3. Software:

Cognitive software applications for natural language, text understanding, and common-sense reasoning will become essential as time passes but we do not currently know how to write software with anything like the properties or power of human cognition. We have argued here in favor of a particular approach to building a brain like computer. Let us briefly discuss appropriate software and its requirements.

3.1. Arithmetic on a Brain-Like Computer.

We have one worked-out demonstration of the way advanced cognitive software might be structured (Anderson, 1998). Consider elementary arithmetic. It can be considered strictly as a set of abstract operations on numbers, for example, we could consider the positive integers as a one-dimensional linked list, the approach taken by Peano’s Postulates. However humans, in their interactions with number do not work this way. Compelling experimental evidence suggests that humans operate with numbers much as they do with sensory magnitudes like light intensities, weights, etc. Therefore the nodes representing number are not simple, but contain important internal structure representing sensory magnitude, and in addition, complex associations involving number name, spelling, character shape, etc. Although these “extraneous” properties are not needed to get the correct answers to elementary arithmetic, they seem to be essential for what humans call “mathematical intuition,” the rich set of refined perceptions that underlie both arithmetic and higher mathematics. If we wish to make an artificial system work with abstractions the way we do, it will be necessary to understand how humans do it.

3.2. Expanded Semantic Network Nodes.

Consider a semantic network. One natural computer implementation is to let a node equal a basic computational unit, a CPU, as explicitly assumed in Hillis’ book, The Connection Machine. However real concepts contain a complex internal structure with powerful sensory components, as we saw for arithmetic. Let us assume that the nodes in our network must be expanded to be both realistic and, we claim, become a flexible and powerful computing tool. Below we discuss the computational and representational assumptions that follow from the idea of expanded nodes. The genesis of these assumptions in cortical physiology and anatomy is obvious.

3.3. Expanded Nodes: Representational Assumptions.

• Regional structure. The 2-D CPU array is divided into regions. Each region performs one, or a small number, of computations.
• Topographic organization of regions. Sensory topography can map directly into the CPU arrangement. Biological examples are vision (topographic maps), audition (frequency maps), and the skin senses (body surface maps).
• Data Representation reflects and makes use of this topography. Since we assume many interactions are local, the computation works best when the necessary data is represented locally. The sensory representations display sparse coding. The connections between modules and between regions are sparse matrices.
• Both “Analog” and “Digital” processing are essential. Continuous valued activity patterns sum, interact, and decide on a discrete output (attractor) that is surrounded by a penumbra of weaker activities that reflect, sum together, and provide context. The penumbra can be the result of past activities fading, coming activity strengthening and weak associations.
• Sensory processing is done by specialized sensory structures in the sense organs and earlier pathways. The array works with highly and effectively processed information. Not all modules receive sensory inputs.

The Figure shows the large modules plus a few individual CPU’s of the million or so. These modules are small groups of neurons if we accept the Network of Networks assumptions.

There are two ways CPUs interact.

In the first, local lateral transmission leads to the formation of traveling waves and interference patterns. There is some evidence for such mechanisms, for example, medial axis representations and effects like those found by Kovacs and Julesz (1994)

In the second, there is a traditional projection system, where potentially any CPU in the target region can receive input from any CPU in the source region. In cortex, these projection systems are nearly always bi-directional, that is, there is both massive upward and downward neural projection.

3.4. Expanded Nodes:

Computational Assumptions: We believe computation in a brain-like computer will not be based on logic but on association, summation, and discretization.

• Association is the key computational operation. There is no basic mechanism performing traditional logic.
• Computational dynamics include a linear and a nonlinear stage. We claim the system needs and uses both. The transition from considering many answers in the linear stage to accepting only one in the non-linear stage is a key step in the computation: weighing evidence and deciding. Non-linear attractor dynamics discretize an underlying continuous system.
• Formation of a stable assemblage corresponds to the result of computation. Strong stable assemblages -- we might call them concepts – can have linguistic tags, that is, words. Complex assemblages can formed from co-occurrence or sum of simple ones and can give rise to their own associations.

Assemblages act much like Hebbian cell assemblies (Hebb, 1949). It has proven difficult to simulate useful and stable Hebbian cell assemblies based on individual neurons and the idea has not been popular recently. However, we conjecture using groups of neurons as in NofN will allow construction of stable assemblages through associative learning. We now have a better idea of the properties of gain control circuitry in cortex, small networks, and the virtues of sparse coding than did Hebb. Some optical imaging evidence on the way that inferotemporal cortex codes complex objects is consistent with our assumptions about small networks and assemblage formation. (See Tsunoda, et al., 2001; Tanaka, 2003).

Context is a key cognitive computational mechanism. Context arises from both spatial (other brain regions) and temporal (past activity in the same and other regions). Every assemblage has a penumbra of low-level associations that we feel perform most of the work of contextual disambiguation and can provide associative flexibility when combined with “analog” control structures that model cognitive mechanisms such as attention.