Working models of working memory

Transcription

1 Working models of working memory Omri Barak and Misha Tsodyks 2014, Curr. Op. in Neurobiology Referenced by Kristjan-Julius Laak Sept 16th 2015 Tartu

2 Working models of working memory

3 Working models of working memory Def. Holding and manipulating information for short periods of time with no structural changes involved. refers more to the whole theoretical framework of structures and processes used for the temporary storage and manipulation of information WM

4 The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information George A. Miller, 1956

5 For a sequence of words in a sentence you get the structural idea in the end. example of a WM task

6 Levels of computation

7 Challenges Data-driven Analysis of behaviour Manipulate several items simultaneously Neurophysiological observations Irregular firing patterns Activity is not stationary Different neurons have different firing profiles

8 Challenges Data-driven Analysis of behaviour Manipulate several items simultaneously Computational-driven Network activity should be stable to retain memories Neurophysiological observations Irregular firing patterns Activity is not stationary Different neurons have different firing profiles

9 Classical idea of WM (still working memory) Itskov, Hansel, Tsodyks, Front Comput Neurosci. 2011

10 Classical idea of WM (still working memory) WM stationary persistent activation of selective neuronal populations

11 Classical idea of WM (still working memory) WM stationary persistent activation of selective neuronal populations Recent advantages explain WM also by 1. Short-term synaptic plasticity (STSP) 2. Recurrent excitatory and inhibitory networks (I-E) 3. Intrinsic network dynamics

12 Delay-effect

13 How can we hold many items simultaneously? or How to overcome the mechanistic challenge of retaining several items in WM?

14 How can we hold many items simultaneously? or How to overcome the mechanistic challenge of the interference between the activation of different items? Alternatively: Capacity of the network

15 Amit et al. 2003, Cerebral Cortex Overcoming interference: Sparse patterns Every item is represented by a small fraction of neuronal population Experiment I-F spiking neuron model

16 Amit et al. 2003, Cerebral Cortex Overcoming interference: I-E balance The balance of inhibition and exhibition determines a) No. of items network can hold b) Mode of failure (fade out, merge)

17 Dorsolateral prefrontal cortex (dlpfc)

18 Boosting of capacity through dlpfc topdown signals. dlpfc has nonspecific, excitatory connections to IPS. (A) If dlpfc has low activity, only two items are stored. (B) When dlpfc activity is high, all four items are remembered. Edin et al. 2008, PNAS

19 Rolls et al., 2013,PLOS One 1. Synaptic facilitation Short-term SF temporarily modify synaptic efficacy in response to stimuli Phenomenological model of Ca-mediated transmissioon ->

20 SF continued 1... Prolongs memory lifetime by reducing the inherent drift of the system 2... Replaces persistent activity!

21 WM sustained by Ca+ facilitation Mongillo et al., 2008, Science

22 1200 species of proteins in post syn end, and only 6 Ca+ ions

23 SF continued 1... Prolongs memory lifetime by reducing the inherent drift of the system 2... Replaces persistent activity! 3... Enables non-linear relationship between pre and post syn neuron

24 Excitatory recurrent currents (~NMDA) make persistent activity models more realistic Wang et al Neuron

25 2. I-E Balance We know that there is a balance and the activity is irregular This has been a puzzle for neuroscientists.

26 Memories are stabilized by fast inhibition and slow excitation Negative feedback loop idea from engineering

27 Rainer, Miller, 2002, EJN See also: Maass, et al. 2002, Neural Comput 3. Intrinsic dynamic mechanism There s no persistent activity -> Idea of stable states during WM tasks

28 Reservoir computing w Liquid State Machines

29 Barak, et al., 2013, Science Direct But maybe some states? Training initally random network gives better results

30 Dynamic attractors chaotic network + learning Laje, Buonomano, 2013, Nature Neuroscience

31 Conclusions Biological systems don't choose one mechanism. It is highly possible that many mechanisms mentioned are utilized by the brain.

32 Thank you! Kristjan-Julius Laak Computational Neuroscience lab (neuro.cs.ut.ee) University of Tartu