| Patent application number | Description | Published |
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| 20130073500 | High level neuromorphic network description apparatus and methods - Apparatus and methods for high-level neuromorphic network description (HLND) framework that may be configured to enable users to define neuromorphic network architectures using a unified and unambiguous representation that is both human-readable and machine-interpretable. The framework may be used to define nodes types, node-to-node connection types, instantiate node instances for different node types, and to generate instances of connection types between these nodes. To facilitate framework usage, the HLND format may provide the flexibility required by computational neuroscientists and, at the same time, provides a user-friendly interface for users with limited experience in modeling neurons. The HLND kernel may comprise an interface to Elementary Network Description (END) that is optimized for efficient representation of neuronal systems in hardware-independent manner and enables seamless translation of HLND model description into hardware instructions for execution by various processing modules. | 03-21-2013 |
| 20130297539 | SPIKING NEURAL NETWORK OBJECT RECOGNITION APPARATUS AND METHODS - Apparatus and methods for feedback in a spiking neural network. In one approach, spiking neurons receive sensory stimulus and context signal that correspond to the same context. When the stimulus provides sufficient excitation, neurons generate response. Context connections are adjusted according to inverse spike-timing dependent plasticity. When the context signal precedes the post synaptic spike, context synaptic connections are depressed. Conversely, whenever the context signal follows the post synaptic spike, the connections are potentiated. The inverse STDP connection adjustment ensures precise control of feedback-induced firing, eliminates runaway positive feedback loops, enables self-stabilizing network operation. In another aspect of the invention, the connection adjustment methodology facilitates robust context switching when processing visual information. When a context (such an object) becomes intermittently absent, prior context connection potentiation enables firing for a period of time. If the object remains absent, the connection becomes depressed thereby preventing further firing. | 11-07-2013 |
| 20130297541 | SPIKING NEURAL NETWORK FEEDBACK APPARATUS AND METHODS - Apparatus and methods for feedback in a spiking neural network. In one approach, spiking neurons receive sensory stimulus and context signal that correspond to the same context. When the stimulus provides sufficient excitation, neurons generate response. Context connections are adjusted according to inverse spike-timing dependent plasticity. When the context signal precedes the post synaptic spike, context synaptic connections are depressed. Conversely, whenever the context signal follows the post synaptic spike, the connections are potentiated. The inverse STDP connection adjustment ensures precise control of feedback-induced firing, eliminates runaway positive feedback loops, enables self-stabilizing network operation. In another aspect of the invention, the connection adjustment methodology facilitates robust context switching when processing visual information. When a context (such an object) becomes intermittently absent, prior context connection potentiation enables firing for a period of time. If the object remains absent, the connection becomes depressed thereby preventing further firing. | 11-07-2013 |
| 20130297542 | SENSORY INPUT PROCESSING APPARATUS IN A SPIKING NEURAL NETWORK - Apparatus and methods for feedback in a spiking neural network. In one approach, spiking neurons receive sensory stimulus and context signal that correspond to the same context. When the stimulus provides sufficient excitation, neurons generate response. Context connections are adjusted according to inverse spike-timing dependent plasticity. When the context signal precedes the post synaptic spike, context synaptic connections are depressed. Conversely, whenever the context signal follows the post synaptic spike, the connections are potentiated. The inverse STDP connection adjustment ensures precise control of feedback-induced firing, eliminates runaway positive feedback loops, enables self-stabilizing network operation. In another aspect of the invention, the connection adjustment methodology facilitates robust context switching when processing visual information. When a context (such an object) becomes intermittently absent, prior context connection potentiation enables firing for a period of time. If the object remains absent, the connection becomes depressed thereby preventing further firing. | 11-07-2013 |
| 20130304447 | Spike-timing computer modeling of working memory - Working memory (WM) is part of the brain's memory system that provides temporary storage and manipulation of information necessary for cognition. Although WM has limited capacity at any given time, it has vast memory content in the sense that it acts on the brain's nearly infinite repertoire of lifetime memories. As described, large memory content and WM functionality emerge spontaneously if the spike-timing nature of neuronal processing is taken into account. The memories are represented by extensively overlapping groups of neurons that exhibit stereotypical time-locked spatiotemporal spike-timing patterns, called polychronous patterns. Using computer-implemented simulations, associative synaptic plasticity in the form of short-term STDP selects such polychronous neuronal groups (PNGs) into WM by temporarily strengthening the synapses of the selected PNGs. This strengthening increases the spontaneous reactivation frequency of the selected PNGs, resulting in irregular, yet systematically changing elevated firing activity patterns consistent with those recorded in vivo during WM tasks. The computer-implemented model implements the relationship between such slowly changing firing rates and precisely timed spikes, and also reveals a novel relationship between WM and the perception of time on the order of seconds. | 11-14-2013 |
| 20140032458 | APPARATUS AND METHODS FOR EFFICIENT UPDATES IN SPIKING NEURON NETWORK - Efficient updates of connections in artificial neuron networks may be implemented. A framework may be used to describe the connections using a linear synaptic dynamic process, characterized by stable equilibrium. The state of neurons and synapses within the network may be updated, based on inputs and outputs to/from neurons. In some implementations, the updates may be implemented at regular time intervals. In one or more implementations, the updates may be implemented on-demand, based on the network activity (e.g., neuron output and/or input) so as to further reduce computational load associated with the synaptic updates. The connection updates may be decomposed into multiple event-dependent connection change components that may be used to describe connection plasticity change due to neuron input. Using event-dependent connection change components, connection updates may be executed on per neuron basis, as opposed to per-connection basis. | 01-30-2014 |
| 20140052679 | APPARATUS AND METHODS FOR IMPLEMENTING EVENT-BASED UPDATES IN SPIKING NEURON NETWORKS - Event-based updates in artificial neuron networks may be implemented. An internal event may be defined in order to update incoming connections of a neuron. The internal event may be triggered by an external signal and/or internally by the neuron. A reinforcement signal may be used to trigger an internal event of a neuron in order to perform synaptic updates without necessitating post-synaptic response. An external event may be defined in order to deliver response of the neuron to desired targets. The external and internal events may be combined into a composite event configured to effectuate connection update and spike delivery to post-synaptic target. The scope of the internal event may comprise the respective neuron and does not extend to other neurons of the network. Conversely, the scope of the external event may extend to other neurons of the network via, for example, post-synaptic spike delivery. | 02-20-2014 |
| 20140122400 | APPARATUS AND METHODS FOR ACTIVITY-BASED PLASTICITY IN A SPIKING NEURON NETWORK - Apparatus and methods for plasticity in spiking neuron network. The network may comprise feature-specific units capable of responding to different objects (red and green color). Plasticity mechanism may be configured based on difference between two similarity measures related to activity of different unit types obtained during network training. One similarity measure may be based on activity of units of the same type (red). Another similarity measure may be based on activity of units of one type (red) and another type (green). Similarity measures may comprise a cross-correlogram and/or mutual information determined over an activity window. Several similarity estimates, corresponding to different unit-to-unit pairs may be combined. The combination may comprise a weighted average. During network operation, the activity based plasticity mechanism may be used to potentiate connections between units of the same type (red-red). The plasticity mechanism may be used to depress connections between units of different types (red-green). | 05-01-2014 |
| 20140156574 | RATE STABILIZATION THROUGH PLASTICITY IN SPIKING NEURON NETWORK - Apparatus and methods for activity based plasticity in a spiking neuron network adapted to process sensory input. In one embodiment, the plasticity mechanism may be configured for example based on activity of one or more neurons providing feed-forward stimulus and activity of one or more neurons providing inhibitory feedback. When an inhibitory neuron generates an output, inhibitory connections may be potentiated. When an inhibitory neuron receives inhibitory input, the inhibitory connection may be depressed. When the inhibitory input arrives subsequent to the neuron response, the inhibitory connection may be depressed. When input features are unevenly distributed in occurrence, the plasticity mechanism is capable of reducing response rate of neurons that develop receptive fields to more prevalent features. Such functionality may provide network output such that rarely occurring features are not drowned out by more widespread stimulus. | 06-05-2014 |
| 20140244557 | APPARATUS AND METHODS FOR RATE-MODULATED PLASTICITY IN A SPIKING NEURON NETWORK - Apparatus and methods for activity based plasticity in a spiking neuron network adapted to process sensory input. In one approach, the plasticity mechanism of a connection may comprise a causal potentiation portion and an anti-causal portion. The anti-causal portion, corresponding to the input into a neuron occurring after the neuron response, may be configured based on the prior activity of the neuron. When the neuron is in low activity state, the connection, when active, may be potentiated by a base amount. When the neuron activity increases due to another input, the efficacy of the connection, if active, may be reduced proportionally to the neuron activity. Such functionality may enable the network to maintain strong, albeit inactive, connections available for use for extended intervals. | 08-28-2014 |
| 20140277718 | ADAPTIVE PREDICTOR APPARATUS AND METHODS - Apparatus and methods for training and operating of robotic devices. Robotic controller may comprise a predictor apparatus configured to generate motor control output. The predictor may be operable in accordance with a learning process based on a teaching signal comprising the control output. An adaptive controller block may provide control output that may be combined with the predicted control output. The predictor learning process may be configured to learn the combined control signal. Predictor training may comprise a plurality of trials. During initial trial, the control output may be capable of causing a robot to perform a task. During intermediate trials, individual contributions from the controller block and the predictor may be inadequate for the task. Upon learning, the control knowledge may be transferred to the predictor so as to enable task execution in absence of subsequent inputs from the controller. Control output and/or predictor output may comprise multi-channel signals. | 09-18-2014 |
| 20140317035 | APPARATUS AND METHODS FOR EVENT-BASED COMMUNICATION IN A SPIKING NEURON NETWORKS - Apparatus and methods for event based communication in a spiking neuron network. The network may comprise units communicating by spikes via synapses. The spikes may communicate a payload data. The data may comprise one or more bits. The payload may be stored in a buffer of a pre-synaptic unit and be configured to accessed by the post-synaptic unit. Spikes of different payload may cause different actions by the recipient unit. Sensory input spikes may cause postsynaptic response and trigger connection efficacy update. Teaching input spikes trigger the efficacy update without causing the post-synaptic response. | 10-23-2014 |
| 20140365414 | SPIKING NETWORK APPARATUS AND METHOD WITH BIMODAL SPIKE-TIMING DEPENDENT PLASTICITY - Apparatus and methods for learning in response to temporally-proximate features. In one implementation, an image processing apparatus utilizes bi-modal spike timing dependent plasticity in a spiking neuron network. Based on a response by the neuron to a frame of input, the bi-modal plasticity mechanism is used to depress synaptic connections delivering the present input frame and to potentiate synaptic connections delivering previous and/or subsequent frames of input. The depression of near-contemporaneous input prevents the creation of a positive feedback loop and provides a mechanism for network response normalization. | 12-11-2014 |
| 20140365417 | APPARATUS AND METHODS FOR RATE-MODULATED PLASTICITY IN A SPIKING NEURON NETWORK - Apparatus and methods for activity based plasticity in a spiking neuron network adapted to process sensory input. In one approach, the plasticity mechanism of a connection may comprise a causal potentiation portion and an anti-causal portion. The anti-causal portion, corresponding to the input into a neuron occurring after the neuron response, may be configured based on the prior activity of the neuron. When the neuron is in low activity state, the connection, when active, may be potentiated by a base amount. When the neuron activity increases due to another input, the efficacy of the connection, if active, may be reduced proportionally to the neuron activity. Such functionality may enable the network to maintain strong, albeit inactive, connections available for use for extended intervals. | 12-11-2014 |
| 20140371907 | ROBOTIC TRAINING APPARATUS AND METHODS - Apparatus and methods for training of robotic devices. Robotic devices may be trained by a user guiding the robot along target trajectory using an input signal. A robotic device may comprise an adaptive controller configured to generate control commands based on one or more of the user guidance, sensory input, and/or performance measure. Training may comprise a plurality of trials. During first trial, the user input may be sufficient to cause the robot to complete the trajectory. During subsequent trials, the user and the robot's controller may collaborate so that user input may be reduced while the robot control may be increased. Individual contributions from the user and the robot controller during training may be may be inadequate (when used exclusively) to complete the task. Upon learning, user's knowledge may be transferred to the robot's controller to enable task execution in absence of subsequent inputs from the user | 12-18-2014 |
| 20140371912 | HIERARCHICAL ROBOTIC CONTROLLER APPARATUS AND METHODS - A robot may be trained by a user guiding the robot along target trajectory using a control signal. A robot may comprise an adaptive controller. The controller may be configured to generate control commands based on the user guidance, sensory input and a performance measure. A user may interface to the robot via an adaptively configured remote controller. The remote controller may comprise a mobile device, configured by the user in accordance with phenotype and/or operational configuration of the robot. The remote controller may detect changes in the robot phenotype and/or operational configuration. The remote controller may comprise multiple control elements configured to activate respective portions of the robot platform. Based on training, the remote controller may configure composite controls configured based two or more of control elements. Activation of a composite control may enable the robot to perform a task. | 12-18-2014 |
| 20140379624 | INCREASED DYNAMIC RANGE ARTIFICIAL NEURON NETWORK APPARATUS AND METHODS - Apparatus and methods for processing inputs by one or more neurons of a network. The neuron(s) may generate spikes based on receipt of multiple inputs. Latency of spike generation may be determined based on an input magnitude. Inputs may be scaled using for example a non-linear concave transform. Scaling may increase neuron sensitivity to lower magnitude inputs, thereby improving latency encoding of small amplitude inputs. The transformation function may be configured compatible with existing non-scaling neuron processes and used as a plug-in to existing neuron models. Use of input scaling may allow for an improved network operation and reduce task simulation time. | 12-25-2014 |
| 20150074026 | APPARATUS AND METHODS FOR EVENT-BASED PLASTICITY IN SPIKING NEURON NETWORKS - Event based communication in a spiking neuron network may be provided. The network may comprise units communicating by spikes via synapses. Responsive to a spike generation, a unit may be configured to update states of outgoing synapses. The spikes may communicate a payload data. The data may comprise one or more bits. The payload may be stored in a buffer of a pre-synaptic unit and be configured to accessed by the post-synaptic unit. Spikes of different payload may cause different actions by the recipient unit. Sensory input spikes may cause postsynaptic response and trigger connection efficacy update. Teaching input may be used to modulate plasticity. | 03-12-2015 |
