerical profiles constructed with the new codes achieved slightly higher two-way classification rates than did hidden Markov models (HMMs) using the primary amino acid sequences, and combining PCI and HMM approaches increased accuracy.

Algorithms↗

PubMed2000

3D-1D threading methods for protein fold recognition.

The threading approach to protein fold recognition attempts to evaluate how well a query sequence fits into an already-solved fold. 3D-1D threaders rely on matching 1-dimensional strings of 3-dimensional information predicted from the query sequence with corresponding features of the target structure. In many cases this is combined with a sequence comparison. The combination of sequence and structure information has been shown to improve the accuracy of fold recognition, relative to the exclusive use of sequence or structure. In this paper, we review progress made since the introduction of threading methods a decade ago, highlighting recent advances. We focus on two emerging methods that are unconventional 3D-1D threaders: proximity correlation matrices and parallel cascade identification.

Animals↗

PubMed1997

Processing of color- and noncolor-coded signals in the gourami retina. III. Ganglion cells.

The dynamics of intracellular responses from ganglion cells, as well as that of spike discharges, were studied with the stimulus regimens and analytic procedures identical to those used to study the dynamics of the responses from horizontal and amacrine cells (,). The stimuli used were large fields of red and green light given as a pulsatile input or modulation about a mean luminance by a white-noise signal. Spike discharges evoked by a white-noise stimulus were analyzed in exactly the same manner as that used for analysis of analog responses. The canonical nature of kernels allowed us to correlate the first- and second-order components in a spike train with those of the intracellular responses from horizontal, amacrine, and ganglion cells. Both red and green stimuli given alone in darkness produced noncolor-coded responses from all ganglion cells. In the case of some cells, steady red illumination changed the polarity or waveform of the response to green light. Color-coded ganglions responded only to simultaneous color contrast. Nonlinearities recovered from intracellular responses, and spike discharges were similar to those found in responses from amacrine cells and were of two types, one characteristic of the C amacrine cells and the other characteristic of the N amacrine cells. The first-order kernels of most ganglion cells could be divided into two basic types, biphasic and triphasic. The combination of kernels of these two basic types with different polarities can produce a wide range of responses. Addition of two types of second-order nonlinearity could render color coding in this relatively simple retina as an extremely complex process. Color information appeared to be represented by the polarity, as well as the waveform, of the first-order kernel. The response dynamics is a means of transmission of color-coded information. Second-order components carry information about changes around a mean luminance regardless of the color of an input. Some spike discharges produced a well-defined cross-kernel between red and green inputs to show that a particular time sequence of red and green stimuli was detected by the retinal neuron network. The similarity between signatures of second-order kernels for both amacrine and ganglion cells indicates that signals undergo a minimal transformation in the temporal domain when they are transmitted from amacrine to ganglion cells and then transformed into a spike train. Under our experimental conditions, a single spike train carried simultaneously information about red and green inputs, as well as about linear and nonlinear components. In addition, the spike train also carries a cross-talk component. A spike train is a carrier of multiple signals. Conversely, many types of information in a stimulus are independently encoded into a spike train.

Animals↗

PubMed1997

Complexity and frequency hierarchies in the catfish retina.

The intricate connectivity and interactions between neurons in the vertebrate retina have made their individual roles in signal processing very difficult to elucidate. We have used a recently developed mathematical tool, fast orthogonal search (FOS), to probe the catfish outer (distal) and inner (proximal) retina, and study the signal processing within. Through FOS, a given waveform can be decomposed into a parsimonious sinusoidal series containing the most significant constituent frequencies. In particular, we examined the light-evoked first-order Wiener kernels of horizontal cells and on-bipolar cells, and on-off, off- and on-amacrine and ganglion cells. Here we report a hierarchy (correlation coefficient up to 0.86) in preferred frequency and complexity of response corresponding to the retina's structural hierarchy. In addition, clear differences between on-, on-off and off-cell functional characteristics were detected. For example, the kernel waveform for the on-amacrine cell was found to be more complex and to have a higher preferred frequency than that for the off-amacrine cell. Indeed FOS analysis revealed that both off- (sustained) amacrine and off-ganglion cells exhibit significantly less complexity in their waveforms for signal processing of light input than do the corresponding on- and on-off cells. This shows a clear breakdown in symmetry between on- and off-pathways, and suggests that connections to off-cells may provide fewer or a smaller variety of inputs than those to on- and on-off cells. Many of our new findings can be appreciated by assuming an underlying cascade structure for the retinal information processing. The FOS findings in particular support the following previously advanced hypothesis: the transition in nonlinear processing from on-off amacrine to on- off-amacrine cells is due to high-pass linear filtering. Furthermore, our results indicate that the high-pass filtering is more sharply differentiating for the on-amacrine than for the off-amacrine cell.

Animals↗

PubMed1995

Nonlinear models of the first synapse in the light-adapted fly retina.

1. Randomly modulated light stimuli were used to characterize the nonlinear dynamic properties of the synapse between photoreceptors and large monopolar neurons (LMC) in the fly retina. Membrane potential fluctuations produced by constant variance contrast stimuli were recorded at eight different levels of background light intensity. 2. Representation of the photoreceptor-LMC input-output data in the form of traditional characteristic curves indicated that synaptic gain was reduced by light adaptation. However, this representation did not include the time-dependent properties of the synaptic function, which are known to be nonlinear. Therefore nonlinear systems analysis was used to characterize the synapse. 3. The responses of photoreceptors and LMCs to random light fluctuations were characterized by second-order Volterra series, with kernel estimation by the parallel cascade method. Photoreceptor responses were approximately linear, but LMC responses were clearly nonlinear. 4. Synaptic input-output relationships were measured by passing the light stimuli to LMCs through the measured photoreceptor characteristics to obtain an estimate of the synaptic input. The resulting nonlinear synaptic functions were well characterized by second-order Volterra series. They could not be modeled by a linear-nonlinear-linear cascade but were better approximated by a nonlinear-linear-nonlinear cascade. 5. These results support two possible structural models of the synapse, the first having two parallel paths for signal flow between the photoreceptor and LMC, and the second having two distinct nonlinear operations, occurring before and after chemical transmission. 6. The two models were cach used to calculate the synaptic gain to a brief change in photoreceptor membrane potential. Both models predicted that synaptic gain is reduced by light adaptation.

Adaptation, Ocular↗

PubMed1993

The dynamic nonlinear behavior of fly photoreceptors evoked by a wide range of light intensities.

Fly photoreceptor cells were stimulated with steps of light over a wide intensity range. First- and second-order Volterra kernels were then computed from sequences of combined step responses. Diagonal values of the second-order Volterra kernels were much greater than the off-diagonal values, and the diagonal values were roughly proportional to the corresponding first-order kernels, suggesting that the response could be approximated by a static nonlinearity followed by a dynamic linear component (Hammerstein model). The amplitudes of the second-order kernels were much smaller in light-adapted than in dark-adapted photoreceptors. Hammerstein models constructed from the step input/output measurements gave reasonable approximations to the actual photoreceptor responses, with light-adapted responses being relatively better fitted. However, Hammerstein models could not account for several features of the photoreceptor behavior, including the dependence of the step response shape on step amplitude. A model containing an additional static nonlinearity after the dynamic linear component gave significantly better fits to the data. These results indicate that blowfly photoreceptors have a strong early gain control nonlinearity acting before the processes that create the characteristic time course of the response, in addition to the nonlinearities caused by membrane conductances.

Animals↗

PubMed1992

Identification of complex-cell intensive nonlinearities in a cascade model of cat visual cortex.

Complex cells in the cat's visual cortex show nonlinearities in processing of image luminance and movement. To study mechanisms, initially we have represented the chain of neurons from retina to cortex as a black-box model. Independent information about the visual system has helped us cast this "Wiener-kernel" model into a dynamic-linear/static-nonlinear/dynamic-linear (LNL) cascade. We then use system identification techniques to define the nature of these transformations directly from responses of the neuron to a single presentation of a stimulus composed of a sequence of white-noise-modulated luminance values. The two dynamic linear filters are mainly low-pass, and the static nonlinearity is mainly of even polynomial degree. This approximate squaring function may be effected in the animal by soft-thresholding each of the linear ON- and OFF-channel signals and then summing them, which account for "ON-OFF" responses and for the squaring operation needed for computation of "motion energy", both observed in these neurons.

Animals↗

PubMed1991

Parallel cascade identification and kernel estimation for nonlinear systems.

We consider the representation and identification of nonlinear systems through the use of parallel cascades of alternating dynamic linear and static nonlinear elements. Building on the work of Palm and others, we show that any discrete-time finite-memory nonlinear system having a finite-order Volterra series representation can be exactly represented by a finite number of parallel LN cascade paths. Each LN path consists of a dynamic linear system followed by a static nonlinearity (which can be a polynomial). In particular, we provide an upper bound for the number of parallel LN paths required to represent exactly a discrete-time finite-memory Volterra functional of a given order. Next, we show how to obtain a parallel cascade representation of a nonlinear system from a single input-output record. The input is not required to be Gaussian or white, nor to have special autocorrelation properties. Next, our parallel cascade identification is applied to measure accurately the kernels of nonlinear systems (even those with lengthy memory), and to discover the significant terms to include in a nonlinear difference equation model for a system. In addition, the kernel estimation is used as a means of studying individual signals to distinguish deterministic from random behaviour, in an alternative to the use of chaotic dynamics. Finally, an alternate kernel estimation scheme is presented.

Algorithms↗

PubMed1991

Dissection of a nonlinear cascade model for sensory encoding.

Action potential encoding in the cockroach tactile spine neuron may be treated as a single-input, single-output dynamic nonlinear process, where the input is the electric current flowing across the neuronal membrane and the output is the resultant train of action potentials. The nonlinear behavior of the system may be characterized by a functional expansion method which efficiently and accurately yields similar kernels to the Wiener method. A simple nonlinear cascade consisting of sequential dynamic linear, static nonlinear, and dynamic linear components was identified and gives a good approximation to the response of the neuron to random stimulation. Next, we attempted to study the components of the cascade by the use of a drug, phentolamine, which selectively modifies the dynamic behavior of the encoder. Application of phentolamine to the neuron caused a significant change in the first dynamic linear component of the cascade without affecting the other components. The change was much larger than the variability between results obtained from individual animals. This finding has implications for the biophysical processes which are involved in the components of the cascade.

Action Potentials↗

PubMed1990

The identification of nonlinear biological systems: Wiener kernel approaches.

Detection, representation, and identification of nonlinearities in biological systems are considered. We begin by briefly but critically examining a well-known test of system nonlinearity, and point out that this test cannot be used to prove that a system is linear. We then concentrate on the representation of nonlinear systems by Wiener's orthogonal functional series, discussing its advantages, limitations, and biological applications. System identification through estimating the kernels in the functional series is considered in detail. An efficient time-domain method of correcting for coloring in inputs is examined and shown to result in significantly improved kernel estimates in a biologically realistic system.

Linear Models↗

PubMed1990

Nonlinear mechanisms for gain adaptation in locust photoreceptors.

Intracellular membrane potential responses were recorded from locust photoreceptors under two stimulus conditions: pairs of flashes to dark-adapted receptors, and white-noise modulated light at a range of background intensities from 500 to 15,000 effective photons per second. Nonlinear analysis of the input-output relationships were performed by estimating the Volterra and Wiener kernels of the system. The Volterra kernels obtained from the double-flash experiments were similar to the Wiener kernels obtained from the white-noise experiments, except for a change of time scale. The structure of the second-order kernels obtained with either method gave evidence for a gain control mechanism acting at an early stage of the cascade. Both feedforward and feedback nonlinearities could account for the observed system behavior at any one background level. The differences in amplitude between the kernels obtained at different background levels could be accounted for by an adaptation process which further decreased the gain of the system, acting on a slower time scale, also at some early stage of the cascade.

Animals↗

PubMed1989

A robust orthogonal algorithm for system identification and time-series analysis.

We describe and illustrate methods for obtaining a parsimonious sinusoidal series representation or model of biological time-series data. The methods are also used to identify nonlinear systems with unknown structure. A key aspect is a rapid search for significant terms to include in the model for the system or the time-series. For example, the methods use fast and robust orthogonal searches for significant frequencies in the time-series, and differ from conventional Fourier series analysis in several important respects. In particular, the frequencies in our resulting sinusoidal series need not be commensurate, nor integral multiples of the fundamental frequency corresponding to the record length. Freed of these restrictions, the methods produce a more economical sinusoidal series representation (than a Fourier series), finding the most significant frequencies first, and automatically determine model order. The methods are also capable of higher resolution than a conventional Fourier series analysis. In addition, the methods can cope with unequally-spaced or missing data, and are applicable to time-series corrupted by noise. Finally, we compare one of our methods with a well-known technique for resolving sinusoidal signals in noise using published data for the test time-series.

Algorithms↗

PubMed1989

An orthogonal ARMA identifier with automatic order estimation for biological modeling.

In this paper an ARMA identification algorithm is developed for modeling biological time series data. The algorithm is based on Gram-Schmidt orthogonalization of automatically selected basis functions from a specified function space. The selection criterion is based on recursive testing of potential benefit to the model of candidate functions. The candidate functions, AR and MA terms, are tested in a pair-wise search direction until a least-squares criterion is satisfied, thereby estimating the order. Additive noise is considered and the basic algorithm extended to improve performance in noise. The algorithm is also extended to systems with inaccessible inputs (signal modeling). Modeling of biological data from speech is included, and indicates good performance. The algorithm is derived from earlier work on nonlinear systems identification.

Algorithms↗

PubMed1989

Applications of fast orthogonal search: time-series analysis and resolution of signals in noise.

In this paper a technique is examined for obtaining accurate and parsimonious sinusoidal series representations of biological time-series data, and for resolving sinusoidal signals in noise. The technique operates via a fast orthogonal search method discussed in the paper, and achieves economy of representation by finding the most significant sinusoidal frequencies first, in a least squares fit sense. Another reason for the parsimony in representation is that the identified sinusoidal series model is not restricted to frequencies which are commensurate or integral multiples of the fundamental frequency corresponding to the record length. Biological applications relate to spectral analysis of noisy time-series data such as EEG, ECG, EMG, EOG, and to speech analysis. Simulations are provided to demonstrate precise detection of component frequencies and weights in short data records, coping with missing or unequally spaced data, and recovery of signals heavily contaminated with noise. The technique is also shown to be capable of higher frequency resolution than is achievable by conventional Fourier series analysis.

Algorithms↗

PubMed1989

A nonlinear cascade model for action potential encoding in an insect sensory neuron.

Action potential encoding in the cockroach tactile spine neuron can be represented as a single-input single-output nonlinear dynamic process. We have used a new functional expansion method to characterize the nonlinear behavior of the neural encoder. This method, which yields similar kernels to the Wiener method, is more accurate than the latter and is efficient enough to obtain reasonable kernels in less than 15 min using a personal computer. The input stimulus was band-limited white Gaussian noise and the output consisted of the resulting train of action potentials, which were unitized to give binary values. The kernels and the system input-output signals were used to identify a model for encoding comprising a cascade of dynamic linear, static nonlinear, and dynamic linear components. The two dynamic linear components had repeatable and distinctive forms with the first being low-pass and the second being high-pass. The static nonlinearity was fitted with a fifth-order polynomial function over several input amplitude ranges and had the form of a half-wave rectifier. The complete model gave a good approximation to the output of the neuron when both were subjected to the same novel white noise input signal.

Action Potentials↗

PubMed1989

Dissection of the neuron network in the catfish inner retina. III. Interpretation of spike kernels.

1. Three independent sets of evidence have been obtained to show that similar first- and second-order light-evoked kernels are computed for the ganglion cell when the system output is taken to be either the spike train (discrete signal) or the postsynaptic potential (analog signal). In this paper we show that the similarity of postsynaptic potential (PSP) kernels and spike kernels is readily explained by assuming an underlying cascade structure for the neural information processing. The cascade structure enables spike kernels to be mathematically related very simply to the process of generating the postsynaptic potentials of ganglion cells. 2. Mathematical analysis of the cascade structure also suggests why spike kernels appear to differ slightly from PSP kernels. The relation between the two sets of kernels predicted from our analysis is substantiated here by experiment and reveals an interconnection between several of the signals measured. 3. Our experimental results, in particular, suggest that the neuronal circuitry leading from the light stimulus to the generation of ganglion cell spike discharges can be represented as follows: either a Wiener (LN) or a dynamic linear-static nonlinear-dynamic linear (LNL) structure is followed by a highly nonlinear process [static or brief-memory Hammerstein static-nonlinear dynamic-linear (NL) structure] of spike generation. Cross-correlation between the analog input and spike output enables identification of these structures.

Action Potentials↗