Computational Neuroscience


Computational neuroscience is characterised by its focus on understanding the nervous system as a computational device rather than by a particular experimental technique (Eeckman & Bower, 1993).


Half a dozen neurophysiological facts often ignored by ANN models

1. Asymmetric Connectivity
Connections between neurons are uni-directional and symmetric influences between pairs of neurons must be exceedingly rare.

2. Partial Connectivity
Groups of neurons are not multiply connected with each neuron connected to every other neuron in the group.

3. Homogeneity of Neuron Efferents
The efferents from a given neuron are either all excitatory or all inhibitory. A single neuron cannot excite some neurons and inhibit others (directly).

4. Immutability of Neuron Type
Neuron types do not change. Excitatory neurons (and the synapses they make on other neurons) cannot become inhibitory or vice versa.

5. Long-Term Potentiation
Long lasting increases in synaptic efficacy have only been found at excitatory synapses.

6. Long-Term Depression
Long lasting decreases in synaptic efficacy do not normally occur at single synapses. In general LTD has only been clearly shown to occur at groups of synapses (the Purkinje cells in the cerebellum are an exception). LTD occurs on the inactive or weakly active afferents to a neuron which is highly depolarised. LTD has also been recorded on the strongly active afferents to hyper-polarised neurons.


Nitric Oxide (NO) supports the volume learning implicit in most ANN models

Until 1990 increases in synaptic efficacy could only be explained in a Hebbian manner (Ie. LTP occurring on the active afferents to a neuron which was highly depolarised). With the discovery that NO could act as a post-synaptic messenger to all of the active pre-synaptic terminals in a local volume the Hebbian explanation could finally be augmented to cover LTP on active afferents to nearby hyper-polarised neurons (Ie. NO could boot-strap nearby neurons into firing).


Some Neurophysiological and Neuroscience References

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