Strong claim: Synaptic plasticity is the only game in town. Weak Claim: Synaptic plasticity is a game in town. Biophysics class: section III The synaptic Basis for Learning and Memory Harel Shouval Phone: Course web page:

The cortex has ~10 9 neurons. Each Neuron has up to 10 4 synapses

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

Classical Conditioning Hebb’s rule “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” Ear Tongue Nose A B D. O. Hebb (1949)

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

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

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

Feldman, 2000 Depolarization based induction

Spike timing dependent plasticity Markram et. al. 1997

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? At this level we know much about the cellular and molecular basis of synaptic plasticity.

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.

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).

Detectability Example from Rioult-Pedotti

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

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

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.

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.

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

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

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

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

Response (spikes/sec) Left Right Tuning curves RightLeft

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

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

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?

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

Monocular Deprivation Normal Left Right % of cells group angle Response (spikes/sec) Right Left Rittenhouse et. al.

Receptive field Plasticity Ocular Dominance Plasticity (Mioche and Singer, 89) Synaptic plasticity in Visual Cortex (Kirkwood and Bear, 94 ) % of baseline Left Eye Right Eye Time from onset of LFS (min) Hz % of baseline LTD

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

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

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

Heynen et. al. 2003

LTD is the basis of Rat Ocular Dominance plasticity

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