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Biologically-Inspired Neural Nets

Biologically-Inspired Neural Nets. Modeling the Hippocampus. 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

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Biologically-Inspired Neural Nets

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  1. 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).

  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).

  4. Cornus Ammon • The most significant feature in the hippocampus is the Cornus Ammon (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

  5. Minimal Model 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

  7. FundamentalProperties • 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 • 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. TransversePatterning • 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. TransitiveInference • 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. ImportantRecent Discoveries • Addition of random “starting pattern” improves performance of network • Synaptic failures improve performance (and reduce energy requirements) • Addition of CA1 decoder improves performance

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