# US 2013/0159231 A1 · Multi-modal neural network for universal, online learning

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2013/0159231 A1
- Document ID: US20130159231A1
- Status: Published application
- Kind: A1
- Application number: 13/596,274
- Filed: 2012-08-28
- Published: 2013-06-20
- Earliest claimed: 2011-12-14
- Assignees: International Business Machines Corporation
- Inventors: Dharmendra S. MODHA
- Web page: https://patentgrep.com/patent/US20130159231A1

## CPC

- G06N 3/08: Learning methods
- G06N 3/04: Architecture, e.g. interconnection topology
- G06N 3/0499: Feedforward networks
- G06N 3/09: Supervised learning
- G06N 3/092: Reinforcement learning
- G06N 3/02: Neural networks
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

In one embodiment, the present invention provides a neural network comprising multiple modalities. Each modality comprises multiple neurons. The neural network further comprises an interconnection lattice for cross-associating signaling between the neurons in different modalities. The interconnection lattice includes a plurality of perception neuron populations along a number of bottom-up signaling pathways, and a plurality of action neuron populations along a number of top-down signaling pathways. Each perception neuron along a bottom-up signaling pathway has a corresponding action neuron along a reciprocal top-down signaling pathway. An input neuron population configured to receive sensory input drives perception neurons along a number of bottom-up signaling pathways. A first set of perception neurons along bottom-up signaling pathways drive a first set of action neurons along top-down signaling pathways. Action neurons along a number of top-down signaling pathways drive an output neuron population configured to generate motor output.

## Claims

### Claim 1

A method, comprising:
interconnecting a plurality of neural nodes via an interconnect network of multiple signaling pathways arranged in a lattice;
wherein interconnecting said plurality of neural nodes includes connecting a plurality of first nodes in a first set of nodes with a plurality of second nodes in a second set of nodes;
each node generating a signal in response to input signals received from one or more other nodes via the interconnect network; and
a connected node in the second set exchanging signals with at least two nodes in the first set via the interconnect network.

### Claim 2 (depends on 1)

The method of claim 1, wherein a node comprises one or more neuron populations interconnected via multiple directed edges arranged in an acyclic digraph, wherein each neuron population comprises one or more neurons, and each edge includes a signaling pathway in the interconnect network.

### Claim 3 (depends on 2)

The method of claim 2, wherein interconnecting said plurality of neural nodes further includes interconnecting said plurality of neural nodes via bottom-up signaling pathways arranged in an acyclic bottom-up digraph in the interconnect network, each bottom-up signaling pathway including one or more neuron populations and directed edges.

### Claim 4 (depends on 3)

The method of claim 3, wherein interconnecting said plurality of neural nodes further includes interconnecting said plurality of neural nodes via top-down signaling pathways arranged in an acyclic top-down digraph in the interconnect network, each top-down signaling pathway including one or more neuron populations and directed edges.

### Claim 5 (depends on 4)

The method of claim 4, wherein:
each neuron population in the top-down digraph corresponds to a neuron population in the bottom-up digraph; and
each top-down signaling pathway in the top-down digraph has a reciprocal bottom-up signaling pathway in the bottom-up digraph, such that information flows along said top-down signaling pathway in a first direction, and information flows along said reciprocal bottom-up signaling pathway in a direction opposite of the first direction.

### Claim 6 (depends on 5)

The method of claim 5, further comprising:
designating a neuron population at an input periphery of the neural network as an input neuron population configured to receive sensory input, wherein the input neuron population drives neurons along a number of bottom-up signaling pathways;
a first set of neurons along bottom-up signaling pathways driving a first set of neurons along top-down signaling pathways; and
designating a neuron population at an output periphery of the neural network as an output neuron population configured to generate motor output, wherein neurons along a number of top-down signaling pathways drive the output neuron population.

### Claim 7 (depends on 6)

The method of claim 6, further comprising:
training each neuron along a bottom-up signaling pathway using a learning rule based on the firing events of said neuron and the firing events of the corresponding neuron along a reciprocal top-down signaling pathway; and
training each neuron along a top-down signaling pathway using a learning rule based on the firing events of said neuron and the firing events of the corresponding neuron along a reciprocal bottom-up signaling pathway.