Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Identifiering och utvärdering av potentialer inom Green Cargo ABs intermodala konfektionslogistik

This Master Thesis has been carried out at Green Cargo AB in Stockholm, and was initiated by Green Cargo in order to gain knowledge about potentials in the existing logistics system for ready made clothing. To be able to identify and analyse potentials within the ready made clothing logistic of Green Cargo, a mapping of the logistic network was accomplished. The mapping quantifies volumes and describes Green Cargo AB’s logistic system for ready made clothing. From these data Key Performance Indicators were calculated. With information given from the mapping, a couple of flows with such prerequisites that would allow improvement were selected. These flows were further analysed to identify and evaluate the conditions of the potentials “Consolidation of Goods” and “Return Flows”. The flows that have been analysed evaluated are: Gothenburg – Umea, Gothenburg – Sundsvall, Gothenburg – Gavle and Gothenburg – Stockholm. The analyse resulted in the following statements: Consolidation of Goods − There is enough storage capacity in Logistikcentra Väst if the fill rates are raised to 85 % in all of the analysed flows − Largest potential is found in the flow between Gothenburg and Gavle with following key figures o Average fill rate: 30 % o Free capacity: 3 200 pallets yearly o Available time after ordinary distribution : 5-7 hours o Cubic price in train transport: 48 % of total costs Return Flows − The return flow between Stockholm and Gothenburg offers the largest potential with a free capacity of 8 110 pallets, corresponding to 20 614 m 3, yearly − Longest available time after ordinary distribution is found in Stockholm where it averages from 7,5 to 9,8 hours − The highest delivery frequency is also found in Stockholm where it averages between three and four days a week At the end of the thesis an analyse of the possibility to transport the goods of Logistikcentra Ost, Stockholm, to Gothenburg, using the free capacity in the return flow with same stretch. In the appendix all data that has been processed during the project is attached.Detta examensarbete har utförts på Green Cargo AB och inleddes för att man inom Green Cargo ville få vetskap om vilka potentialer som finns i det existerande logistiksystemet för konfektion. För att identifiera och analysera potentialer inom Green Cargos konfektionslogistik genomfördes först en kartläggning av alla de konfektionsflöden företaget hanterar. Kartläggningen redogör för vilka volymer som transporteras genom Sverige för respektive kunds räkning. Utifrån dessa data har också fyllnadsgrader beräknats. Utifrån kartläggningen genomfördes ett urval av ett antal flöden som har goda möjligheter att påverkas. Dessa utvalda flöden analyserades vidare för att identifiera förutsättningarna i potentialerna ”samlastning” och ”returer”. De flöden som analyserats är: Göteborg-Umeå, Göteborg-Sundsvall, Göteborg-Gävle samt Göteborg-Stockholm. Analysen av respektive potential resulterade i följande fakta: Samlastning − Tillräcklig lagerkapacitet i Logistikcentra Väst finns för att höja fyllnadsgraden till 85 % i samtliga undersökta flöden. − Störst potential återfinns i flödet Göteborg-Gävle, med följande nyckeltal: o Fyllnadsgrad: 30 % o Fri kapacitet: 3 200 pallplatser årligen o Ledig tid efter ordinarie distribution: 5-7 timmar o Kubikmeterpris i dragning: 48 % av totalkostnaden Returer − Returflödet mellan Stockholm – Göteborg erbjuder störst fri kapacitet med 8 110 pallplatser motsvarande 20 614 m 3 årligen − Mest tillgänglig tid efter ordinarie distribution återfinns i Stockholm med 7,5 till 9,8 timmar − Ovan nämnda flöde har även den högsta leveransfrekvensen med tre till fyra ordinarie leveranser per vecka Som avslutande stycke följer en analys över möjligheten att lasta Logistikcentra Osts varor i returflödet från Stockholm till Göteborg. I bilagorna återfinns alla data som tagits fram under projektet

Identifiering och utvärdering av potentialer inom Green Cargo ABs intermodala konfektionslogistik

This Master Thesis has been carried out at Green Cargo AB in Stockholm, and was initiated by Green Cargo in order to gain knowledge about potentials in the existing logistics system for ready made clothing. To be able to identify and analyse potentials within the ready made clothing logistic of Green Cargo, a mapping of the logistic network was accomplished. The mapping quantifies volumes and describes Green Cargo AB’s logistic system for ready made clothing. From these data Key Performance Indicators were calculated. With information given from the mapping, a couple of flows with such prerequisites that would allow improvement were selected. These flows were further analysed to identify and evaluate the conditions of the potentials “Consolidation of Goods” and “Return Flows”. The flows that have been analysed evaluated are: Gothenburg – Umea, Gothenburg – Sundsvall, Gothenburg – Gavle and Gothenburg – Stockholm. The analyse resulted in the following statements: Consolidation of Goods − There is enough storage capacity in Logistikcentra Väst if the fill rates are raised to 85 % in all of the analysed flows − Largest potential is found in the flow between Gothenburg and Gavle with following key figures o Average fill rate: 30 % o Free capacity: 3 200 pallets yearly o Available time after ordinary distribution : 5-7 hours o Cubic price in train transport: 48 % of total costs Return Flows − The return flow between Stockholm and Gothenburg offers the largest potential with a free capacity of 8 110 pallets, corresponding to 20 614 m 3, yearly − Longest available time after ordinary distribution is found in Stockholm where it averages from 7,5 to 9,8 hours − The highest delivery frequency is also found in Stockholm where it averages between three and four days a week At the end of the thesis an analyse of the possibility to transport the goods of Logistikcentra Ost, Stockholm, to Gothenburg, using the free capacity in the return flow with same stretch. In the appendix all data that has been processed during the project is attached.Detta examensarbete har utförts på Green Cargo AB och inleddes för att man inom Green Cargo ville få vetskap om vilka potentialer som finns i det existerande logistiksystemet för konfektion. För att identifiera och analysera potentialer inom Green Cargos konfektionslogistik genomfördes först en kartläggning av alla de konfektionsflöden företaget hanterar. Kartläggningen redogör för vilka volymer som transporteras genom Sverige för respektive kunds räkning. Utifrån dessa data har också fyllnadsgrader beräknats. Utifrån kartläggningen genomfördes ett urval av ett antal flöden som har goda möjligheter att påverkas. Dessa utvalda flöden analyserades vidare för att identifiera förutsättningarna i potentialerna ”samlastning” och ”returer”. De flöden som analyserats är: Göteborg-Umeå, Göteborg-Sundsvall, Göteborg-Gävle samt Göteborg-Stockholm. Analysen av respektive potential resulterade i följande fakta: Samlastning − Tillräcklig lagerkapacitet i Logistikcentra Väst finns för att höja fyllnadsgraden till 85 % i samtliga undersökta flöden. − Störst potential återfinns i flödet Göteborg-Gävle, med följande nyckeltal: o Fyllnadsgrad: 30 % o Fri kapacitet: 3 200 pallplatser årligen o Ledig tid efter ordinarie distribution: 5-7 timmar o Kubikmeterpris i dragning: 48 % av totalkostnaden Returer − Returflödet mellan Stockholm – Göteborg erbjuder störst fri kapacitet med 8 110 pallplatser motsvarande 20 614 m 3 årligen − Mest tillgänglig tid efter ordinarie distribution återfinns i Stockholm med 7,5 till 9,8 timmar − Ovan nämnda flöde har även den högsta leveransfrekvensen med tre till fyra ordinarie leveranser per vecka Som avslutande stycke följer en analys över möjligheten att lasta Logistikcentra Osts varor i returflödet från Stockholm till Göteborg. I bilagorna återfinns alla data som tagits fram under projektet

Identifiering och utvärdering av potentialer inom Green Cargo ABs intermodala konfektionslogistik

This Master Thesis has been carried out at Green Cargo AB in Stockholm, and was initiated by Green Cargo in order to gain knowledge about potentials in the existing logistics system for ready made clothing. To be able to identify and analyse potentials within the ready made clothing logistic of Green Cargo, a mapping of the logistic network was accomplished. The mapping quantifies volumes and describes Green Cargo AB’s logistic system for ready made clothing. From these data Key Performance Indicators were calculated. With information given from the mapping, a couple of flows with such prerequisites that would allow improvement were selected. These flows were further analysed to identify and evaluate the conditions of the potentials “Consolidation of Goods” and “Return Flows”. The flows that have been analysed evaluated are: Gothenburg – Umea, Gothenburg – Sundsvall, Gothenburg – Gavle and Gothenburg – Stockholm. The analyse resulted in the following statements: Consolidation of Goods − There is enough storage capacity in Logistikcentra Väst if the fill rates are raised to 85 % in all of the analysed flows − Largest potential is found in the flow between Gothenburg and Gavle with following key figures o Average fill rate: 30 % o Free capacity: 3 200 pallets yearly o Available time after ordinary distribution : 5-7 hours o Cubic price in train transport: 48 % of total costs Return Flows − The return flow between Stockholm and Gothenburg offers the largest potential with a free capacity of 8 110 pallets, corresponding to 20 614 m 3, yearly − Longest available time after ordinary distribution is found in Stockholm where it averages from 7,5 to 9,8 hours − The highest delivery frequency is also found in Stockholm where it averages between three and four days a week At the end of the thesis an analyse of the possibility to transport the goods of Logistikcentra Ost, Stockholm, to Gothenburg, using the free capacity in the return flow with same stretch. In the appendix all data that has been processed during the project is attached.Detta examensarbete har utförts på Green Cargo AB och inleddes för att man inom Green Cargo ville få vetskap om vilka potentialer som finns i det existerande logistiksystemet för konfektion. För att identifiera och analysera potentialer inom Green Cargos konfektionslogistik genomfördes först en kartläggning av alla de konfektionsflöden företaget hanterar. Kartläggningen redogör för vilka volymer som transporteras genom Sverige för respektive kunds räkning. Utifrån dessa data har också fyllnadsgrader beräknats. Utifrån kartläggningen genomfördes ett urval av ett antal flöden som har goda möjligheter att påverkas. Dessa utvalda flöden analyserades vidare för att identifiera förutsättningarna i potentialerna ”samlastning” och ”returer”. De flöden som analyserats är: Göteborg-Umeå, Göteborg-Sundsvall, Göteborg-Gävle samt Göteborg-Stockholm. Analysen av respektive potential resulterade i följande fakta: Samlastning − Tillräcklig lagerkapacitet i Logistikcentra Väst finns för att höja fyllnadsgraden till 85 % i samtliga undersökta flöden. − Störst potential återfinns i flödet Göteborg-Gävle, med följande nyckeltal: o Fyllnadsgrad: 30 % o Fri kapacitet: 3 200 pallplatser årligen o Ledig tid efter ordinarie distribution: 5-7 timmar o Kubikmeterpris i dragning: 48 % av totalkostnaden Returer − Returflödet mellan Stockholm – Göteborg erbjuder störst fri kapacitet med 8 110 pallplatser motsvarande 20 614 m 3 årligen − Mest tillgänglig tid efter ordinarie distribution återfinns i Stockholm med 7,5 till 9,8 timmar − Ovan nämnda flöde har även den högsta leveransfrekvensen med tre till fyra ordinarie leveranser per vecka Som avslutande stycke följer en analys över möjligheten att lasta Logistikcentra Osts varor i returflödet från Stockholm till Göteborg. I bilagorna återfinns alla data som tagits fram under projektet

Probabilistic computation underlying sequence learning in a spiking attractor memory network

QC 20170421</p

Data from: Computing the local field potential (LFP) from integrate-and-fire network models

Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best “LFP proxy”, we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with “ground-truth” LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo

The temporal structure of neural recall dynamics reflects the temporal interval used during training.

<p><b>(A)</b> Average after training as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004954#pcbi.1004954.g004" target="_blank">Fig 4C</a> (reproduced here by the 0 ms line) except now depicting terminal weight profiles for many differently trained networks with IPIs varying between 0 and 2000 ms. <b>(B)</b> CRP curves calculated for networks with representative IPIs = 0, 500, 1000, 1500 and 2000 ms after 1 minute of recall, with colors corresponding to (A). Increasing IPIs flattened the CRP curve, promoting attractor transition distribution evenness. Error bars reflect standard deviations. <b>(C)</b> Average strength of taken across entire networks after training for different IPIs, where the number of NMDA synapses in these separate networks was constant. <b>(D)</b> Average dwell times <i>μ</i>_<i>dwell</i> measured during 1 minute recall periods for entire networks trained with different IPIs. Shaded areas denote standard deviations here and in (E). <b>(E)</b> Average neural firing rates for attractors with dwell times corresponding to those measured in (D).</p