Hi Chips,
This is the original patent:
US8250011B2 Autonomous learning dynamic artificial neural computing device and brain inspired system 20080921
This is the main claim which defined the invention in its broadest form:
A
n information processing system intended for use in artificial intelligence and having a plurality of digital artificial neuron circuits connected in an array, the system comprising
a plurality of digital dynamic synapse circuits, wherein each digital dynamic synapse circuit contains a binary register that stores a value representing neurotransmitter type and level, wherein the digital dynamic synapse circuits comprise a means of learning and responding to input signals, either by producing or compounding the value, thereby simulating behavior of a biological synapse; and
a temporal integrator circuit that integrates and combines each individually simulated synapse neurotransmitter type and value over time, wherein time is dependent on the neurotransmitter type stored in each digital dynamic synapse circuit.
It is highly improbable that it can be extended. That requires exceptional circumstances which prevented exploitation of the invention, eg, war. Normal technical/commercial circumstances would not suffice.
I think that this one is more relevant to Akida 1 ...
US11468299B2 Spiking neural network 20181101
View attachment 67401
A
neuromorphic integrated circuit, comprising:
a spike converter circuit configured to generate spikes from input data;
a reconfigurable neuron fabric comprising a neural processor comprising a plurality of spiking neuron circuits configured to perform a task based on the spikes and a neural network configuration; and
a memory comprising the neural network configuration, wherein the neural network configuration comprises a potential array and a plurality of synapses, and the neural network configuration defines connections between the plurality of spiking neuron circuits and the plurality of synapses, the potential array comprising membrane potential values for the plurality of spiking neuron circuits, and the plurality of synapses having corresponding synaptic weights,
wherein the neural processor is configured to:
select a spiking neuron circuit in the plurality of spiking neuron circuits based on the selected spiking neuron circuit having a membrane potential value that is a highest value among the membrane potential values for the plurality of spiking neuron circuits;
determine that the membrane potential value of the selected spiking neuron circuit reached a learning threshold value associated with the selected spiking neuron circuit; and
perform a Spike Time Dependent Plasticity (STDP) learning function based on the determination that the membrane potential value of the selected spiking neuron circuit reached the learning threshold value associated with the selected spiking neuron circuit.
... because it gives me the chance to post the Mona Lisa of NNs (elegance, beauty and simplicity).
The TeNNs patent may prove to be just as valuable:
WO2023250093A1 METHOD AND SYSTEM FOR IMPLEMENTING TEMPORAL CONVOLUTION IN SPATIOTEMPORAL NEURAL NETWORKS 20220622
View attachment 67402
a
neural network system generally relates to the field of neural networks (NNs). In particular, the present disclosure relates to event-based convolutional neural networks (NNs) that are trained to process spatial and temporal data using kernels represented by polynomial expansion. The event-based convolutional neural networks (NNs) are spatiotemporal neural networks. According to an embodiment, an explicit temporal convolution capability is added through Temporal Event-based Neural Networks (TENN) models, or TENNs in the spatiotemporal neural networks. The TENNs includes a plurality of temporal and spatial convolution layers that combine spatial and temporal features of data for low-level and high-level features. The TENNs as disclosed herein are configured to perform in a buffer mode and recurrent mode that effectively learns both spatial and temporal correlations from the input data.
... and this supporting patent application:
WO2023250092A1 METHOD AND SYSTEM FOR PROCESSING EVENT-BASED DATA IN EVENT-BASED SPATIOTEMPORAL NEURAL NETWORKS 20220622
a
method for processing event-based input data using a neural network. The neural network comprises a plurality of neurons and one or more connections associated with each of the plurality of neurons. Further, each of the plurality of neurons is configured to receive a corresponding portion of the event-based data. The method comprises receiving, at a neuron of the plurality of neurons, a plurality of events associated with the event-based data over the one or more connections associated with the neuron. Each of the one or more connections is associated with a kernel. The method further comprises determining a potential of the neuron over the period of time based on processing of the kernels. In order to determine the potential, the method further comprises offsetting the kernels in one of a spatial dimension, a temporal dimension, or a spatiotemporal dimension, and processing the offset kernels in order to determine the potential. The method further comprises generating, at the neuron, output based on the determined potential.
so nice to see
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nice that our ceo decided to sell his private car … looks like the company is going well” or something like this
