Chien, V.

Knösche, T.

Maess, B.

English

MPG.PuRe

•  The elicitation of mismatch negativity (MMN) relies on two steps: (1) regularity formation of repeated stimuli, and (2) detection of deviant stimulus. However, how the two steps are realized in neural circuits is still unclear.  •  In this study, we examined in how far regularity formation and deviant detection are attributable to the plasticity in excitatory-to-inhibitory connections (EI plasticity), in a neural network during perception of sequences.  Network model   Nodes •  Each node is represented by a Wilson-Cowan model [1] that consists of an excitatory (E) and an inhibitory (I) population, as shown in Fig. 1a.  •  The intra-node connections are set to be fixed: Wee=0.8, Wei=0.6, Wie=-0.25, Wii=-0.05.  Inter-node connections •  The network is constructed by 4 nodes that are fully connected. As shown in Fig. 1b, the inter-node connections include: WEE(i,j), WEI(i,j), WIE(i,j), and WII(i,j), where i and j represent different nodes. In this study, we focused on the impact of WEI and set the other inter-node connections to zero. Inputs •  Background noise (Inputnoise) is a constant positive value, which is fed to the E population of each node. •  Thalamic input (Inputth) is fed to the E population (weight=1) and the I population (weight=0.5) of each node.  Excitatory-to-inhibitory (EI) plasticity •  As in Fig. 2a,b, the inter-node connections WEI adjust according to the differential anti-Hebbian learning rule [2], accounting for the pre-to-post time delay from E to I activities. The rule was based on the spike-time dependent plasticity (STDP) for excitatory synapses onto inhibitory neurons (Fig. 2c), where a pre-to-post pairing induces long-term depression (LTD) and a post-to-pre pairing causes long-term potentiation (LTP) [3].    Excitatory-to-inhibitory Plasticity for Regularity Formation and Deviant Detection  Vincent S.C. Chien, Burkhard Maess, Thomas R. Knösche Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany vchien@cbs.mpg.de Computational Neuroscience, Antwerp    July 2017 Simulation 1 (regularity formation)  •  EI plasticity is turned on throughout the simulation.  •  Observe network response during/between repeated sequence A (1à2à3à4) and B (4à3à2à1). •  Observe WEI changes during regularity formation.   Regularity formation •  The network, initialized with fully connected WEI, showed no sequential activities under background noise (t=0-1s in Fig. 3a,b).  •  The regular pattern of activities was formed after a couple of repeated sequence A (1à2à3à4). The WEI adjusted, effectively forming a stable heteroclinic channel. The WEI adjusted again for repeated sequence B (4à3à2à1). Deviant detection •  In Fig. 4a, due to the adjusted WEI, the activations in node 2 and 3 dropped at the deviant sequence (1à3à2à4). WEI recovered soon (Fig. 4b). •  Fig. 4c shows that the adjusted WEI could lead to either smaller (e.g. 3Hz sequence) or larger (e.g. 2Hz sequence) activations in nodes. In both cases, the plastic WEI resulted in a stronger deviant response than the fixed WEI.  •  Excitatory-to-inhibitory  plasticity, reflecting the differential anti-Hebbian learning rule, allows regularity formation. After a regular pattern is formed, the excitatory-to-inhibitory connections (i.e. the adjusted WEI) support the deviant detection. •  After regularity formation, the background noise (inputnoise) allows (1) the generation of a deviant response following a deviant stimulus, and (2) the recall of a memorized sequence when the stimuli (inputth) is turned off.  •  In the network, recalled patterns (facilitated through inputnoise) are competing with current input patterns (inputth). The formation of a novel regular pattern is thus hampered by strong background noise.  •  This study suggests that the two fundamental mechanisms (i.e. regularity formation and deviant detection) underlying mismatch negativity (MMN) may rely on the excitatory-to-inhibitory  plasticity during the perception of sequences. Regularity formation. (a) repeated sequence A: 1à2à3à4 and B: 4à3à2à1 were fed to the network. The network established the regularity and persisted when there was only background noise. (b) Changes of WEI strength across time, during sequence A (green), sequence B(red), and only background noise (black).  1 Deviant detection. (a) A deviant sequence 1à3à2à4 appeared at time=5 sec. The activation of node 2 and 3 dropped immediately. (b) Changes of WEI strength across time, during standard sequences (green), deviant sequence (red), and background noise (black). (c) and (d) Summed network activities (grey area) in deviant condition when the sequences repeated in 3Hz and 2Hz. Difference between standard and devaint condition (red lines). The time of deviant sequence (vertical lines).   (1) (2) (4) Introduction Methods Results Discussion References 4 3 Repeated sequence B (Inputth) Repeated sequence A (Inputth) (Inputnoise) Repeated standard sequences  Deviant  Simulation 2 (deviant detection)  •  Observe network response to deviant sequence. •  Check whether deviant detection relies on EI plasticity.    [1] Wilson HR, Cowan JD: Excitatory and inhibitory interactions in localized populations of model neurons. Biophys J 1972, 12(1):1-24. [2] Xie X, Seung HS: Spike-based learning rules and stabilization of persistent neural activity. Advances in neural information processing systems 2000: 199-208 . [3] Bell CC, Han VZ, Sugawara Y, Grant K: Synaptic plasticity in a cerebellum-like structure depends on temporal order. Nature 1997, 387(6630):278-281.    Input to nodes1 2 3 4 5 6 71234Node index0 1 2 3 4 5 6 7Time (sec)012345Firing rateResponse of nodes1234(b) Input to nodes1 2 3 4 5 6 71234Node index0 1 2 3 4 5 6 7Time (sec)012345Firing rateResponse of nodes1234(a) (a) (b) (c) (d)       Dev-Std (a) (b) Network model. (a) The intra-node connections of a node. (b) The inputs and inter-node connections.  excitatory inhibitory 1 EI plasticity. (a) The WEI follows LTP or LTD. (b) The order of activities of E populations (1 and 3) and I populations (2 and 4). (c) The STDP temporal window for excitatory synapses onto inhibitory neurons.  -60 STDP window tpost – tpre LTP LTD 2 sequence (a) (b) (c) W Wee Wie Wei Wii Repeated sequence A Repeated sequence B no input seq 1234 seq 1324 recall recall 1 2 3 4 4 3 2 1 4 3 2 1 (Deviant sequence at t=5) 1 2 3 4 inputnoise inputth - + Input to nodes1 2 3 4 5 6 71234Node index0 1 2 3 4 5 6 7Time (sec)012345Firing rateResponse of nodes1234012345Input to nodes1 2 3 4 5 6 71234Node index0 1 2 3 4 5 6 7Time (sec)012345Firing rateResponse of nodes1234012345regularity new regularity Weigh matrix of WEI Weigh matrix of WEI ∆!!" !, ! = ! ℊ !! , !! − !!!!!" !, ! , where !, ! = 1,… ,!.	                                                                                    !: derivative of the activity                                                                                     !: learning rate	ℊ !! , !! = !"#$ !! ⋅ !!!! 	

Excitatory-to-inhibitory plasticity for regularity formation and deviant detection

http://hdl.handle.net/21.11116/0000-0004-C106-B

Excitatory-to-inhibitory plasticity for regularity formation and deviant detection

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