Wiring optimization can relate neuronal structure and function
... Our approach for understanding neuronal structures complements neural development and relies on the existence of general principles governing the architecture of a mature brain. Specifically, we exploit the wiring economy principle proposed by Ramón y Cajal more than 100 years ago (11). This princi ...
... Our approach for understanding neuronal structures complements neural development and relies on the existence of general principles governing the architecture of a mature brain. Specifically, we exploit the wiring economy principle proposed by Ramón y Cajal more than 100 years ago (11). This princi ...
The Impact of Prior Experience With Cross-Modal
... refers to the general increase in the number of action potentials measured when a second modality is presented to a cell doing MI, while depression refers to the general decrease in the number of action potentials. Meredith and Stein (1983) demonstrated that a sensory stimulus (light) presented to a ...
... refers to the general increase in the number of action potentials measured when a second modality is presented to a cell doing MI, while depression refers to the general decrease in the number of action potentials. Meredith and Stein (1983) demonstrated that a sensory stimulus (light) presented to a ...
Learning Innate Face Preferences
... addition to the primary visual cortex. The same model allows preferences for low-level features such as orientation or spatial frequency, and high-level features such as faces. to be compared, in order to account for the different categories of preferences found in experiments with newborns. Very fe ...
... addition to the primary visual cortex. The same model allows preferences for low-level features such as orientation or spatial frequency, and high-level features such as faces. to be compared, in order to account for the different categories of preferences found in experiments with newborns. Very fe ...
The encoding and decoding of com-
... readout stage. We propose a novel neural readout circuit based on wavelet transform that decodes the TPC over different frequency bands. We show that, in comparison with pure linear readouts used previously, the proposed system provides a robust, fast and highly compact representation of visual inpu ...
... readout stage. We propose a novel neural readout circuit based on wavelet transform that decodes the TPC over different frequency bands. We show that, in comparison with pure linear readouts used previously, the proposed system provides a robust, fast and highly compact representation of visual inpu ...
Simulations of the Role of the Muscarinic-Activated Calcium- I in Entorhinal Neuronal
... Entorhinal layer II pyramidal cells The properties of entorhinal pyramidal cells were simulated with biophysical models containing multiple compartments, with an emphasis on the calcium-sensitive nonspecific cation current INCM. The compartmental structure of these simulations is shown in Figure 1. ...
... Entorhinal layer II pyramidal cells The properties of entorhinal pyramidal cells were simulated with biophysical models containing multiple compartments, with an emphasis on the calcium-sensitive nonspecific cation current INCM. The compartmental structure of these simulations is shown in Figure 1. ...
Zebrafish and motor control over the last decade
... escape. This variation is related to variability in the behavior, with stronger escape bends produced in response to stimuli at the head associated with activation of the entire serial set of cells and the weaker escapes produced by stimuli to the tail involving only the activation of the Mauthner c ...
... escape. This variation is related to variability in the behavior, with stronger escape bends produced in response to stimuli at the head associated with activation of the entire serial set of cells and the weaker escapes produced by stimuli to the tail involving only the activation of the Mauthner c ...
Neural Coding and Auditory Perception
... present in the VAS stimuli, low-CF cells maintain better directional sensitivity in reverberation than high-CF cells. Using recordings from primary auditory neurons, we show that this result can be attributed to the fact that reverberation degrades the directional information in envelope ITDs more s ...
... present in the VAS stimuli, low-CF cells maintain better directional sensitivity in reverberation than high-CF cells. Using recordings from primary auditory neurons, we show that this result can be attributed to the fact that reverberation degrades the directional information in envelope ITDs more s ...
the distribution of the cells of origin of callosal projections in cat
... cortex (Palmer et al., 1978) and of areas 20 and 21 (Heath and Jones, 1971; Tusa and Palmer, 1980). This is unfortunate since, in behavioral experiments, Berlucchi et al. (1979) showed that the callosal connections of the cat’s suprasylvian cortex (including the lateral suprasylvian visual areas and ...
... cortex (Palmer et al., 1978) and of areas 20 and 21 (Heath and Jones, 1971; Tusa and Palmer, 1980). This is unfortunate since, in behavioral experiments, Berlucchi et al. (1979) showed that the callosal connections of the cat’s suprasylvian cortex (including the lateral suprasylvian visual areas and ...
Encoding of Movement Fragments in the Motor Cortex
... also used to build the exponential encoding model. The crosshairs represent the mean position of the sample trajectory. Right, Fifty example movement segments extracted from an RTP task data set. C, Temporal evolution of preferred directions for the same four neurons as in A as a function of lead/la ...
... also used to build the exponential encoding model. The crosshairs represent the mean position of the sample trajectory. Right, Fifty example movement segments extracted from an RTP task data set. C, Temporal evolution of preferred directions for the same four neurons as in A as a function of lead/la ...
Deep learning in neural networks: An overview
... a car. Depending on the problem and how the neurons are connected, such behavior may require long causal chains of computational stages (Section 3), where each stage transforms (often in a non-linear way) the aggregate activation of the network. Deep Learning is about accurately assigning credit acr ...
... a car. Depending on the problem and how the neurons are connected, such behavior may require long causal chains of computational stages (Section 3), where each stage transforms (often in a non-linear way) the aggregate activation of the network. Deep Learning is about accurately assigning credit acr ...
sv-lncs
... There are other reasons that SVMs are used for intrusion detection. The first is speed: as real-time performance is of primary importance to intrusion detection systems, any classifier that can potentially run “fast” is worth considering. The second reason is scalability: SVMs are relatively insensi ...
... There are other reasons that SVMs are used for intrusion detection. The first is speed: as real-time performance is of primary importance to intrusion detection systems, any classifier that can potentially run “fast” is worth considering. The second reason is scalability: SVMs are relatively insensi ...
Short-Lasting Classical Conditioning Induces
... ing. The stimulator was controlled manually. After a 6 sec interval the trial was repeated. Pairings were repeated four times/min for 10 min/ d for 3 d. Altogether, these animals (n = 7) received 120 pairings of CS + UCS. One day after the end of training the cortical representation of row B was ma ...
... ing. The stimulator was controlled manually. After a 6 sec interval the trial was repeated. Pairings were repeated four times/min for 10 min/ d for 3 d. Altogether, these animals (n = 7) received 120 pairings of CS + UCS. One day after the end of training the cortical representation of row B was ma ...
A Bayesian network primer
... acyclic graphs (DAGs) instead of more general graphs to represent a probability distribution and optionally the causal structure of the domain. In an intuitive causal interpretation, the nodes represent the uncertain quantities, the edges denote direct causal influences, defining the model structure ...
... acyclic graphs (DAGs) instead of more general graphs to represent a probability distribution and optionally the causal structure of the domain. In an intuitive causal interpretation, the nodes represent the uncertain quantities, the edges denote direct causal influences, defining the model structure ...
How the brain uses time to represent and process visual information
... deletions, and time-shifts of spikes. The cost of moving a spike by an amount of time t is set at qt, and the cost of inserting a spike or deleting it is set at unity. Thus, spike trains are considered similar (in the sense of D spike [q]) only if they have approximately the same number of spikes, a ...
... deletions, and time-shifts of spikes. The cost of moving a spike by an amount of time t is set at qt, and the cost of inserting a spike or deleting it is set at unity. Thus, spike trains are considered similar (in the sense of D spike [q]) only if they have approximately the same number of spikes, a ...
A neuronal network model of primary visual cortex explains spatial
... preference with some local positive spatial correlation. To be consistent with these data, we also divide the network into many clusters of cells, inside each of which the V1 neurons share the same width of their LGN input arrays. Clusters of different width are arranged randomly across the V1 layer ...
... preference with some local positive spatial correlation. To be consistent with these data, we also divide the network into many clusters of cells, inside each of which the V1 neurons share the same width of their LGN input arrays. Clusters of different width are arranged randomly across the V1 layer ...
Classification using sparse representations
... Representing signals as linear combinations of basis vectors sparsely selected from an overcomplete dictionary has proven to be advantageous for many applications in pattern recognition, machine learning, signal processing, and computer vision. While this approach was originally inspired by insights ...
... Representing signals as linear combinations of basis vectors sparsely selected from an overcomplete dictionary has proven to be advantageous for many applications in pattern recognition, machine learning, signal processing, and computer vision. While this approach was originally inspired by insights ...
Encoding Information in Neuronal Activity
... and lower during the next second. During the NO- GO tasks the pattern of correlated firing was reversed. The correlation was low in the first second and high in the following second. There was no difference in the firing rates between the two paradigms and no difference in the cross-correlation . Th ...
... and lower during the next second. During the NO- GO tasks the pattern of correlated firing was reversed. The correlation was low in the first second and high in the following second. There was no difference in the firing rates between the two paradigms and no difference in the cross-correlation . Th ...
Improving the Associative Rule Chaining Architecture
... of rules was generated with a given depth d and maximum branching factor b, using the same procedure as detailed in previous work [1]. These rules were then learned by both systems, and rule chaining was performed on them. In all experiments, we fixed the vector weight for both rules and tokens to b ...
... of rules was generated with a given depth d and maximum branching factor b, using the same procedure as detailed in previous work [1]. These rules were then learned by both systems, and rule chaining was performed on them. In all experiments, we fixed the vector weight for both rules and tokens to b ...
Chapter 06 Abstract Neuron Models
... behaviors of networks. At the level of neuron modeling, what is immediately of concern to us is Grossberg's comment, "Two seemingly different models can be equivalent from a functional viewpoint if they both generate similar sets of emergent behaviors." In every abstract neuron model some or even al ...
... behaviors of networks. At the level of neuron modeling, what is immediately of concern to us is Grossberg's comment, "Two seemingly different models can be equivalent from a functional viewpoint if they both generate similar sets of emergent behaviors." In every abstract neuron model some or even al ...
Neural Control of Interappendage Phase During Locomotion
... Coordinated locomotion in many multiappendage animals (e.g., crayfish, cockroach, chicken, cat) is characterized by the movement of a given limb having (i) the same frequency as another limb and (ii) a regulated phase relationship with another limb (Gray, 1968). Motor neuron recordings from deaffere ...
... Coordinated locomotion in many multiappendage animals (e.g., crayfish, cockroach, chicken, cat) is characterized by the movement of a given limb having (i) the same frequency as another limb and (ii) a regulated phase relationship with another limb (Gray, 1968). Motor neuron recordings from deaffere ...
A COMMON REFERENCE FRAME FOR MOVEMENT PLANS IN
... effectors. The posterior parietal cortex has an important role in these transformations. Recent work indicates that a significant proportion of parietal neurons in two cortical areas transforms the sensory signals that are used to guide movements into a common reference frame. This common reference ...
... effectors. The posterior parietal cortex has an important role in these transformations. Recent work indicates that a significant proportion of parietal neurons in two cortical areas transforms the sensory signals that are used to guide movements into a common reference frame. This common reference ...
Gee JNeuro 2012 - Stanford University
... layer V pyramidal neurons above this threshold as “type A” neurons, and those below this threshold as “type B neurons.” Based on the studies outlined above, we would predict that type A neurons (more h-current) would be thick-tufted, whereas type B neurons (minimal h-current) should be thin-tufted. ...
... layer V pyramidal neurons above this threshold as “type A” neurons, and those below this threshold as “type B neurons.” Based on the studies outlined above, we would predict that type A neurons (more h-current) would be thick-tufted, whereas type B neurons (minimal h-current) should be thin-tufted. ...
Neural Syntax: Cell Assemblies, Synapsembles, and
... requires two key conditions: a reader-classifier and a temporal frame. Neurons come together in transient time frames to produce a composite downstream effect, which cannot be achieved by single neurons alone. The most important modus operandi in this process is synchrony of events (Abeles, 1991; En ...
... requires two key conditions: a reader-classifier and a temporal frame. Neurons come together in transient time frames to produce a composite downstream effect, which cannot be achieved by single neurons alone. The most important modus operandi in this process is synchrony of events (Abeles, 1991; En ...
Intracellular study of rat substantia nigra pars reticulata neurons in
... Fig. 1. Input resistance and spike discharges of type-t neurons A membrane responses to mtraceilularly rejected hyper- and depolarrang currents of various intensities. In order to eliminate spontaneous finng, a hyperpolanzmg current of 0.06 nA was continuously injected m the neuron. B membrane respo ...
... Fig. 1. Input resistance and spike discharges of type-t neurons A membrane responses to mtraceilularly rejected hyper- and depolarrang currents of various intensities. In order to eliminate spontaneous finng, a hyperpolanzmg current of 0.06 nA was continuously injected m the neuron. B membrane respo ...
The Control of Rate and Timing of Spikes in the Deep Cerebellar
... DCN spiking by using three different synchronization levels. At the highest level of input synchronization, our 400 presynaptic elements were divided into 10 groups of 40 synchronously activated elements. The activity between groups was uncorrelated. An intermediate level of input synchronization co ...
... DCN spiking by using three different synchronization levels. At the highest level of input synchronization, our 400 presynaptic elements were divided into 10 groups of 40 synchronously activated elements. The activity between groups was uncorrelated. An intermediate level of input synchronization co ...