Strong claim: Synaptic plasticity is the only game in town.

1. Biophysics class: section III The synaptic Basis for Learning and Memory Harel Shouval Phone: 713-500-5708 Email: harel.shouval@uth.tmc.edu Course web page: http://nba.uth.tmc.edu/homepage/shouval/Biophysics%20Class/Biophysics.htm Strong claim: Synaptic plasticity is the only game in town. Weak Claim: Synaptic plasticity is a game in town.

2. The cortex has ~109 neurons. Each Neuron has up to 104 synapses

4. Central Hypothesis Changes in synapses underlie the basis of learning, memory and some aspects of development. • What is the connection between these seemingly very different phenomena? • Do we have experimental evidence for this hypothesis A cellular correlate of Learning, memory- receptive field plasticity

5. Classical Conditioning Hebb’s rule Ear A Nose B Tongue “When an axon in cell A is near enough to excite cell B and repeatedly and persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficacy in firing B is increased” D. O. Hebb (1949)

6. Two examples of Machine learning based on synaptic plasticity 1.The Perceptron (Rosenblatt 1962) 2. Associative memory (We will talk about these next week)

7. Synaptic plasticity evoked artificially Examples of Long term potentiation (LTP) and long term depression (LTD). LTP First demonstrated by Bliss and Lomo in 1973. Since then induced in many different ways, usually in slice. LTD, robustly shown by Dudek and Bear in 1992, in Hippocampal slice.

8. Artificially induced synaptic plasticity. Presynaptic rate-based induction Bear et. al. 94

9. Feldman, 2000 Depolarization based induction

10. Markram et. al. 1997 Spike timing dependent plasticity

11. At this level we know much about the cellular and molecular basis of synaptic plasticity. But how do we know that “synaptic plasticity” as observed on the cellular level has any connection to learning and memory? What types of criterions can we use to answer this question?

12. Assessment criterions for the synaptic hypothesis: (From Martin and Morris 2002) 1. DETECTABILITY: If an animal displays memory of some previous experience (or has learnt a new task), a change in synaptic efficacy should be detectable somewhere in its nervous system. 2. MIMICRY: If it were possible to induce the appropriate pattern of synaptic weight changes artificially, the animal should display ‘apparent’ memory for some past experience which did not in practice occur.

13. 3. ANTEROGRADE ALTERATION: Interventions that prevent the induction of synaptic weight changes during a learning experience should impair the animal’s memory of that experience (or prevent the learning). 4. RETROGRADE ALTERATION: Interventions that alter the spatial distribution of synaptic weight changes induced by a prior learning experience (see detectability) should alter the animals memory of that experience (or alter the learning).

14. Detectability Example from Rioult-Pedotti - 1998

15. Example: Inhibitory avoidance • Fast • Depends on Hippocampus Whitlock et. al. 2006

16. Occlusion of LTP in trained hemisphere More LTD in trained hemisphere (Riolt-Pedoti 2000)

17. Mimicry: Generate a false memory, teach a skill by directly altering the synaptic connections. This is the ultimate test, and at this point in time it is science fiction.

18. ANTEROGRADE ALTERATION: Interventions that prevent the induction of synaptic weight changes during a learning experience should impair the animal’s memory of that experience (or prevent the learning). This is the most common approach. It relies on utilizing the known properties of synaptic plasticity as induced artificially.

19. Example: Spatial learning is impaired by block of NMDA receptors (Morris, 1989) platform Morris water maze rat

21. 4. RETROGRADE ALTERATION: Interventions that alter the spatial distribution of synaptic weight changes induced by a prior learning experience should alter the animals memory of that experience (or alter the learning). Lacuna – memory control

22. Receptive field plasticity is a cellular correlate of learning. What is a receptive field? First described – somatosensory receptive fields (Mountcastle) Best known example – visual receptive fields

23. light electrical signals Visual Cortex Receptive fields are: • Binocular • Orientation Selective Visual Pathway Area 17 LGN Receptive fields are: • Monocular • Radially Symmetric Retina

24. Left Right Tuning curves Response (spikes/sec) 0 90 180 270 360 Right Left

25. Tuning curves and receptive fields A feed forward model of orientation selective cells in visual cortex. (Hubel and Wiesel model of simple cell)

26. Receptive field plasticity is a correlate of learning An imaginary example Learning to discriminate between similar lines Before learning After learning

27. Generalization of the meaning of RF and Selectivity • First described in somatosensory cortex (Mountcastle) • Retinal cell RF’s • Simple cell RF in primary Visual cortex (VC) • Complex cell in VC • Motion selective cells in area MT • Selective cells in Auditory areas … • Is there another form of representation?

28. Binocular Deprivation Normal Receptive field plasticity can be induced by changes in the visual environment Adult Response (spikes/sec) Response (spikes/sec) Adult angle angle Eye-opening Eye-opening

29. Monocular Deprivation Normal Left Right Right Left Response (spikes/sec) angle angle 20 30 % of cells 15 10 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Rittenhouse et. al. group group

30. R e c o r d S t i m u l a t e LTD 1 5 0 1 2 5 % of baseline 1 0 0 7 5 1 H z 5 0 - 3 0 - 1 5 0 1 5 3 0 4 5 T i m e f r o m o n s e t o f L F S ( m i n ) LTP 2 0 0 1 5 0 1 0 0 HFS 5 0 - 1 5 0 1 5 3 0 Time (min) Ocular Dominance Plasticity (Mioche and Singer, 89) Synaptic plasticity in Visual Cortex (Kirkwood and Bear, 94 ) Receptive field Plasticity Left Eye Right Eye % of baseline

31. Evidence that Ocular Dominance plasticity depends on synaptic plasticity. Bear et. al. 1990

32. Similar experiment using Antisense for NR1 in Ferrets Roberts et. al. 1998

33. Blocking NMDAR with Antisense prevents the development of orientation selectivity in Ferrets . Ramoa et. al. 2001

34. Heynen et. al. 2003

35. LTD is the basis of Rat Ocular Dominance plasticity Heynen et. al. 2003

37. So what did we learn today? What is the support for the claim that synaptic plasticity is the basis of learning and memory?