1. CS 416 Artificial Intelligence
Lecture 20
Biologically-Inspired Neural Nets
Modeling the Hippocampus

2. Hippocampus 101 In 1957, Scoville and Milner reported on patient HM
Since then, numerous studies have used fMRI and PET scans to demonstrate use of hippocampus during learning and recall
Numerous rat studies that monitor individual neurons demonstrate the existence of place cells
Generally, hippocampus is associated with intermediate term memory (ITM).
HM bilaterally had large regions of his hippocampus and other temporal lobe structures removed for treatment of epilepsy. Consequently suffered from retrograde and anterograde amnesia. Retrograde amnesia was graded with respect to time, with no noticeable loss for events happening more than 10 years ago. (IQ actually went up a couple of points, but within margin of error for test.)

3. Hippocampus 101
In 1994, Wilson and McNaughton demonstrated that sharp wave bursts (SPW) during sleep are time-compressed sequences learned earlier
Levy hypothesizes that the hippocampus teaches learned sequences to the neocortex as part of a biased random processes
Levy also hypothesizes that erasure/bias demotion happens when the neocortex signals to the hippocampus that the sequence was acquired, probably during slow-wave sleep (SWS).
Busaki achieved similar results in 1996
Levy hypothesis relate to PTSD

4. Cornu Ammonis
The most significant feature in the hippocampus is the Cornu Ammonis (CA)
Most work in the Levy Lab focuses specifically on the CA3 region, although recently we’ve started re-examining the CA1 region as well
Ammon, “the hidden one”, is the supreme divinity in the Egyptian pantheon

5. Minimal Model CA3 recurrent activity
The hippocampus is getting highly processed, abstract information
Other important areas of the brain include the visual, auditory, and motor cortices, Broca’s region and Wernicke’s region, and multimodal cortices such as Brodman’s area 46/9 – the prefrontal cortex. The hippocampus is strongly inter-connected to multimodal regions of the brain.
The hippocampus is thought to be a random recoder that is activated more strongly when novelty is encountered whose purpose is to associate things we can’t otherwise associate
High dimensionality is key
CA3 recurrent activity

6. Typical Equations Definitions yj net excitation of j
xj external input to j
zj output state of j
θ threshold to fire
KI feedforward inhibition
KR feedback inhibition
K0 resting conductance
cij connectivity from i to j
wij weight between i and j
ε rate constant of synaptic modification
α spike decay rate
t time
Memory is stored in the weights
Neurons have binary outputs 0/1

7. Fundamental Properties
Neurons are McCulloch-Pitts-type threshold elements
Synapses modify associatively on a local Hebbian-type rule
Most connections are excitatory
Recurrent excitation is sparse, asymmetric, and randomly connected
Inhibitory neurons approximately control net activity
In CA3, recurrent excitation contributes more to activity than external excitation
Activity is low, but not too low

8. Model Variables Functional Average activity Activity fluctuations
Sequence length memory capacity
Average lifetime of local context neurons
Speed of learning
Ratio of external to recurrent excitations
Actual
Number of neurons
Percent connectivity
Time span of synaptic associations
Threshold to fire
Feedback inhibition weight constant
Feedforward inhibition weight constant
Resting conductance
Rate constant of synaptic modification
Input code

9. Eleven Problems Simple sequence completion Spontaneous rebroadcast
One-trial learning
Jump-ahead recall
Sequence disambiguation (context past)
Finding a shortcut
Goal finding (context future)
Combining appropriate subsequences
Transverse patterning
Transitive inference
Trace conditioning

10. Sequence Completion Train on sequence ABCDEFG Provide input A
Network recalls BCDEFG

11. Rebroadcast Train network on one or more sequences
Provide random input patterns
All or part of one of the trained sequences is recalled

12. One-trial learning Requires high synaptic modification rate
Does not use same parameters as other problems
Models short-term memory (STM) instead of intermediate-term memory (ITM-hippocampus)

13. Jump-ahead recall
With adjusted inhibition, sequence completion can be short-circuited
Train network on ABCDEFG
Provide A
Network recalls G or possibly BDG, etc.
Inhibition in hippocampus does vary

14. Disambiguation Train network on patterns ABC456GHI and abc456ghi
Present pattern A to the network
Network recalls BC456GHI
Requires patterns 4, 5, and 6 to be coded differently depending on past context

15. Shortcuts Train network on pattern ABC456GHIJKL456PQR
Present pattern A to the network
Network recalls BC456PQR
Uses common neurons of patterns 4, 5, and 6 to generate a shortcut

16. Goal Finding Train network on pattern ABC456GHIJKL456PQR
Present pattern A and part of pattern K to the network
Network recalls BC456GHIJK…
Requires use of context future

17. Combinations Train network on patterns ABC456GHI and abc456ghi
Present pattern A and part of pattern i to the network
Network recalls BC456ghi
Also requires use of context future

18. Transverse Patterning
Similar to rock, paper, scissors
Train network on sequences [AB]a+, [AB]b-, [BC]b+, [BC]c-, [AC]c+, [AC]a-
Present [AB] and part of + to network and network will generate a
Present [BC] and part of + to network and network will generate b
Present [AC] and part of + to network and network will generate c

19. Transitive Inference Transitivity: if A>B and B>C, then A>C
Train network on [AB]a+, [AB]b-, [BC]b+, [BC]c-, [CD]c+, [CD]d-, [DE]d+, [DE]e-
Present [BD] and part of + to network, and it will generate b

20. Trace Conditioning Train network on sequence A……B
Vary the amount of time between presentation of pattern A and pattern B
Computational results match experimental results on trace conditioning in rabbits

21. Important Recent Discoveries
Addition of random “starting pattern” improves performance of network
Synaptic failures improve performance (and reduce energy requirements)
Addition of CA1 decoder improves performance