300 research outputs found
Synchronized dynamics of cortical neurons with time-delay feedback
Get PDFThe dynamics of three mutually coupled cortical neurons with time delays in
the coupling are explored numerically and analytically. The neurons are coupled
in a line, with the middle neuron sending a somewhat stronger projection to the
outer neurons than the feedback it receives, to model for instance the relay of
a signal from primary to higher cortical areas. For a given coupling
architecture, the delays introduce correlations in the time series at the
time-scale of the delay. It was found that the middle neuron leads the outer
ones by the delay time, while the outer neurons are synchronized with zero lag
times. Synchronization is found to be highly dependent on the synaptic time
constant, with faster synapses increasing both the degree of synchronization
and the firing rate. Analysis shows that presynaptic input during the
interspike interval stabilizes the synchronous state, even for arbitrarily weak
coupling, and independent of the initial phase. The finding may be of
significance to synchronization of large groups of cells in the cortex that are
spatially distanced from each other.Comment: 21 pages, 11 figure
Self-organization in the olfactory system: one shot odor recognition in insects
Get PDFWe show in a model of spiking neurons that synaptic plasticity in the mushroom bodies in combination with the general fan-in, fan-out properties of the early processing layers of the olfactory system might be sufficient to account for its efficient recognition of odors. For a large variety of initial conditions the model system consistently finds a working solution without any fine-tuning, and is, therefore, inherently robust. We demonstrate that gain control through the known feedforward inhibition of lateral horn interneurons increases the capacity of the system but is not essential for its general function. We also predict an upper limit for the number of odor classes Drosophila can discriminate based on the number and connectivity of its olfactory neurons
Information transmission in oscillatory neural activity
Full text linkPeriodic neural activity not locked to the stimulus or to motor responses is
usually ignored. Here, we present new tools for modeling and quantifying the
information transmission based on periodic neural activity that occurs with
quasi-random phase relative to the stimulus. We propose a model to reproduce
characteristic features of oscillatory spike trains, such as histograms of
inter-spike intervals and phase locking of spikes to an oscillatory influence.
The proposed model is based on an inhomogeneous Gamma process governed by a
density function that is a product of the usual stimulus-dependent rate and a
quasi-periodic function. Further, we present an analysis method generalizing
the direct method (Rieke et al, 1999; Brenner et al, 2000) to assess the
information content in such data. We demonstrate these tools on recordings from
relay cells in the lateral geniculate nucleus of the cat.Comment: 18 pages, 8 figures, to appear in Biological Cybernetic
Competition-based model of pheromone component ratio detection in the moth
Get PDFFor some moth species, especially those closely interrelated and sympatric, recognizing a specific pheromone component concentration ratio is essential for males to successfully locate conspecific females. We propose and determine the properties of a minimalist competition-based feed-forward neuronal model capable of detecting a certain ratio of pheromone components independently of overall concentration. This model represents an elementary recognition unit for the ratio of binary mixtures which we propose is entirely contained in the macroglomerular complex (MGC) of the male moth. A set of such units, along with projection neurons (PNs), can provide the input to higher brain centres. We found that (1) accuracy is mainly achieved by maintaining a certain ratio of connection strengths between olfactory receptor neurons (ORN) and local neurons (LN), much less by properties of the interconnections between the competing LNs proper. An exception to this rule is that it is beneficial if connections between generalist LNs (i.e. excited by either pheromone component) and specialist LNs (i.e. excited by one component only) have the same strength as the reciprocal specialist to generalist connections. (2) successful ratio recognition is achieved using latency-to-first-spike in the LN populations which, in contrast to expectations with a population rate code, leads to a broadening of responses for higher overall concentrations consistent with experimental observations. (3) when longer durations of the competition between LNs were observed it did not lead to higher recognition accuracy
Gain control network conditions in early sensory coding
Get PDFGain control is essential for the proper function of any sensory system. However, the precise mechanisms for achieving effective gain control in the brain are unknown. Based on our understanding of the existence and strength of connections in the insect olfactory system, we analyze the conditions that lead to controlled gain in a randomly connected network of excitatory and inhibitory neurons. We consider two scenarios for the variation of input into the system. In the first case, the intensity of the sensory input controls the input currents to a fixed proportion of neurons of the excitatory and inhibitory populations. In the second case, increasing intensity of the sensory stimulus will both, recruit an increasing number of neurons that receive input and change the input current that they receive. Using a mean field approximation for the network activity we derive relationships between the parameters of the network that ensure that the overall level of activity
of the excitatory population remains unchanged for increasing intensity of the external stimulation. We find that, first, the main parameters that regulate network gain are the probabilities of connections from the inhibitory population to the excitatory population and of the connections within the inhibitory population. Second, we show that strict gain control is not achievable in a random network in the second case, when the input recruits an increasing number of neurons. Finally, we confirm that the gain control conditions derived from the mean field approximation are valid in simulations of firing rate
models and Hodgkin-Huxley conductance based models
A phase I dose escalation study of BIBW 2992, an irreversible dual inhibitor of epidermal growth factor receptor 1 (EGFR) and 2 (HER2) tyrosine kinase in a 2-week on, 2-week off schedule in patients with advanced solid tumours
Get PDFTo assess tolerability, pharmacokinetics (PK), pharmacodynamics (PD) and clinical activity of the dual epidermal growth factor receptor (EGFR) 1 and 2 (HER2) tyrosine kinase inhibitor BIBW 2992. An escalating schedule of once-daily (OD) BIBW 2992 for 14 days followed by 14 days off medication was explored. Thirty-eight patients were enrolled. Dose levels were 10, 20, 30, 45, 70, 85, and 100 mg. At 100 mg dose-limiting toxicity (DLT) (common toxicity criteria grade 3 skin rash and grade 3 diarrhoea despite treatment with loperamide) occurred in two patients. In the next-lower dose of 70 mg, DLT (grade 3 fatigue and ALAT elevation) occurred in one of six patients. An intermediate dose level of 85 mg was studied. Here DLT occurred in two patients (grade 3 diarrhoea despite treatment and grade 2 diarrhoea lasting more than 7 days despite treatment). An additional 12 patients were treated at 70 mg. BIBW 2992 PK after single and multiple doses revealed moderately fast absorption, and no deviation from dose proportionality. Pharmacodynamics analysis in skin biopsies did not show significant changes in EGFR-associated biomarkers. However, a significant inhibitory effect on the proliferation index of epidermal keratinocytes was observed. No partial or complete responses were observed, stable disease lasting more than four cycles was seen in seven patients. The recommended dose for studies with BIBW 2992 for 14 days followed by 14 days off medication is 70 mg OD
Stimulus-dependent maximum entropy models of neural population codes
Get PDFNeural populations encode information about their stimulus in a collective
fashion, by joint activity patterns of spiking and silence. A full account of
this mapping from stimulus to neural activity is given by the conditional
probability distribution over neural codewords given the sensory input. To be
able to infer a model for this distribution from large-scale neural recordings,
we introduce a stimulus-dependent maximum entropy (SDME) model---a minimal
extension of the canonical linear-nonlinear model of a single neuron, to a
pairwise-coupled neural population. The model is able to capture the
single-cell response properties as well as the correlations in neural spiking
due to shared stimulus and due to effective neuron-to-neuron connections. Here
we show that in a population of 100 retinal ganglion cells in the salamander
retina responding to temporal white-noise stimuli, dependencies between cells
play an important encoding role. As a result, the SDME model gives a more
accurate account of single cell responses and in particular outperforms
uncoupled models in reproducing the distributions of codewords emitted in
response to a stimulus. We show how the SDME model, in conjunction with static
maximum entropy models of population vocabulary, can be used to estimate
information-theoretic quantities like surprise and information transmission in
a neural population.Comment: 11 pages, 7 figure
Generalization Mediates Sensitivity to Complex Odor Features in the Honeybee
Get PDFAnimals use odors as signals for mate, kin, and food recognition, a strategy which appears ubiquitous and successful despite the high intrinsic variability of naturally-occurring odor quantities. Stimulus generalization, or the ability to decide that two objects, though readily distinguishable, are similar enough to afford the same consequence [1], could help animals adjust to variation in odor signals without losing sensitivity to key inter-stimulus differences. The present study was designed to investigate whether an animal's ability to generalize learned associations to novel odors can be influenced by the nature of the associated outcome. We use a classical conditioning paradigm for studying olfactory learning in honeybees [2] to show that honeybees conditioned on either a fixed- or variable-proportion binary odor mixture generalize learned responses to novel proportions of the same mixture even when inter-odor differences are substantial. We also show that the resulting olfactory generalization gradients depend critically on both the nature of the stimulus-reward paradigm and the intrinsic variability of the conditioned stimulus. The reward dependency we observe must be cognitive rather than perceptual in nature, and we argue that outcome-dependent generalization is necessary for maintaining sensitivity to inter-odor differences in complex olfactory scenes
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