Abstract: Object recognition apparatus and methods useful for extracting information from an input signal. In one embodiment, the input signal is representative of an element of an image, and the extracted information is encoded into patterns of pulses. The patterns of pulses are directed via transmission channels to a plurality of detector nodes configured to generate an output pulse upon detecting an object of interest. Upon detecting a particular object, a given detector node elevates its sensitivity to that particular object when processing subsequent inputs. In one implementation, one or more of the detector nodes are also configured to prevent adjacent detector nodes from generating detection signals in response to the same object representation. The object recognition apparatus modulates properties of the transmission channels by promoting contributions from channels carrying information used in object recognition.

Abstract: Apparatus and methods for encoding sensory input information into patterns of pulses and message multiplexing. In one implementation, the patterns of pulses are polychronous (time-locked by not necessary synchronous), and a retinal prosthetic encodes the input signal into the polychronous patterns for delivery via stimulating electrodes. Different polychronous patterns simultaneously encode different sensory signals; (such as different features of the image), thus providing for message multiplexing. Increasing data transmission capacity allows for a reduction in the number of electrodes required for data transmission. In one implementation, an adaptive feedback mechanism is employed to facilitate encoder operation. In another aspect, a computer vision system is described.

Abstract: Image processing systems and methods extract information from an input signal representative of an element of an image and to encode the information in a pulsed output signal. A plurality of channels communicates the pulsed output signal, each of the plurality of channels being characterized by a latency. The information may be encoded as a pattern of relative pulse latencies observable in pulses communicated through the plurality of channels and the pattern of relative pulse latencies is substantially insensitive to image contrast and/or image luminance. A filter can be employed to provide a generator signal based on the input signal and pulse latencies can be determined using a logarithmic function of the generator signal. The filter may be temporally and/or spatially balanced and characterized by an integral along spatial and/or temporal dimensions of the filter that is substantially zero for all values of a temporal and/or a spatial variable.

Abstract: In Pavlovian and instrumental conditioning, rewards typically come seconds after reward-triggering actions, creating an explanatory conundrum known as the distal reward problem or the credit assignment problem. How does the brain know what firing patterns of what neurons are responsible for the reward if (1) the firing patterns are no longer there when the reward arrives and (2) most neurons and synapses are active during the waiting period to the reward? A model network and computer simulation of cortical spiking neurons with spike-timing-dependent plasticity (STDP) modulated by dopamine (DA) is disclosed to answer this question. STDP is triggered by nearly-coincident firing patterns of a presynaptic neuron and a postsynaptic neuron on a millisecond time scale, with slow kinetics of subsequent synaptic plasticity being sensitive to changes in the extracellular dopamine DA concentration during the critical period of a few seconds after the nearly-coincident firing patterns.

Abstract: 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.

Abstract: In Pavlovian and instrumental conditioning, rewards typically come seconds after reward-triggering actions, creating an explanatory conundrum known as the distal reward problem or the credit assignment problem. How does the brain know what firing patterns of what neurons are responsible for the reward if (1) the firing patterns are no longer there when the reward arrives and (2) most neurons and synapses are active during the waiting period to the reward? A model network and computer simulation of cortical spiking neurons with spike-timing-dependent plasticity (STDP) modulated by dopamine (DA) is disclosed to answer this question. STDP is triggered by nearly-coincident firing patterns of a presynaptic neuron and a postsynaptic neuron on a millisecond time scale, with slow kinetics of subsequent synaptic plasticity being sensitive to changes in the extracellular dopamine DA concentration during the critical period of a few seconds after the nearly-coincident firing patterns.

Abstract: Image processing systems and methods extract information from an input signal representative of an element of an image and to encode the information in a pulsed output signal. A plurality of channels communicates the pulsed output signal, each of the plurality of channels being characterized by a latency. The information may be encoded as a pattern of relative pulse latencies observable in pulses communicated through the plurality of channels and the pattern of relative pulse latencies is substantially insensitive to image contrast and/or image luminance. A filter can be employed to provide a generator signal based on the input signal and pulse latencies can be determined using a logarithmic function of the generator signal. The filter may be temporally and/or spatially balanced and characterized by an integral along spatial and/or temporal dimensions of the filter that is substantially zero for all values of a temporal and/or a spatial variable.

Abstract: Systems and methods for processing image signals are described. One method comprises obtaining a generator signal based on an image signal and determining relative latencies associated with two or more pulses in a pulsed signal using a function of the generator signal that can comprise a logarithmic function. The function of the generator signal can be the absolute value of its argument. Information can be encoded in the pattern of relative latencies. Latencies can be determined using a scaling parameter that is calculated from a history of the image signal. The pulsed signal is typically received from a plurality of channels and the scaling parameter corresponds to at least one of the channels. The scaling parameter may be adaptively calculated such that the latency of the next pulse falls within one or more of a desired interval and an optimal interval.

Abstract: Among other things, with respect to entities each of which has attributes from which a value of the entity to an aspect of one or more fields of human activity can be evaluated subjectively, accumulating subjective information interactively and electronically from people who are experts or peers in one or more of the fields of human activity concerning the value of the entities to the aspect of one or more of the fields, and automatically generating data about relative values of at least some of the entities to the aspect of at least one of the fields based on at least some of the accumulated subjective information.

Abstract: In Pavlovian and instrumental conditioning, rewards typically come seconds after reward-triggering actions, creating an explanatory conundrum known as the distal reward problem or the credit assignment problem. How does the brain know what firing patterns of what neurons are responsible for the reward if (1) the firing patterns are no longer there when the reward arrives and (2) most neurons and synapses are active during the waiting period to the reward? A model network and computer simulation of cortical spiking neurons with spike-timing-dependent plasticity (STDP) modulated by dopamine (DA) is disclosed to answer this question. STDP is triggered by nearly-coincident firing patterns of a presynaptic neuron and a postsynaptic neuron on a millisecond time scale, with slow kinetics of subsequent synaptic plasticity being sensitive to changes in the extracellular dopamine DA concentration during the critical period of a few seconds after the nearly-coincident firing patterns.

Abstract: A neural network computer (20) includes a weighting network (21) coupled to a plurality of phase-locked loop circuits (251-25N). The weighting network (21) has a plurality of weighting circuits (C11, . . . , CNN) having output terminals connected to a plurality of adder circuits (311-31N). A single weighting element (Ckj) typically has a plurality of output terminals coupled to a corresponding adder circuit (31k). Each adder circuit (31k) is coupled to a corresponding bandpass filter circuit (31k) which is in turn coupled to a corresponding phase-locked loop circuit (25k). The weighting elements (C1,1, . . . , CN,N) are programmed with connection strengths, wherein the connection strengths have phase-encoded weights. The phase relationships are used to recognize an incoming pattern.