CS 416 Artificial Intelligence Lecture 20 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 Numerous rat studies that monitor individual neurons demonstrate the existence of place cells Generally hippocampus is associated with intermediate term memory ITM 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 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 Minimal Model CA3 recurrent activity Typical Equations y j t w c z t 1 z t 1 K z t 1 K ij ij i w c i ij ij i i 1 x j t 0 R i i 0 K I xi t i y t x t 1 j j otherwise wij t wij t 1 z j t 1 zi t 1 wij t 1 Definitions yj net excitation of j xj external input to j zj output state of j threshold to fire KI feedforward inhibition KR feedback inhibition K0 resting conductance cij 1 z j t z j t 1 z j t 1 z j t 0 connectivity from i to j wij weight between i and j rate constant of synaptic modification spike decay rate t time 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 Model Variables Functional 1 Average activity 2 Activity fluctuations 3 Sequence length memory capacity 4 Average lifetime of local context neurons 5 Speed of learning 6 Ratio of external to recurrent excitations Actual 1 Number of neurons 2 Percent connectivity 3 Time span of synaptic associations 4 Threshold to fire 5 Feedback inhibition weight constant 6 Feedforward inhibition weight constant 7 Resting conductance 8 Rate constant of synaptic modification 9 Input code Eleven Problems 1 2 3 4 5 6 7 8 9 10 11 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 Sequence Completion Train on sequence ABCDEFG Provide input A Network recalls BCDEFG Rebroadcast Train network on one or more sequences Provide random input patterns All or part of one of the trained sequences is recalled 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 ITMhippocampus 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 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 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 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 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 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 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 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 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