Models that contain the Model Concept : Pattern Recognition

(The detection of a pattern by an organism or machine. Examples: the recognition of female faces (vision), spoken numbers (sound), rough textures (touch), peppermint (smell), and salt (taste).)
Re-display model names without descriptions
    Models   Description
1.  3D model of the olfactory bulb (Migliore et al. 2014)
This entry contains a link to a full HD version of movie 1 and the NEURON code of the paper: "Distributed organization of a brain microcircuit analysed by three-dimensional modeling: the olfactory bulb" by M Migliore, F Cavarretta, ML Hines, and GM Shepherd.
2.  Biologically Constrained Basal Ganglia model (BCBG model) (Lienard, Girard 2014)
We studied the physiology and function of the basal ganglia through the design of mean-field models of the whole basal ganglia. The parameterizations are optimized with multi-objective evolutionary algorithm to respect best a collection of numerous anatomical data and electrophysiological data. The main outcomes of our study are: • The strength of the GPe to GPi/SNr connection does not support opposed activities in the GPe and GPi/SNr. • STN and MSN target more the GPe than the GPi/SNr. • Selection arises from the structure of the basal ganglia, without properly segregated direct and indirect pathways and without specific inputs from pyramidal tract neurons of the cortex. Selection is enhanced when the projection from GPe to GPi/SNr has a diffuse pattern.
3.  CA1 pyramidal neurons: binding properties and the magical number 7 (Migliore et al. 2008)
NEURON files from the paper: Single neuron binding properties and the magical number 7, by M. Migliore, G. Novara, D. Tegolo, Hippocampus, in press (2008). In an extensive series of simulations with realistic morphologies and active properties, we demonstrate how n radial (oblique) dendrites of these neurons may be used to bind n inputs to generate an output signal. The results suggest a possible neural code as the most effective n-ple of dendrites that can be used for short-term memory recollection of persons, objects, or places. Our analysis predicts a straightforward physiological explanation for the observed puzzling limit of about 7 short-term memory items that can be stored by humans.
4.  CONFIGR: a vision-based model for long-range figure completion (Carpenter et al. 2007)
"CONFIGR (CONtour FIgure GRound) is a computational model based on principles of biological vision that completes sparse and noisy image figures. Within an integrated vision/recognition system, CONFIGR posits an initial recognition stage which identifies figure pixels from spatially local input information. The resulting, and typically incomplete, figure is fed back to the “early vision” stage for long-range completion via filling-in. The reconstructed image is then re-presented to the recognition system for global functions such as object recognition. ... Multi-scale simulations illustrate the vision/recognition system. ..."
5.  Contrast invariance by LGN synaptic depression (Banitt et al. 2007)
"Simple cells in layer 4 of the primary visual cortex of the cat show contrast-invariant orientation tuning, in which the amplitude of the peak response is proportional to the stimulus contrast but the width of the tuning curve hardly changes with contrast. This study uses a detailed model of spiny stellate cells (SSCs) from cat area 17 to explain this property. The model integrates our experimental data, including morphological and intrinsic membrane properties and the number and spatial distribution of four major synaptic input sources of the SSC: the dorsal lateral geniculate nucleus (dLGN) and three cortical sources. ... The model response is in close agreement with experimental results, in terms of both output spikes and membrane voltage (amplitude and fluctuations), with reasonable exceptions given that recurrent connections were not incorporated."
6.  Cortex learning models (Weber at al. 2006, Weber and Triesch, 2006, Weber and Wermter 2006/7)
A simulator and the configuration files for three publications are provided. First, "A hybrid generative and predictive model of the motor cortex" (Weber at al. 2006) which uses reinforcement learning to set up a toy action scheme, then uses unsupervised learning to "copy" the learnt action, and an attractor network to predict the hidden code of the unsupervised network. Second, "A Self-Organizing Map of Sigma-Pi Units" (Weber and Wermter 2006/7) learns frame of reference transformations on population codes in an unsupervised manner. Third, "A possible representation of reward in the learning of saccades" (Weber and Triesch, 2006) implements saccade learning with two possible learning schemes for horizontal and vertical saccades, respectively.
7.  Democratic population decisions result in robust policy-gradient learning (Richmond et al. 2011)
This model demonstrates the use of GPU programming (with CUDA) to simulate a two-layer network of Integrate-and-Fire neurons with varying degrees of recurrent connectivity and to investigate its ability to learn a simplified navigation task using a learning rule stemming from Reinforcement Learning, a policy-gradient rule.
8.  Development of orientation-selective simple cell receptive fields (Rishikesh and Venkatesh, 2003)
Implementation of a computational model for the development of simple-cell receptive fields spanning the regimes before and after eye-opening. The before eye-opening period is governed by a correlation-based rule from Miller (Miller, J. Neurosci., 1994), and the post eye-opening period is governed by a self-organizing, experience-dependent dynamics derived in the reference below.
9.  Discrimination on behavioral time-scales mediated by reaction-diffusion in dendrites (Bhalla 2017)
Sequences of events are ubiquitous in sensory, motor, and cognitive function. Key computational operations, including pattern recognition, event prediction, and plasticity, involve neural discrimination of spatio-temporal sequences. Here we show that synaptically-driven reaction diffusion pathways on dendrites can perform sequence discrimination on behaviorally relevant time-scales. We used abstract signaling models to show that selectivity arises when inputs at successive locations are aligned with, and amplified by, propagating chemical waves triggered by previous inputs. We incorporated biological detail using sequential synaptic input onto spines in morphologically, electrically, and chemically detailed pyramidal neuronal models based on rat data.
10.  Effects of electric fields on cognitive functions (Migliore et al 2016)
The paper discusses the effects induced by an electric field at power lines frequency on neuronal activity during cognitive processes.
11.  Encoding and retrieval in a model of the hippocampal CA1 microcircuit (Cutsuridis et al. 2009)
This NEURON code implements a small network model (100 pyramidal cells and 4 types of inhibitory interneuron) of storage and recall of patterns in the CA1 region of the mammalian hippocampus. Patterns of PC activity are stored either by a predefined weight matrix generated by Hebbian learning, or by STDP at CA3 Schaffer collateral AMPA synapses.
12.  Feedforward heteroassociative network with HH dynamics (Lytton 1998)
Using the original McCulloch-Pitts notion of simple on and off spike coding in lieu of rate coding, an Anderson-Kohonen artificial neural network (ANN) associative memory model was ported to a neuronal network with Hodgkin-Huxley dynamics.
13.  First-Spike-Based Visual Categorization Using Reward-Modulated STDP (Mozafari et al. 2018)
"...Here, for the first time, we show that (Reinforcement Learning) RL can be used efficiently to train a spiking neural network (SNN) to perform object recognition in natural images without using an external classifier. We used a feedforward convolutional SNN and a temporal coding scheme where the most strongly activated neurons fire first, while less activated ones fire later, or not at all. In the highest layers, each neuron was assigned to an object category, and it was assumed that the stimulus category was the category of the first neuron to fire. ..."
14.  Hierarchical network model of perceptual decision making (Wimmer et al 2015)
Neuronal variability in sensory cortex predicts perceptual decisions. To investigate the interaction of bottom-up and top-down mechanisms during the decision process, we developed a hierarchical network model. The network consists of two circuits composed of leaky integrate-and-fire neurons: an integration circuit (e.g. LIP, FEF) and a sensory circuit (MT), recurrently coupled via bottom-up feedforward connections and top-down feedback connections. The integration circuit accumulates sensory evidence and produces a binary categorization due to winner-take-all competition between two decision-encoding populations (X.J. Wang, Neuron, 2002). The sensory circuit is a balanced randomly connected EI-network, that contains neural populations selective to opposite directions of motion. We have used this model to simulate a standard two-alternative forced-choice motion discrimination task.
15.  Hippocampus temporo-septal engram shift model (Lytton 1999)
Temporo-septal engram shift model of hippocampal memory. The model posits that memories gradually move along the hippocampus from a temporal encoding site to ever more septal sites from which they are recalled. We propose that the sense of time is encoded by the location of the engram along the temporo-septal axis.
16.  Hopfield and Brody model (Hopfield, Brody 2000)
NEURON implementation of the Hopfield and Brody model from the papers: JJ Hopfield and CD Brody (2000) JJ Hopfield and CD Brody (2001). Instructions are provided in the below readme.txt file.
17.  Hopfield and Brody model (Hopfield, Brody 2000) (NEURON+python)
Demonstration of Hopfield-Brody snychronization using artificial cells in NEURON+python.
18.  Human Attentional Networks: A Connectionist Model (Wang and Fan 2007)
"... We describe a connectionist model of human attentional networks to explore the possible interplays among the networks from a computational perspective. This model is developed in the framework of leabra (local, error-driven, and associative, biologically realistic algorithm) and simultaneously involves these attentional networks connected in a biologically inspired way. ... We evaluate the model by simulating the empirical data collected on normal human subjects using the Attentional Network Test (ANT). The simulation results fit the experimental data well. In addition, we show that the same model, with a single parameter change that affects executive control, is able to simulate the empirical data collected from patients with schizophrenia. This model represents a plausible connectionist explanation for the functional structure and interaction of human attentional networks."
19.  Inhibitory cells enable sparse coding in V1 model (King et al. 2013)
" ... Here we show that adding a separate population of inhibitory neurons to a spiking model of V1 provides conformance to Dale’s Law, proposes a computational role for at least one class of interneurons, and accounts for certain observed physiological properties in V1. ... "
20.  Inhibitory control by an integral feedback signal in prefrontal cortex (Miller and Wang 2006)
The prefrontal cortex (PFC) is known to be critical for inhibitory control of behavior, but the underlying mechanisms are unclear. Here, we propose that inhibitory control can be instantiated by an integral signal derived from working memory, another key function of the PFC. Specifically, we assume that an integrator converts excitatory input into a graded mnemonic activity that provides an inhibitory signal (integral feedback control) to upstream afferent neurons. We demonstrate this scenario in a neuronal-network model for a temporal discrimination task... See paper for details and more.
21.  Large scale model of the olfactory bulb (Yu et al., 2013)
The readme file currently contains links to the results for all the 72 odors investigated in the paper, and the movie showing the network activity during learning of odor k3-3 (an aliphatic ketone).
22.  Leech Mechanosensory Neurons: Synaptic Facilitation by Reflected APs (Baccus 1998)
This model by Stephen Baccus explores the phenomena of action potential (AP) propagation at branch boints in axons. APs are sometimes transmitted down the efferent processes and sometimes are reflected back to the axon of AP origin or neither. See the paper for details. The model zip file contains a readme.txt which list introductory steps to follow to run the simulation. Stephen Baccus's email address: baccus@fas.harvard.edu
23.  Long time windows from theta modulated inhib. in entorhinal–hippo. loop (Cutsuridis & Poirazi 2015)
"A recent experimental study (Mizuseki et al., 2009) has shown that the temporal delays between population activities in successive entorhinal and hippocampal anatomical stages are longer (about 70–80 ms) than expected from axon conduction velocities and passive synaptic integration of feed-forward excitatory inputs. We investigate via computer simulations the mechanisms that give rise to such long temporal delays in the hippocampus structures. ... The model shows that the experimentally reported long temporal delays in the DG, CA3 and CA1 hippocampal regions are due to theta modulated somatic and axonic inhibition..."
24.  Microsaccades and synchrony coding in the retina (Masquelier et al. 2016)
We show that microsaccades (MS) enable efficient synchrony-based coding among the primate retinal ganglion cells (RGC). We find that each MS causes certain RGCs to fire synchronously, namely those whose receptive fields contain contrast edges after the MS. The emitted synchronous spike volley thus rapidly transmits the most salient edges of the stimulus. We demonstrate that the readout could be done rapidly by simple coincidence-detector neurons, and that the required connectivity could emerge spontaneously with spike timing-dependent plasticity.
25.  Model of the cerebellar granular network (Sudhakar et al 2017)
"The granular layer, which mainly consists of granule and Golgi cells, is the first stage of the cerebellar cortex and processes spatiotemporal information transmitted by mossy fiber inputs with a wide variety of firing patterns. To study its dynamics at multiple time scales in response to inputs approximating real spatiotemporal patterns, we constructed a large-scale 3D network model of the granular layer. ..."
26.  Modular grid cell responses as a basis for hippocampal remapping (Monaco and Abbott 2011)
"Hippocampal place fields, the local regions of activity recorded from place cells in exploring rodents, can undergo large changes in relative location during remapping. This process would appear to require some form of modulated global input. Grid-cell responses recorded from layer II of medial entorhinal cortex in rats have been observed to realign concurrently with hippocampal remapping, making them a candidate input source. However, this realignment occurs coherently across colocalized ensembles of grid cells (Fyhn et al., 2007). The hypothesized entorhinal contribution to remapping depends on whether this coherence extends to all grid cells, which is currently unknown. We study whether dividing grid cells into small numbers of independently realigning modules can both account for this localized coherence and allow for hippocampal remapping. ..."
27.  Motion Clouds: Synthesis of random textures for motion perception (Leon et al. 2012)
We describe a framework to generate random texture movies with controlled information content. In particular, these stimuli can be made closer to naturalistic textures compared to usual stimuli such as gratings and random-dot kinetograms. We simplified the definition to parametrically define these "Motion Clouds" around the most prevalent feature axis (mean and bandwith): direction, spatial frequency, orientation.
28.  Olfactory bulb mitral and granule cell: dendrodendritic microcircuits (Migliore and Shepherd 2008)
This model shows how backpropagating action potentials in the long lateral dendrites of mitral cells, together with granule cell actions on mitral cells within narrow columns forming glomerular units, can provide a mechanism to activate strong local inhibition between arbitrarily distant mitral cells. The simulations predict a new role for the dendrodendritic synapses in the multicolumnar organization of the granule cells.
29.  Optimal spatiotemporal spike pattern detection by STDP (Masquelier 2017)
We simulate a LIF neuron equipped with STDP. A pattern repeats in its inputs. The LIF progressively becomes selective to the repeating pattern, in an optimal manner.
30.  Oscillation and coding in a proposed NN model of insect olfaction (Horcholle-Bossavit et al. 2007)
"For the analysis of coding mechanisms in the insect olfactory system, a fully connected network of synchronously updated McCulloch and Pitts neurons (MC-P type) was (previously) developed. ... Considering the update time as an intrinsic clock, this “Dynamic Neural Filter” (DNF), which maps regions of input space into spatio-temporal sequences of neuronal activity, is able to produce exact binary codes extracted from the synchronized activities recorded at the level of projection neurons (PN) in the locust antennal lobe (AL) in response to different odors ... We find synaptic matrices which lead to both the emergence of robust oscillations and spatio-temporal patterns, using a formal criterion, based on a Normalized Euclidian Distance (NED), in order to measure the use of the temporal dimension as a coding dimension by the DNF. Similarly to biological PN, the activity of excitatory neurons in the model can be both phase-locked to different cycles of oscillations which (is reminiscent of the) local field potential (LFP), and nevertheless exhibit dynamic behavior complex enough to be the basis of spatio-temporal codes."
31.  Oscillations, phase-of-firing coding and STDP: an efficient learning scheme (Masquelier et al. 2009)
The model demonstrates how a common oscillatory drive for a group of neurons formats and reliabilizes their spike times - through an activation-to-phase conversion - so that repeating activation patterns can be easily detected and learned by a downstream neuron equipped with STDP, and then recognized in just one oscillation cycle.
32.  Parallel odor processing by mitral and middle tufted cells in the OB (Cavarretta et al 2016, 2018)
"[...] experimental findings suggest that MC and mTC may encode parallel and complementary odor representations. We have analyzed the functional roles of these pathways by using a morphologically and physiologically realistic three-dimensional model to explore the MC and mTC microcircuits in the glomerular layer and deeper plexiform layers. [...]"
33.  Place and grid cells in a loop (Rennó-Costa & Tort 2017)
This model implements a loop circuit between place and grid cells. The model was used to explain place cell remapping and grid cell realignment. Grid cell model as a continuous attractor network. Place cells have recurrent attractor network. Rate models implemented with E%-MAX winner-take-all network dynamics, with gamma cycle time-step.
34.  Relative spike time coding and STDP-based orientation selectivity in V1 (Masquelier 2012)
Phenomenological spiking model of the cat early visual system. We show how natural vision can drive spike time correlations on sufficiently fast time scales to lead to the acquisition of orientation-selective V1 neurons through STDP. This is possible without reference times such as stimulus onsets, or saccade landing times. But even when such reference times are available, we demonstrate that the relative spike times encode the images more robustly than the absolute ones.
35.  Reward modulated STDP (Legenstein et al. 2008)
"... This article provides tools for an analytic treatment of reward-modulated STDP, which allows us to predict under which conditions reward-modulated STDP will achieve a desired learning effect. These analytical results imply that neurons can learn through reward-modulated STDP to classify not only spatial but also temporal firing patterns of presynaptic neurons. They also can learn to respond to specific presynaptic firing patterns with particular spike patterns. Finally, the resulting learning theory predicts that even difficult credit-assignment problems, where it is very hard to tell which synaptic weights should be modified in order to increase the global reward for the system, can be solved in a self-organizing manner through reward-modulated STDP. This yields an explanation for a fundamental experimental result on biofeedback in monkeys by Fetz and Baker. In this experiment monkeys were rewarded for increasing the firing rate of a particular neuron in the cortex and were able to solve this extremely difficult credit assignment problem. ... In addition our model demonstrates that reward-modulated STDP can be applied to all synapses in a large recurrent neural network without endangering the stability of the network dynamics."
36.  Self-influencing synaptic plasticity (Tamosiunaite et al. 2007)
"... Similar to a previous study (Saudargiene et al., 2004) we employ a differential Hebbian learning rule to emulate spike-timing dependent plasticity and investigate how the interaction of dendritic and back-propagating spikes, as the post-synaptic signals, could influence plasticity. ..."
37.  Self-organized olfactory pattern recognition (Kaplan & Lansner 2014)
" ... We present a large-scale network model with single and multi-compartmental Hodgkin–Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. ... The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian–Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures."
38.  Spikes,synchrony,and attentive learning by laminar thalamocort. circuits (Grossberg & Versace 2007)
"... The model hereby clarifies, for the first time, how the following levels of brain organization coexist to realize cognitive processing properties that regulate fast learning and stable memory of brain representations: single cell properties, such as spiking dynamics, spike-timing-dependent plasticity (STDP), and acetylcholine modulation; detailed laminar thalamic and cortical circuit designs and their interactions; aggregate cell recordings, such as current-source densities and local field potentials; and single cell and large-scale inter-areal oscillations in the gamma and beta frequency domains. ..."
39.  Spiking GridPlaceMap model (Pilly & Grossberg, PLoS One, 2013)
Development of spiking grid cells and place cells in the entorhinal-hippocampal system to represent positions in large spaces
40.  STDP allows fast rate-modulated coding with Poisson-like spike trains (Gilson et al. 2011)
The model demonstrates that a neuron equipped with STDP robustly detects repeating rate patterns among its afferents, from which the spikes are generated on the fly using inhomogenous Poisson sampling, provided those rates have narrow temporal peaks (10-20ms) - a condition met by many experimental Post-Stimulus Time Histograms (PSTH).
41.  STDP and NMDAR Subunits (Gerkin et al. 2007)
The paper argues for competing roles of NR2A- and NR2B-containing NMDARs in spike-timing-dependent plasticity. This simple dynamical model recapitulates the results of STDP experiments involving selective blockers of NR2A- and NR2B-containing NMDARs, for which the stimuli are pre- and postsynaptic spikes in varying combinations. Experiments were done using paired recordings from glutamatergic neurons in rat hippocampal cultures. This model focuses on the dynamics of the putative potentiation and depression modules themselves, and their interaction For detailed dynamics involving NMDARs and Ca2+ transients, see Rubin et al., J. Neurophys., 2005.
42.  Striatal GABAergic microcircuit, spatial scales of dynamics (Humphries et al, 2010)
The main thrust of this paper was the development of the 3D anatomical network of the striatum's GABAergic microcircuit. We grew dendrite and axon models for the MSNs and FSIs and extracted probabilities for the presence of these neurites as a function of distance from the soma. From these, we found the probabilities of intersection between the neurites of two neurons given their inter-somatic distance, and used these to construct three-dimensional striatal networks. These networks were examined for their predictions for the distributions of the numbers and distances of connections for all the connections in the microcircuit. We then combined the neuron models from a previous model (Humphries et al, 2009; ModelDB ID: 128874) with the new anatomical model. We used this new complete striatal model to examine the impact of the anatomical network on the firing properties of the MSN and FSI populations, and to study the influence of all the inputs to one MSN within the network.
43.  Theory of arachnid prey localization (Sturzl et al. 2000)
"Sand scorpions and many other arachnids locate their prey through highly sensitive slit sensilla at the tips (tarsi) of their eight legs. This sensor array responds to vibrations with stimulus-locked action potentials encoding the target direction. We present a neuronal model to account for stimulus angle determination using a population of second-order neurons, each receiving excitatory input from one tarsus and inhibition from a triad opposite to it. ..."
44.  Time-warp-invariant neuronal processing (Gutig & Sompolinsky 2009)
" ... Here, we report that time-warp-invariant neuronal processing can be subserved by the shunting action of synaptic conductances that automatically rescales the effective integration time of postsynaptic neurons. We propose a novel spike-based learning rule for synaptic conductances that adjusts the degree of synaptic shunting to the temporal processing requirements of a given task. Applying this general biophysical mechanism to the example of speech processing, we propose a neuronal network model for time-warp-invariant word discrimination and demonstrate its excellent performance on a standard benchmark speech-recognition task. ..."
45.  Visual Cortex Neurons: Dendritic study (Anderson et al 1999)
Neuron mod and hoc files for the paper: Anderson, J.C. Binzegger, T., Kahana, O., Segev, I., and Martin, K.A.C Dendritic asymmetry cannot account for directional responses in visual cortex. Nature Neuroscience 2:820:824, 1999

Re-display model names without descriptions