10,504 research outputs found
Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems
Neuromorphic chips embody computational principles operating in the nervous
system, into microelectronic devices. In this domain it is important to
identify computational primitives that theory and experiments suggest as
generic and reusable cognitive elements. One such element is provided by
attractor dynamics in recurrent networks. Point attractors are equilibrium
states of the dynamics (up to fluctuations), determined by the synaptic
structure of the network; a `basin' of attraction comprises all initial states
leading to a given attractor upon relaxation, hence making attractor dynamics
suitable to implement robust associative memory. The initial network state is
dictated by the stimulus, and relaxation to the attractor state implements the
retrieval of the corresponding memorized prototypical pattern. In a previous
work we demonstrated that a neuromorphic recurrent network of spiking neurons
and suitably chosen, fixed synapses supports attractor dynamics. Here we focus
on learning: activating on-chip synaptic plasticity and using a theory-driven
strategy for choosing network parameters, we show that autonomous learning,
following repeated presentation of simple visual stimuli, shapes a synaptic
connectivity supporting stimulus-selective attractors. Associative memory
develops on chip as the result of the coupled stimulus-driven neural activity
and ensuing synaptic dynamics, with no artificial separation between learning
and retrieval phases.Comment: submitted to Scientific Repor
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Efficient spiking neural network model of pattern motion selectivity in visual cortex
Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance approach to simulating large-scale neural network models in real-time is to leverage the parallel processing capability of graphics processing units (GPUs). Based on this approach, we present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction- selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and Heeger. Pattern-direction-selective (PDS) cells in MT are constructed by pooling over MT CDS cells with a wide range of preferred directions. Responses of our model neurons are comparable to electrophysiological results for grating and plaid stimuli as well as speed tuning. The behavioral response of the network in a motion discrimination task is in agreement with psychophysical data. Moreover, our implementation outperforms a previous implementation of the motion energy model by orders of magnitude in terms of computational speed and memory usage. The full network, which comprises 153,216 neurons and approximately 40 million synapses, processes 20 frames per second of a 40∈×∈40 input video in real-time using a single off-the-shelf GPU. To promote the use of this algorithm among neuroscientists and computer vision researchers, the source code for the simulator, the network, and analysis scripts are publicly available. © 2014 Springer Science+Business Media New York
Neural population coding: combining insights from microscopic and mass signals
Behavior relies on the distributed and coordinated activity of neural populations. Population activity can be measured using multi-neuron recordings and neuroimaging. Neural recordings reveal how the heterogeneity, sparseness, timing, and correlation of population activity shape information processing in local networks, whereas neuroimaging shows how long-range coupling and brain states impact on local activity and perception. To obtain an integrated perspective on neural information processing we need to combine knowledge from both levels of investigation. We review recent progress of how neural recordings, neuroimaging, and computational approaches begin to elucidate how interactions between local neural population activity and large-scale dynamics shape the structure and coding capacity of local information representations, make them state-dependent, and control distributed populations that collectively shape behavior
An Efficient Method for online Detection of Polychronous Patterns in Spiking Neural Network
Polychronous neural groups are effective structures for the recognition of
precise spike-timing patterns but the detection method is an inefficient
multi-stage brute force process that works off-line on pre-recorded simulation
data. This work presents a new model of polychronous patterns that can capture
precise sequences of spikes directly in the neural simulation. In this scheme,
each neuron is assigned a randomized code that is used to tag the post-synaptic
neurons whenever a spike is transmitted. This creates a polychronous code that
preserves the order of pre-synaptic activity and can be registered in a hash
table when the post-synaptic neuron spikes. A polychronous code is a
sub-component of a polychronous group that will occur, along with others, when
the group is active. We demonstrate the representational and pattern
recognition ability of polychronous codes on a direction selective visual task
involving moving bars that is typical of a computation performed by simple
cells in the cortex. The computational efficiency of the proposed algorithm far
exceeds existing polychronous group detection methods and is well suited for
online detection.Comment: 17 pages, 8 figure
Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity
In rodent visual cortex, synaptic connections between orientation-selective neurons are unspecific at the time of eye opening, and become to some degree functionally specific only later during development. An explanation for this two-stage process was proposed in terms of Hebbian plasticity based on visual experience that would eventually enhance connections between neurons with similar response features. For this to work, however, two conditions must be satisfied: First, orientation selective neuronal responses must exist before specific recurrent synaptic connections can be established. Second, Hebbian learning must be compatible with the recurrent network dynamics contributing to orientation selectivity, and the resulting specific connectivity must remain stable for unspecific background activity. Previous studies have mainly focused on very simple models, where the receptive fields of neurons were essentially determined by feedforward mechanisms, and where the recurrent network was small, lacking the complex recurrent dynamics of large-scale networks of excitatory and inhibitory neurons. Here we studied the emergence of functionally specific connectivity in large-scale recurrent networks with synaptic plasticity. Our results show that balanced random networks, which already exhibit highly selective responses at eye opening, can develop feature-specific connectivity if appropriate rules of synaptic plasticity are invoked within and between excitatory and inhibitory populations. If these conditions are met, the initial orientation selectivity guides the process of Hebbian learning and, as a result, functionally specific and a surplus of bidirectional connections emerge. Our results thus demonstrate the cooperation of synaptic plasticity and recurrent dynamics in large-scale functional networks with realistic receptive fields, highlight the role of inhibition as a critical element in this process, and paves the road for further computational studies of sensory processing in neocortical network models equipped with synaptic plasticity
Neural synchrony in cortical networks : history, concept and current status
Following the discovery of context-dependent synchronization of oscillatory neuronal responses in the visual system, the role of neural synchrony in cortical networks has been expanded to provide a general mechanism for the coordination of distributed neural activity patterns. In the current paper, we present an update of the status of this hypothesis through summarizing recent results from our laboratory that suggest important new insights regarding the mechanisms, function and relevance of this phenomenon. In the first part, we present recent results derived from animal experiments and mathematical simulations that provide novel explanations and mechanisms for zero and nero-zero phase lag synchronization. In the second part, we shall discuss the role of neural synchrony for expectancy during perceptual organization and its role in conscious experience. This will be followed by evidence that indicates that in addition to supporting conscious cognition, neural synchrony is abnormal in major brain disorders, such as schizophrenia and autism spectrum disorders. We conclude this paper with suggestions for further research as well as with critical issues that need to be addressed in future studies
Neural synchrony in cortical networks : history, concept and current status
Following the discovery of context-dependent synchronization of oscillatory neuronal responses in the visual system, the role of neural synchrony in cortical networks has been expanded to provide a general mechanism for the coordination of distributed neural activity patterns. In the current paper, we present an update of the status of this hypothesis through summarizing recent results from our laboratory that suggest important new insights regarding the mechanisms, function and relevance of this phenomenon. In the first part, we present recent results derived from animal experiments and mathematical simulations that provide novel explanations and mechanisms for zero and nero-zero phase lag synchronization. In the second part, we shall discuss the role of neural synchrony for expectancy during perceptual organization and its role in conscious experience. This will be followed by evidence that indicates that in addition to supporting conscious cognition, neural synchrony is abnormal in major brain disorders, such as schizophrenia and autism spectrum disorders. We conclude this paper with suggestions for further research as well as with critical issues that need to be addressed in future studies
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